From 9d6f39bd09566548e285c10d62bde0b39cd8902a Mon Sep 17 00:00:00 2001
From: Jared Wilber <jdwlbr@gmail.com>
Date: Fri, 21 Jul 2023 15:31:49 -0700
Subject: [PATCH] linting first pass

---
 .gitignore                                    |   2 +-
 .pre-commit-config.yaml                       |  22 +
 docs/_build/html/_modules/index.html          |   2 +-
 .../html/_modules/peft/tuners/lora.html       |   2 +-
 .../html/_modules/plotano/application.html    |   2 +-
 .../html/_modules/plotano/component/base.html |   2 +-
 .../component/chatbot_database_factory.html   |   2 +-
 .../_modules/plotano/component/constants.html |   2 +-
 .../html/_modules/plotano/db/qa_database.html |   2 +-
 .../_modules/plotano/db/ranking_database.html |   2 +-
 .../html/_modules/plotano/llm/abs_llm.html    |   2 +-
 .../html/_modules/plotano/llm/constants.html  |   2 +-
 .../_modules/plotano/llm/huggingface.html     |   2 +-
 .../_modules/plotano/llm/model_factory.html   |   2 +-
 .../html/_modules/plotano/llm/openai.html     |   2 +-
 .../html/_modules/plotano/rlhf/rlhf.html      |   2 +-
 docs/_build/html/_modules/plotano/state.html  |   2 +-
 docs/_build/html/_static/basic.css            |   2 +-
 docs/_build/html/_static/css/badge_only.css   |   2 +-
 docs/_build/html/_static/css/theme.css        |   2 +-
 .../html/_static/documentation_options.js     |   2 +-
 docs/_build/html/_static/js/badge_only.js     |   2 +-
 .../_static/js/html5shiv-printshiv.min.js     |   2 +-
 docs/_build/html/_static/js/html5shiv.min.js  |   2 +-
 docs/_build/html/_static/js/theme.js          |   2 +-
 docs/_build/html/_static/language_data.js     |   1 -
 docs/_build/html/_static/pygments.css         |   2 +-
 docs/_build/html/genindex.html                |   2 +-
 docs/_build/html/index.html                   |   2 +-
 docs/_build/html/modules.html                 |   2 +-
 docs/_build/html/plotano.component.html       |   2 +-
 docs/_build/html/plotano.db.html              |   2 +-
 docs/_build/html/plotano.html                 |   2 +-
 docs/_build/html/plotano.llm.html             |   2 +-
 docs/_build/html/plotano.rlhf.html            |   2 +-
 docs/_build/html/py-modindex.html             |   2 +-
 docs/_build/html/search.html                  |   2 +-
 docs/_build/html/searchindex.js               |   2 +-
 docs/conf.py                                  |  27 +-
 example/chatbot/README.md                     |   2 +-
 example/chatbot/demo.py                       |   4 +-
 plotano/application.py                        |   7 +-
 plotano/component/base.py                     |  25 +-
 plotano/component/chatbot_database_factory.py |   5 +-
 plotano/db/qa_database.py                     |  31 +-
 plotano/db/ranking_database.py                |  30 +-
 plotano/frontend/dist/vite.svg                |   2 +-
 plotano/frontend/public/vite.svg              |   2 +-
 plotano/frontend/src/app.css                  |   2 +-
 plotano/frontend/src/assets/svelte.svg        |   2 +-
 plotano/frontend/src/normalize.css            |   2 +-
 plotano/frontend/src/styles.css               |   2 +-
 plotano/llm/abs_llm.py                        |   4 +-
 plotano/llm/constants.py                      |   1 +
 plotano/llm/huggingface.py                    |  50 +-
 plotano/llm/model_factory.py                  |  19 +-
 plotano/llm/openai.py                         |  17 +-
 plotano/rlhf/rlhf.py                          | 532 ++++++++++--------
 tests/component/test_base.py                  |   5 +-
 tests/db/test_qa_database.py                  |  13 +-
 tests/db/test_ranking_database.py             |  26 +-
 tests/llm/test_abs_llm.py                     |   1 +
 tests/llm/test_constants.py                   |   1 +
 tests/llm/test_huggingface.py                 |  21 +-
 tests/llm/test_model_factory.py               |   9 +-
 tests/llm/test_openai.py                      |  14 +-
 tests/rlhf/test_rlhf.py                       |  12 +-
 tests/test_application.py                     |  37 +-
 tests/test_state.py                           |   3 +
 69 files changed, 586 insertions(+), 417 deletions(-)
 create mode 100644 .pre-commit-config.yaml

diff --git a/.gitignore b/.gitignore
index 5666644..3f46c3b 100644
--- a/.gitignore
+++ b/.gitignore
@@ -176,4 +176,4 @@ rlhf_tests
 test_data
 
 # mac
-.DS_Store
\ No newline at end of file
+.DS_Store
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
new file mode 100644
index 0000000..a7db8d5
--- /dev/null
+++ b/.pre-commit-config.yaml
@@ -0,0 +1,22 @@
+# See https://pre-commit.com for more information
+# See https://pre-commit.com/hooks.html for more hooks
+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.0.1
+    hooks:
+      - id: check-toml
+      - id: check-yaml
+      - id: end-of-file-fixer
+      - id: mixed-line-ending
+  - repo: https://github.com/pylint-dev/pylint
+    rev: v2.17.4 # Use the ref you want to point at
+    hooks:
+      - id: pylint
+  - repo: https://github.com/PyCQA/flake8
+    rev: 3.9.2 # Use the ref you want to point at
+    hooks:
+      - id: flake8
+  - repo: https://github.com/PyCQA/isort
+    rev: 5.12.0
+    hooks:
+      - id: isort
diff --git a/docs/_build/html/_modules/index.html b/docs/_build/html/_modules/index.html
index 9b53255..8e56838 100644
--- a/docs/_build/html/_modules/index.html
+++ b/docs/_build/html/_modules/index.html
@@ -111,4 +111,4 @@ <h1>All modules for which code is available</h1>
   </script> 
 
 </body>
-</html>
\ No newline at end of file
+</html>
diff --git a/docs/_build/html/_modules/peft/tuners/lora.html b/docs/_build/html/_modules/peft/tuners/lora.html
index b387d40..b123c9b 100644
--- a/docs/_build/html/_modules/peft/tuners/lora.html
+++ b/docs/_build/html/_modules/peft/tuners/lora.html
@@ -1244,4 +1244,4 @@ <h1>Source code for peft.tuners.lora</h1><div class="highlight"><pre>
   </script> 
 
 </body>
-</html>
\ No newline at end of file
+</html>
diff --git a/docs/_build/html/_modules/plotano/application.html b/docs/_build/html/_modules/plotano/application.html
index ab7f4d5..ca3f62a 100644
--- a/docs/_build/html/_modules/plotano/application.html
+++ b/docs/_build/html/_modules/plotano/application.html
@@ -308,4 +308,4 @@ <h1>Source code for plotano.application</h1><div class="highlight"><pre>
   </script> 
 
 </body>
-</html>
\ No newline at end of file
+</html>
diff --git a/docs/_build/html/_modules/plotano/component/base.html b/docs/_build/html/_modules/plotano/component/base.html
index 51b80e1..38f133b 100644
--- a/docs/_build/html/_modules/plotano/component/base.html
+++ b/docs/_build/html/_modules/plotano/component/base.html
@@ -228,4 +228,4 @@ <h1>Source code for plotano.component.base</h1><div class="highlight"><pre>
   </script> 
 
 </body>
-</html>
\ No newline at end of file
+</html>
diff --git a/docs/_build/html/_modules/plotano/component/chatbot_database_factory.html b/docs/_build/html/_modules/plotano/component/chatbot_database_factory.html
index 1f58e06..048d027 100644
--- a/docs/_build/html/_modules/plotano/component/chatbot_database_factory.html
+++ b/docs/_build/html/_modules/plotano/component/chatbot_database_factory.html
@@ -131,4 +131,4 @@ <h1>Source code for plotano.component.chatbot_database_factory</h1><div class="h
   </script> 
 
 </body>
-</html>
\ No newline at end of file
+</html>
diff --git a/docs/_build/html/_modules/plotano/component/constants.html b/docs/_build/html/_modules/plotano/component/constants.html
index 64c90d0..4856af1 100644
--- a/docs/_build/html/_modules/plotano/component/constants.html
+++ b/docs/_build/html/_modules/plotano/component/constants.html
@@ -107,4 +107,4 @@ <h1>Source code for plotano.component.constants</h1><div class="highlight"><pre>
   </script> 
 
 </body>
-</html>
\ No newline at end of file
+</html>
diff --git a/docs/_build/html/_modules/plotano/db/qa_database.html b/docs/_build/html/_modules/plotano/db/qa_database.html
index 046cfeb..9e30789 100644
--- a/docs/_build/html/_modules/plotano/db/qa_database.html
+++ b/docs/_build/html/_modules/plotano/db/qa_database.html
@@ -294,4 +294,4 @@ <h1>Source code for plotano.db.qa_database</h1><div class="highlight"><pre>
   </script> 
 
 </body>
-</html>
\ No newline at end of file
+</html>
diff --git a/docs/_build/html/_modules/plotano/db/ranking_database.html b/docs/_build/html/_modules/plotano/db/ranking_database.html
index 7d20bc9..edf38d9 100644
--- a/docs/_build/html/_modules/plotano/db/ranking_database.html
+++ b/docs/_build/html/_modules/plotano/db/ranking_database.html
@@ -258,4 +258,4 @@ <h1>Source code for plotano.db.ranking_database</h1><div class="highlight"><pre>
   </script> 
 
 </body>
-</html>
\ No newline at end of file
+</html>
diff --git a/docs/_build/html/_modules/plotano/llm/abs_llm.html b/docs/_build/html/_modules/plotano/llm/abs_llm.html
index 1cadd62..76847b9 100644
--- a/docs/_build/html/_modules/plotano/llm/abs_llm.html
+++ b/docs/_build/html/_modules/plotano/llm/abs_llm.html
@@ -128,4 +128,4 @@ <h1>Source code for plotano.llm.abs_llm</h1><div class="highlight"><pre>
   </script> 
 
 </body>
-</html>
\ No newline at end of file
+</html>
diff --git a/docs/_build/html/_modules/plotano/llm/constants.html b/docs/_build/html/_modules/plotano/llm/constants.html
index 4f37c98..ae85cac 100644
--- a/docs/_build/html/_modules/plotano/llm/constants.html
+++ b/docs/_build/html/_modules/plotano/llm/constants.html
@@ -112,4 +112,4 @@ <h1>Source code for plotano.llm.constants</h1><div class="highlight"><pre>
   </script> 
 
 </body>
-</html>
\ No newline at end of file
+</html>
diff --git a/docs/_build/html/_modules/plotano/llm/huggingface.html b/docs/_build/html/_modules/plotano/llm/huggingface.html
index ed538ef..f9f2434 100644
--- a/docs/_build/html/_modules/plotano/llm/huggingface.html
+++ b/docs/_build/html/_modules/plotano/llm/huggingface.html
@@ -174,4 +174,4 @@ <h1>Source code for plotano.llm.huggingface</h1><div class="highlight"><pre>
   </script> 
 
 </body>
-</html>
\ No newline at end of file
+</html>
diff --git a/docs/_build/html/_modules/plotano/llm/model_factory.html b/docs/_build/html/_modules/plotano/llm/model_factory.html
index d36716c..92482bf 100644
--- a/docs/_build/html/_modules/plotano/llm/model_factory.html
+++ b/docs/_build/html/_modules/plotano/llm/model_factory.html
@@ -150,4 +150,4 @@ <h1>Source code for plotano.llm.model_factory</h1><div class="highlight"><pre>
   </script> 
 
 </body>
-</html>
\ No newline at end of file
+</html>
diff --git a/docs/_build/html/_modules/plotano/llm/openai.html b/docs/_build/html/_modules/plotano/llm/openai.html
index 824c524..fc8d7ad 100644
--- a/docs/_build/html/_modules/plotano/llm/openai.html
+++ b/docs/_build/html/_modules/plotano/llm/openai.html
@@ -160,4 +160,4 @@ <h1>Source code for plotano.llm.openai</h1><div class="highlight"><pre>
   </script> 
 
 </body>
-</html>
\ No newline at end of file
+</html>
diff --git a/docs/_build/html/_modules/plotano/rlhf/rlhf.html b/docs/_build/html/_modules/plotano/rlhf/rlhf.html
index 317a001..5160fb5 100644
--- a/docs/_build/html/_modules/plotano/rlhf/rlhf.html
+++ b/docs/_build/html/_modules/plotano/rlhf/rlhf.html
@@ -745,4 +745,4 @@ <h1>Source code for plotano.rlhf.rlhf</h1><div class="highlight"><pre>
   </script> 
 
 </body>
-</html>
\ No newline at end of file
+</html>
diff --git a/docs/_build/html/_modules/plotano/state.html b/docs/_build/html/_modules/plotano/state.html
index c7f9a08..836e3b6 100644
--- a/docs/_build/html/_modules/plotano/state.html
+++ b/docs/_build/html/_modules/plotano/state.html
@@ -144,4 +144,4 @@ <h1>Source code for plotano.state</h1><div class="highlight"><pre>
   </script> 
 
 </body>
-</html>
\ No newline at end of file
+</html>
diff --git a/docs/_build/html/_static/basic.css b/docs/_build/html/_static/basic.css
index 7577acb..92f0788 100644
--- a/docs/_build/html/_static/basic.css
+++ b/docs/_build/html/_static/basic.css
@@ -900,4 +900,4 @@ div.math:hover a.headerlink {
     #top-link {
         display: none;
     }
-}
\ No newline at end of file
+}
diff --git a/docs/_build/html/_static/css/badge_only.css b/docs/_build/html/_static/css/badge_only.css
index c718cee..08397ca 100644
--- a/docs/_build/html/_static/css/badge_only.css
+++ b/docs/_build/html/_static/css/badge_only.css
@@ -1 +1 @@
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diff --git a/docs/_build/html/_static/css/theme.css b/docs/_build/html/_static/css/theme.css
index 19a446a..473b8b4 100644
--- a/docs/_build/html/_static/css/theme.css
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index ec8cedd..49f8978 100644
--- a/docs/_build/html/_static/documentation_options.js
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     ENABLE_SEARCH_SHORTCUTS: true,
-};
\ No newline at end of file
+};
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index 526d723..02c3291 100644
--- a/docs/_build/html/_static/js/badge_only.js
+++ b/docs/_build/html/_static/js/badge_only.js
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index 2b43bd0..ee12995 100644
--- a/docs/_build/html/_static/js/html5shiv-printshiv.min.js
+++ b/docs/_build/html/_static/js/html5shiv-printshiv.min.js
@@ -1,4 +1,4 @@
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diff --git a/docs/_build/html/_static/js/html5shiv.min.js b/docs/_build/html/_static/js/html5shiv.min.js
index cd1c674..b4011dd 100644
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index 250f566..68b9e83 100644
--- a/docs/_build/html/_static/language_data.js
+++ b/docs/_build/html/_static/language_data.js
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index 08bec68..4bf0582 100644
--- a/docs/_build/html/_static/pygments.css
+++ b/docs/_build/html/_static/pygments.css
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\ No newline at end of file
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diff --git a/docs/_build/html/genindex.html b/docs/_build/html/genindex.html
index 31aff15..b921d56 100644
--- a/docs/_build/html/genindex.html
+++ b/docs/_build/html/genindex.html
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 </body>
-</html>
\ No newline at end of file
+</html>
diff --git a/docs/_build/html/index.html b/docs/_build/html/index.html
index 98094fe..4d599e7 100644
--- a/docs/_build/html/index.html
+++ b/docs/_build/html/index.html
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 </body>
-</html>
\ No newline at end of file
+</html>
diff --git a/docs/_build/html/modules.html b/docs/_build/html/modules.html
index d0dc2dd..dfd4f09 100644
--- a/docs/_build/html/modules.html
+++ b/docs/_build/html/modules.html
@@ -174,4 +174,4 @@ <h1>plotano<a class="headerlink" href="#plotano" title="Permalink to this headin
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 </body>
-</html>
\ No newline at end of file
+</html>
diff --git a/docs/_build/html/plotano.component.html b/docs/_build/html/plotano.component.html
index abea7c5..fbbf6cf 100644
--- a/docs/_build/html/plotano.component.html
+++ b/docs/_build/html/plotano.component.html
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-</html>
\ No newline at end of file
+</html>
diff --git a/docs/_build/html/plotano.db.html b/docs/_build/html/plotano.db.html
index e321643..158448e 100644
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+++ b/docs/_build/html/plotano.db.html
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\ No newline at end of file
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diff --git a/docs/_build/html/plotano.html b/docs/_build/html/plotano.html
index e8435eb..941ed30 100644
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-</html>
\ No newline at end of file
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diff --git a/docs/_build/html/plotano.llm.html b/docs/_build/html/plotano.llm.html
index 1736084..abc090f 100644
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+++ b/docs/_build/html/plotano.llm.html
@@ -362,4 +362,4 @@ <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this
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-</html>
\ No newline at end of file
+</html>
diff --git a/docs/_build/html/plotano.rlhf.html b/docs/_build/html/plotano.rlhf.html
index b6630a0..c679d90 100644
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+++ b/docs/_build/html/plotano.rlhf.html
@@ -578,4 +578,4 @@ <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this
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-</html>
\ No newline at end of file
+</html>
diff --git a/docs/_build/html/py-modindex.html b/docs/_build/html/py-modindex.html
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index ea1b7e8..a767e6e 100644
--- a/docs/_build/html/search.html
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"module-plotano.component.chatbot_database_factory"], [3, "module-plotano.component.constants"], [4, "module-plotano.db"], [4, "module-plotano.db.qa_database"], [4, "module-plotano.db.ranking_database"], [5, "module-plotano.llm"], [5, "module-plotano.llm.abs_llm"], [5, "module-plotano.llm.constants"], [5, "module-plotano.llm.huggingface"], [5, "module-plotano.llm.model_factory"], [5, "module-plotano.llm.openai"], [6, "module-plotano.rlhf"], [6, "module-plotano.rlhf.rlhf"]], "name (plotano.state.store attribute)": [[2, "plotano.state.Store.name"]], "plotano": [[2, "module-plotano"]], "plotano.application": [[2, "module-plotano.application"]], "plotano.cambio": [[2, "module-plotano.cambio"]], "plotano.state": [[2, "module-plotano.state"]], "run() (plotano.application.application method)": [[2, "plotano.application.Application.run"]], "chatbot (class in plotano.component.base)": [[3, "plotano.component.base.Chatbot"]], "chatbotdatabasefactory (class in plotano.component.chatbot_database_factory)": [[3, "plotano.component.chatbot_database_factory.ChatbotDatabaseFactory"]], "component (class in plotano.component.base)": [[3, "plotano.component.base.Component"]], "dashboard (class in plotano.component.base)": [[3, "plotano.component.base.Dashboard"]], "datasource (class in plotano.component.base)": [[3, "plotano.component.base.DataSource"]], "dropdown (class in plotano.component.base)": [[3, "plotano.component.base.Dropdown"]], "feedbacktype (class in plotano.component.constants)": [[3, "plotano.component.constants.FeedbackType"]], "rank (plotano.component.constants.feedbacktype attribute)": [[3, "plotano.component.constants.FeedbackType.RANK"]], "vote (plotano.component.constants.feedbacktype attribute)": [[3, "plotano.component.constants.FeedbackType.VOTE"]], "create() (plotano.component.chatbot_database_factory.chatbotdatabasefactory static method)": [[3, "plotano.component.chatbot_database_factory.ChatbotDatabaseFactory.create"]], "data_source (plotano.component.base.component attribute)": [[3, "plotano.component.base.Component.data_source"]], "database (plotano.component.base.chatbot attribute)": [[3, "plotano.component.base.Chatbot.database"]], "database (plotano.component.base.dashboard attribute)": [[3, "plotano.component.base.Dashboard.database"]], "fetch_func (plotano.component.base.datasource attribute)": [[3, "plotano.component.base.DataSource.fetch_func"]], "id (plotano.component.base.component attribute)": [[3, "plotano.component.base.Component.id"]], "id (plotano.component.base.datasource attribute)": [[3, "plotano.component.base.DataSource.id"]], "model (plotano.component.base.chatbot attribute)": [[3, "plotano.component.base.Chatbot.model"]], "plotano.component": [[3, "module-plotano.component"]], "plotano.component.base": [[3, "module-plotano.component.base"]], "plotano.component.chatbot_database_factory": [[3, "module-plotano.component.chatbot_database_factory"]], "plotano.component.constants": [[3, "module-plotano.component.constants"]], "props (plotano.component.base.component attribute)": [[3, "plotano.component.base.Component.props"]], "svelte_component (plotano.component.base.component attribute)": [[3, "plotano.component.base.Component.svelte_component"]], "value_column (plotano.component.base.dropdown attribute)": [[3, "plotano.component.base.Dropdown.value_column"]], "questionanswerdatabase (class in plotano.db.qa_database)": [[4, "plotano.db.qa_database.QuestionAnswerDatabase"]], "rankingdatabase (class in plotano.db.ranking_database)": [[4, "plotano.db.ranking_database.RankingDatabase"]], "close_connection() (plotano.db.qa_database.questionanswerdatabase method)": [[4, "plotano.db.qa_database.QuestionAnswerDatabase.close_connection"]], "close_connection() (plotano.db.ranking_database.rankingdatabase method)": [[4, "plotano.db.ranking_database.RankingDatabase.close_connection"]], "create_table() (plotano.db.qa_database.questionanswerdatabase method)": [[4, "plotano.db.qa_database.QuestionAnswerDatabase.create_table"]], "create_table() (plotano.db.ranking_database.rankingdatabase method)": [[4, "plotano.db.ranking_database.RankingDatabase.create_table"]], "get_connection() (plotano.db.qa_database.questionanswerdatabase method)": [[4, "plotano.db.qa_database.QuestionAnswerDatabase.get_connection"]], "get_connection() (plotano.db.ranking_database.rankingdatabase method)": [[4, "plotano.db.ranking_database.RankingDatabase.get_connection"]], "get_cursor() (plotano.db.qa_database.questionanswerdatabase method)": [[4, "plotano.db.qa_database.QuestionAnswerDatabase.get_cursor"]], "get_cursor() (plotano.db.ranking_database.rankingdatabase method)": [[4, "plotano.db.ranking_database.RankingDatabase.get_cursor"]], "insert_question_answer() (plotano.db.qa_database.questionanswerdatabase method)": [[4, "plotano.db.qa_database.QuestionAnswerDatabase.insert_question_answer"]], "insert_ranking() (plotano.db.ranking_database.rankingdatabase method)": [[4, "plotano.db.ranking_database.RankingDatabase.insert_ranking"]], "plotano.db": [[4, "module-plotano.db"]], "plotano.db.qa_database": [[4, "module-plotano.db.qa_database"]], "plotano.db.ranking_database": [[4, "module-plotano.db.ranking_database"]], "print_table() (plotano.db.qa_database.questionanswerdatabase method)": [[4, "plotano.db.qa_database.QuestionAnswerDatabase.print_table"]], "print_table() (plotano.db.ranking_database.rankingdatabase method)": [[4, "plotano.db.ranking_database.RankingDatabase.print_table"]], "retrieve_all_question_answers() (plotano.db.qa_database.questionanswerdatabase method)": [[4, "plotano.db.qa_database.QuestionAnswerDatabase.retrieve_all_question_answers"]], "retrieve_all_question_answers() (plotano.db.ranking_database.rankingdatabase method)": [[4, "plotano.db.ranking_database.RankingDatabase.retrieve_all_question_answers"]], "save_to_csv() (plotano.db.qa_database.questionanswerdatabase method)": [[4, "plotano.db.qa_database.QuestionAnswerDatabase.save_to_csv"]], "save_to_csv() (plotano.db.ranking_database.rankingdatabase method)": [[4, "plotano.db.ranking_database.RankingDatabase.save_to_csv"]], "update_vote_status() (plotano.db.qa_database.questionanswerdatabase method)": [[4, "plotano.db.qa_database.QuestionAnswerDatabase.update_vote_status"]], "absllm (class in plotano.llm.abs_llm)": [[5, "plotano.llm.abs_llm.AbsLlm"]], "huggingface (plotano.llm.constants.llmname attribute)": [[5, "id0"], [5, "plotano.llm.constants.LlmName.HUGGINGFACE"]], "huggingfacemodel (class in plotano.llm.huggingface)": [[5, "plotano.llm.huggingface.HuggingfaceModel"]], "llmname (class in plotano.llm.constants)": [[5, "plotano.llm.constants.LlmName"]], "modelfactory (class in plotano.llm.model_factory)": [[5, "plotano.llm.model_factory.ModelFactory"]], "none (plotano.llm.abs_llm.absllm attribute)": [[5, "plotano.llm.abs_llm.AbsLlm.None"]], "openai (plotano.llm.constants.llmname attribute)": [[5, "id1"], [5, "plotano.llm.constants.LlmName.OPENAI"]], "openaimodel (class in plotano.llm.openai)": [[5, "plotano.llm.openai.OpenAIModel"]], "_engine (plotano.llm.openai.openaimodel attribute)": [[5, "plotano.llm.openai.OpenAIModel._engine"]], "_max_tokens (plotano.llm.openai.openaimodel attribute)": [[5, "plotano.llm.openai.OpenAIModel._max_tokens"]], "_temperature (plotano.llm.openai.openaimodel attribute)": [[5, "plotano.llm.openai.OpenAIModel._temperature"]], "create_model() (plotano.llm.model_factory.modelfactory static method)": [[5, "plotano.llm.model_factory.ModelFactory.create_model"]], "plotano.llm": [[5, "module-plotano.llm"]], "plotano.llm.abs_llm": [[5, "module-plotano.llm.abs_llm"]], "plotano.llm.constants": [[5, "module-plotano.llm.constants"]], "plotano.llm.huggingface": [[5, "module-plotano.llm.huggingface"]], "plotano.llm.model_factory": [[5, "module-plotano.llm.model_factory"]], "plotano.llm.openai": [[5, "module-plotano.llm.openai"]], "predict() (plotano.llm.abs_llm.absllm method)": [[5, "plotano.llm.abs_llm.AbsLlm.predict"]], "predict() (plotano.llm.huggingface.huggingfacemodel method)": [[5, "plotano.llm.huggingface.HuggingfaceModel.predict"]], "predict() (plotano.llm.openai.openaimodel method)": [[5, "plotano.llm.openai.OpenAIModel.predict"]], "rlhfconfig (class in plotano.rlhf.rlhf)": [[6, "plotano.rlhf.rlhf.RLHFConfig"]], "rewarddatacollatorwithpadding (class in plotano.rlhf.rlhf)": [[6, "plotano.rlhf.rlhf.RewardDataCollatorWithPadding"]], "rewardtrainer (class in plotano.rlhf.rlhf)": [[6, "plotano.rlhf.rlhf.RewardTrainer"]], "sft (class in plotano.rlhf.rlhf)": [[6, "plotano.rlhf.rlhf.SFT"]], "adafactor (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.adafactor"]], "adap_kl_ctrl (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.adap_kl_ctrl"]], "answer_title (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.answer_title"]], "base_model_path (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.base_model_path"]], "bf16 (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.bf16"]], "compute_loss() (plotano.rlhf.rlhf.rewardtrainer method)": [[6, "plotano.rlhf.rlhf.RewardTrainer.compute_loss"]], "create_datasets() (plotano.rlhf.rlhf.rewardtrainer method)": [[6, "plotano.rlhf.rlhf.RewardTrainer.create_datasets"]], "create_datasets() (plotano.rlhf.rlhf.sft method)": [[6, "plotano.rlhf.rlhf.SFT.create_datasets"]], "dataset_eval_folder (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.dataset_eval_folder"]], "dataset_name (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.dataset_name"]], "dataset_reward_folder (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.dataset_reward_folder"]], "dataset_subset_rl (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.dataset_subset_rl"]], "dataset_subset_rl_train (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.dataset_subset_rl_train"]], "dataset_subset_sft (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.dataset_subset_sft"]], "dataset_subset_sft_train (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.dataset_subset_sft_train"]], "dataset_type (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.dataset_type"]], "deepspeed (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.deepspeed"]], "device_map (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.device_map"]], "do_sample (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.do_sample"]], "early_stopping (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.early_stopping"]], "eos_token_id (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.eos_token_id"]], "eval_freq (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.eval_freq"]], "evaluation_strategy (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.evaluation_strategy"]], "fp16 (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.fp16"]], "gradient_accumulation_steps (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.gradient_accumulation_steps"]], "gradient_checkpointing (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.gradient_checkpointing"]], "init_kl_coef (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.init_kl_coef"]], "label_names (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.label_names"]], "learning_rate (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.learning_rate"]], "load_in_8bit (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.load_in_8bit"]], "load_lora() (plotano.rlhf.rlhf.sft method)": [[6, "plotano.rlhf.rlhf.SFT.load_lora"]], "local_rank (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.local_rank"]], "log_freq (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.log_freq"]], "logging_steps (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.logging_steps"]], "logging_strategy (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.logging_strategy"]], "lora_config_reward (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.lora_config_reward"]], "lora_config_rl (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.lora_config_rl"]], "lr_scheduler_type_sft (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.lr_scheduler_type_sft"]], "max_seq_length (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.max_seq_length"]], "max_seq_length_reward (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.max_seq_length_reward"]], "max_steps (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.max_steps"]], "mini_batch_size (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.mini_batch_size"]], "num_warmup_steps (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.num_warmup_steps"]], "num_workers (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.num_workers"]], "output_dir (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.output_dir"]], "output_max_length (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.output_max_length"]], "per_device_eval_batch_size (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.per_device_eval_batch_size"]], "per_device_train_batch_size (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.per_device_train_batch_size"]], "plotano.rlhf": [[6, "module-plotano.rlhf"]], "plotano.rlhf.rlhf": [[6, "module-plotano.rlhf.rlhf"]], "ppo_batch_size (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.ppo_batch_size"]], "ppo_epochs (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.ppo_epochs"]], "prepare_sample_text() (plotano.rlhf.rlhf.sft method)": [[6, "plotano.rlhf.rlhf.SFT.prepare_sample_text"]], "push_to_hub (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.push_to_hub"]], "question_title (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.question_title"]], "read_json_file() (in module plotano.rlhf.rlhf)": [[6, "plotano.rlhf.rlhf.read_json_file"]], "remove_unused_columns (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.remove_unused_columns"]], "resume_from_checkpoint (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.resume_from_checkpoint"]], "reward_baseline (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.reward_baseline"]], "reward_epochs (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.reward_epochs"]], "reward_lora_path (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.reward_lora_path"]], "reward_merged_path (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.reward_merged_path"]], "reward_model_path (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.reward_model_path"]], "reward_num_of_data (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.reward_num_of_data"]], "save() (plotano.rlhf.rlhf.rewardtrainer method)": [[6, "plotano.rlhf.rlhf.RewardTrainer.save"]], "save() (plotano.rlhf.rlhf.sft method)": [[6, "plotano.rlhf.rlhf.SFT.save"]], "save_freq (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.save_freq"]], "seed (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.seed"]], "sft_lora_path (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.sft_lora_path"]], "sft_merged_path (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.sft_merged_path"]], "shuffle_buffer (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.shuffle_buffer"]], "size_valid_set (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.size_valid_set"]], "split (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.split"]], "streaming (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.streaming"]], "target_kl (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.target_kl"]], "top_k (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.top_k"]], "top_p (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.top_p"]], "total_ppo_epochs (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.total_ppo_epochs"]], "train() (plotano.rlhf.rlhf.sft method)": [[6, "plotano.rlhf.rlhf.SFT.train"]], "train_and_save() (plotano.rlhf.rlhf.rewardtrainer method)": [[6, "plotano.rlhf.rlhf.RewardTrainer.train_and_save"]], "train_and_save() (plotano.rlhf.rlhf.sft method)": [[6, "plotano.rlhf.rlhf.SFT.train_and_save"]], "train_test_split_ratio (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.train_test_split_ratio"]], "weight_decay (plotano.rlhf.rlhf.rlhfconfig attribute)": [[6, "plotano.rlhf.rlhf.RLHFConfig.weight_decay"]]}})
diff --git a/docs/conf.py b/docs/conf.py
index 04baca0..9ea2aba 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -8,30 +8,29 @@
 import os
 import sys
 
+sys.path.insert(0, os.path.abspath(".."))
 
-sys.path.insert(0, os.path.abspath('..'))
-
-project = 'plotano'
-copyright = '2023, CambioML'
-author = 'CambioML'
-release = '0.0.0'
+project = "plotano"
+copyright = "2023, CambioML"
+author = "CambioML"
+release = "0.0.0"
 
 # -- General configuration ---------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
 
 extensions = [
-    'sphinx.ext.todo',
-    'sphinx.ext.autodoc',
-    'sphinx.ext.viewcode',
-    'sphinx.ext.napoleon'
+    "sphinx.ext.todo",
+    "sphinx.ext.autodoc",
+    "sphinx.ext.viewcode",
+    "sphinx.ext.napoleon",
 ]
 
-templates_path = ['_templates']
-exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
+templates_path = ["_templates"]
+exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"]
 
 
 # -- Options for HTML output -------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output
 
-html_theme = 'sphinx_rtd_theme'
-html_static_path = ['_static']
+html_theme = "sphinx_rtd_theme"
+html_static_path = ["_static"]
diff --git a/example/chatbot/README.md b/example/chatbot/README.md
index 904b79a..8f9699c 100644
--- a/example/chatbot/README.md
+++ b/example/chatbot/README.md
@@ -49,4 +49,4 @@ uncomment the following lines in chatbot_demo.py and put your api_key in the var
 #### 2.1.3 Run
 ```
 python demo.py
-```
\ No newline at end of file
+```
diff --git a/example/chatbot/demo.py b/example/chatbot/demo.py
index 31abbbc..0f5388f 100644
--- a/example/chatbot/demo.py
+++ b/example/chatbot/demo.py
@@ -1,7 +1,6 @@
 """Demo for the chatbot application."""
 import plotano.cambio as cb
 
-
 ###########################################################
 # # Creating an OpenAI model (requires an OpenAI API key) #
 ###########################################################
@@ -18,7 +17,8 @@
     model_name="huggingface",
     pretrained_model_name_or_path="tiiuae/falcon-7b",
     trust_remote_code=True,
-    load_in_8bit=True)
+    load_in_8bit=True,
+)
 
 #####################################
 # Creating a chatbot with the model #
diff --git a/plotano/application.py b/plotano/application.py
index 44d8c95..f53c694 100644
--- a/plotano/application.py
+++ b/plotano/application.py
@@ -1,6 +1,7 @@
 """Application module."""
 from typing import Any, Dict
-from flask import Flask, jsonify, send_from_directory, request
+
+from flask import Flask, jsonify, request, send_from_directory
 from flask_cors import CORS
 from pyngrok import ngrok
 
@@ -11,6 +12,7 @@ class Application:
     """
     The Application class.
     """
+
     def __init__(self, share: bool = False, debug: bool = False):
         """
         Initialize the Application.
@@ -53,6 +55,7 @@ def create_chatbot_route(self, app: Flask, component: Dict[str, Any]):
             app (Flask): The Flask application.
             component (Dict[str, Any]): The component for which the routes are being created.
         """
+
         @app.route("/chat/<message>", methods=["POST"])
         def inference(message: str):
             try:
@@ -135,6 +138,7 @@ def create_feedback_route(self, app: Flask, component: Dict[str, Any]):
             app (Flask): The Flask application.
             component (Dict[str, Any]): The component for which the routes are being created.
         """
+
         @app.route("/chat/qa_table/retrieve", methods=["GET"])
         def retrieve_qa_table():
             try:
@@ -179,6 +183,7 @@ def create_data_route(id: str, data_source: Any):
                 id (str): The id of the data source.
                 data_source (Any): The data source.
             """
+
             @app.route(f"/data/{id}", methods=["GET"], endpoint=id)
             def get_data():
                 data = data_source.fetch_func()
diff --git a/plotano/component/base.py b/plotano/component/base.py
index 6888b56..1537e4c 100644
--- a/plotano/component/base.py
+++ b/plotano/component/base.py
@@ -2,11 +2,11 @@
 import uuid
 from typing import Callable, List, Optional, Union
 
+from plotano.component.chatbot_database_factory import ChatbotDatabaseFactory
+from plotano.component.constants import FeedbackType
 from plotano.db.qa_database import QuestionAnswerDatabase
 from plotano.db.ranking_database import RankingDatabase
 from plotano.llm.abs_llm import AbsLlm
-from plotano.component.constants import FeedbackType
-from plotano.component.chatbot_database_factory import ChatbotDatabaseFactory
 
 
 class DataSource:
@@ -41,10 +41,7 @@ class Component:
         props (Dict[str, Any]): Additional properties for the component.
     """
 
-    def __init__(self,
-                 fetch_func: Optional[Callable],
-                 svelte_component: str,
-                 **kwargs):
+    def __init__(self, fetch_func: Optional[Callable], svelte_component: str, **kwargs):
         """
         Initialize a new instance of Component.
 
@@ -67,10 +64,7 @@ class Dropdown(Component):
         value_column (str): The column to use for the dropdown values.
     """
 
-    def __init__(self,
-                 fetch_func: Callable,
-                 value_column: List[str],
-                 **kwargs):
+    def __init__(self, fetch_func: Callable, value_column: List[str], **kwargs):
         """
         Initialize a new instance of Dropdown.
 
@@ -92,9 +86,7 @@ class Chatbot(Component):
         database (str): The database to use for the chatbot.
     """
 
-    def __init__(self,
-                 model: AbsLlm,
-                 **kwargs):
+    def __init__(self, model: AbsLlm, **kwargs):
         """
         Initialize a new instance of Chatbot.
 
@@ -105,7 +97,8 @@ def __init__(self,
         super().__init__(None, "Chatbot", **kwargs)
         self.model = model
         self.database = ChatbotDatabaseFactory.create(
-            feedback=kwargs.get("feedback", "vote"))
+            feedback=kwargs.get("feedback", "vote")
+        )
 
 
 class Dashboard(Component):
@@ -116,9 +109,7 @@ class Dashboard(Component):
         database (str): The database to use for the dashboard.
     """
 
-    def __init__(self,
-                 database: QuestionAnswerDatabase,
-                 **kwargs):
+    def __init__(self, database: QuestionAnswerDatabase, **kwargs):
         """
         Initialize a new instance of Dashboard.
 
diff --git a/plotano/component/chatbot_database_factory.py b/plotano/component/chatbot_database_factory.py
index 2dfde00..9052570 100644
--- a/plotano/component/chatbot_database_factory.py
+++ b/plotano/component/chatbot_database_factory.py
@@ -1,9 +1,9 @@
 """Chatbot Database Factory class."""
 from typing import Union
 
+from plotano.component.constants import FeedbackType
 from plotano.db.qa_database import QuestionAnswerDatabase
 from plotano.db.ranking_database import RankingDatabase
-from plotano.component.constants import FeedbackType
 
 
 class ChatbotDatabaseFactory:
@@ -30,4 +30,5 @@ def create(feedback: Union[str, FeedbackType]):
         else:
             raise ValueError(
                 f"Invalid feedback name: {feedback}. "
-                f"Valid values are: 'question_answer', 'ranking'.")
+                f"Valid values are: 'question_answer', 'ranking'."
+            )
diff --git a/plotano/db/qa_database.py b/plotano/db/qa_database.py
index c66627c..21fdc58 100644
--- a/plotano/db/qa_database.py
+++ b/plotano/db/qa_database.py
@@ -1,32 +1,33 @@
 """Question answer database module"""
 import csv
+import datetime
 import os
 import sqlite3
 import threading
-import datetime
+
 import pandas as pd
 
 # QA_CSV_HEADER = ('ID', 'Question', 'Answer', 'Vote Status', 'Timestamp')
-QA_CSV_HEADER_ID = 'ID'
-QA_CSV_HEADER_QUESTION = 'Question'
-QA_CSV_HEADER_ANSWER = 'Answer'
-QA_CSV_HEADER_VOTE_STATUS = 'Vote Status'
-QA_CSV_HEADER_TIMESTAMPS = 'Timestamp'
+QA_CSV_HEADER_ID = "ID"
+QA_CSV_HEADER_QUESTION = "Question"
+QA_CSV_HEADER_ANSWER = "Answer"
+QA_CSV_HEADER_VOTE_STATUS = "Vote Status"
+QA_CSV_HEADER_TIMESTAMPS = "Timestamp"
 QA_CSV_HEADER = (
     QA_CSV_HEADER_ID,
     QA_CSV_HEADER_QUESTION,
     QA_CSV_HEADER_ANSWER,
     QA_CSV_HEADER_VOTE_STATUS,
-    QA_CSV_HEADER_TIMESTAMPS
+    QA_CSV_HEADER_TIMESTAMPS,
 )
 
 
 class QuestionAnswerDatabase:
     """Question Answer Database class"""
 
-    def __init__(self,
-                 db_file: str = os.path.join(os.getcwd(), 'qd.db'),
-                 debug: bool = False):
+    def __init__(
+        self, db_file: str = os.path.join(os.getcwd(), "qd.db"), debug: bool = False
+    ):
         """
         Initializes a new instance of the QuestionAnswerDatabase class.
 
@@ -151,7 +152,7 @@ def retrieve_all_question_answers(self):
             cursor.execute(query)
             rows = cursor.fetchall()
         return rows
-    
+
     def retrieve_all_question_answers_as_pandas(self):
         """
         Retrieves all question-answer pairs from the database as a pandas dataframe.
@@ -184,8 +185,10 @@ def print_table(self, rows):
                         Each tuple contains five elements: ID, Question, Answer, Timestamp, and Vote Status.
         """
         for row in rows:
-            print(f"ID: {row[0]}, Question: {row[1]}, "
-                  f"Answer: {row[2]}, Vote Status: {row[3]}, Timestamp: {row[4]}")
+            print(
+                f"ID: {row[0]}, Question: {row[1]}, "
+                f"Answer: {row[2]}, Vote Status: {row[3]}, Timestamp: {row[4]}"
+            )
 
     def save_to_csv(self, csv_file_name="question_answer_votes.csv"):
         """
@@ -203,7 +206,7 @@ def save_to_csv(self, csv_file_name="question_answer_votes.csv"):
         """
         my_sql_data = self.retrieve_all_question_answers()
 
-        with open(csv_file_name, 'w', newline='') as file:
+        with open(csv_file_name, "w", newline="") as file:
             writer = csv.writer(file)
             writer.writerow(QA_CSV_HEADER)
             writer.writerows(my_sql_data)
diff --git a/plotano/db/ranking_database.py b/plotano/db/ranking_database.py
index da2a1f3..7de39a5 100644
--- a/plotano/db/ranking_database.py
+++ b/plotano/db/ranking_database.py
@@ -4,24 +4,26 @@
 import sqlite3
 import threading
 
-CSV_HEADER_ID = 'ID'
-CSV_HEADER_QUESTION = 'Question'
-RANKING_CSV_HEADER_UP_RANKING_ANSWER = 'Up Ranking Answer'
-RANKING_CSV_HEADER_LOW_RANKING_ANSWER = 'Low Ranking Answer'
+CSV_HEADER_ID = "ID"
+CSV_HEADER_QUESTION = "Question"
+RANKING_CSV_HEADER_UP_RANKING_ANSWER = "Up Ranking Answer"
+RANKING_CSV_HEADER_LOW_RANKING_ANSWER = "Low Ranking Answer"
 RANKING_CSV_HEADER = (
     CSV_HEADER_ID,
     CSV_HEADER_QUESTION,
     RANKING_CSV_HEADER_UP_RANKING_ANSWER,
-    RANKING_CSV_HEADER_LOW_RANKING_ANSWER
+    RANKING_CSV_HEADER_LOW_RANKING_ANSWER,
 )
 
 
 class RankingDatabase:
     """Ranking Database class"""
 
-    def __init__(self,
-                 db_file: str = os.path.join(os.getcwd(), 'ranking.db'),
-                 debug: bool = False):
+    def __init__(
+        self,
+        db_file: str = os.path.join(os.getcwd(), "ranking.db"),
+        debug: bool = False,
+    ):
         """
         Initializes a new instance of the RankingDatabase class.
 
@@ -70,7 +72,9 @@ def create_table(self):
             print("Table contents after creating table:")
             self.print_table(rows)
 
-    def insert_ranking(self, question: str, up_ranking_answer: str, low_ranking_answer: str):
+    def insert_ranking(
+        self, question: str, up_ranking_answer: str, low_ranking_answer: str
+    ):
         """
         Inserts a new ranking entry into the database with the given question, up_ranking_answer,
         and low_ranking_answer.
@@ -135,8 +139,10 @@ def print_table(self, rows):
                          Each tuple contains four elements: ID, Question, Up_Ranking_Answer, Low_Ranking_Answer.
         """
         for row in rows:
-            print(f"ID: {row[0]}, Question: {row[1]}, "
-                  f"Up_Ranking_Answer: {row[2]}, Low_Ranking_Answer: {row[3]}")
+            print(
+                f"ID: {row[0]}, Question: {row[1]}, "
+                f"Up_Ranking_Answer: {row[2]}, Low_Ranking_Answer: {row[3]}"
+            )
 
     def save_to_csv(self, csv_file_name="ranking_data.csv"):
         """
@@ -154,7 +160,7 @@ def save_to_csv(self, csv_file_name="ranking_data.csv"):
         """
         ranking_data = self.retrieve_all_question_answers()
 
-        with open(csv_file_name, 'w', newline='') as file:
+        with open(csv_file_name, "w", newline="") as file:
             writer = csv.writer(file)
             writer.writerow(RANKING_CSV_HEADER)
             writer.writerows(ranking_data)
diff --git a/plotano/frontend/dist/vite.svg b/plotano/frontend/dist/vite.svg
index e7b8dfb..ee9fada 100644
--- a/plotano/frontend/dist/vite.svg
+++ b/plotano/frontend/dist/vite.svg
@@ -1 +1 @@
-<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" class="iconify iconify--logos" width="31.88" height="32" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 257"><defs><linearGradient id="IconifyId1813088fe1fbc01fb466" x1="-.828%" x2="57.636%" y1="7.652%" y2="78.411%"><stop offset="0%" stop-color="#41D1FF"></stop><stop offset="100%" stop-color="#BD34FE"></stop></linearGradient><linearGradient id="IconifyId1813088fe1fbc01fb467" x1="43.376%" x2="50.316%" y1="2.242%" y2="89.03%"><stop offset="0%" stop-color="#FFEA83"></stop><stop offset="8.333%" stop-color="#FFDD35"></stop><stop offset="100%" stop-color="#FFA800"></stop></linearGradient></defs><path fill="url(#IconifyId1813088fe1fbc01fb466)" d="M255.153 37.938L134.897 252.976c-2.483 4.44-8.862 4.466-11.382.048L.875 37.958c-2.746-4.814 1.371-10.646 6.827-9.67l120.385 21.517a6.537 6.537 0 0 0 2.322-.004l117.867-21.483c5.438-.991 9.574 4.796 6.877 9.62Z"></path><path fill="url(#IconifyId1813088fe1fbc01fb467)" d="M185.432.063L96.44 17.501a3.268 3.268 0 0 0-2.634 3.014l-5.474 92.456a3.268 3.268 0 0 0 3.997 3.378l24.777-5.718c2.318-.535 4.413 1.507 3.936 3.838l-7.361 36.047c-.495 2.426 1.782 4.5 4.151 3.78l15.304-4.649c2.372-.72 4.652 1.36 4.15 3.788l-11.698 56.621c-.732 3.542 3.979 5.473 5.943 2.437l1.313-2.028l72.516-144.72c1.215-2.423-.88-5.186-3.54-4.672l-25.505 4.922c-2.396.462-4.435-1.77-3.759-4.114l16.646-57.705c.677-2.35-1.37-4.583-3.769-4.113Z"></path></svg>
\ No newline at end of file
+<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" class="iconify iconify--logos" width="31.88" height="32" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 257"><defs><linearGradient id="IconifyId1813088fe1fbc01fb466" x1="-.828%" x2="57.636%" y1="7.652%" y2="78.411%"><stop offset="0%" stop-color="#41D1FF"></stop><stop offset="100%" stop-color="#BD34FE"></stop></linearGradient><linearGradient id="IconifyId1813088fe1fbc01fb467" x1="43.376%" x2="50.316%" y1="2.242%" y2="89.03%"><stop offset="0%" stop-color="#FFEA83"></stop><stop offset="8.333%" stop-color="#FFDD35"></stop><stop offset="100%" stop-color="#FFA800"></stop></linearGradient></defs><path fill="url(#IconifyId1813088fe1fbc01fb466)" d="M255.153 37.938L134.897 252.976c-2.483 4.44-8.862 4.466-11.382.048L.875 37.958c-2.746-4.814 1.371-10.646 6.827-9.67l120.385 21.517a6.537 6.537 0 0 0 2.322-.004l117.867-21.483c5.438-.991 9.574 4.796 6.877 9.62Z"></path><path fill="url(#IconifyId1813088fe1fbc01fb467)" d="M185.432.063L96.44 17.501a3.268 3.268 0 0 0-2.634 3.014l-5.474 92.456a3.268 3.268 0 0 0 3.997 3.378l24.777-5.718c2.318-.535 4.413 1.507 3.936 3.838l-7.361 36.047c-.495 2.426 1.782 4.5 4.151 3.78l15.304-4.649c2.372-.72 4.652 1.36 4.15 3.788l-11.698 56.621c-.732 3.542 3.979 5.473 5.943 2.437l1.313-2.028l72.516-144.72c1.215-2.423-.88-5.186-3.54-4.672l-25.505 4.922c-2.396.462-4.435-1.77-3.759-4.114l16.646-57.705c.677-2.35-1.37-4.583-3.769-4.113Z"></path></svg>
diff --git a/plotano/frontend/public/vite.svg b/plotano/frontend/public/vite.svg
index e7b8dfb..ee9fada 100644
--- a/plotano/frontend/public/vite.svg
+++ b/plotano/frontend/public/vite.svg
@@ -1 +1 @@
-<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" class="iconify iconify--logos" width="31.88" height="32" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 257"><defs><linearGradient id="IconifyId1813088fe1fbc01fb466" x1="-.828%" x2="57.636%" y1="7.652%" y2="78.411%"><stop offset="0%" stop-color="#41D1FF"></stop><stop offset="100%" stop-color="#BD34FE"></stop></linearGradient><linearGradient id="IconifyId1813088fe1fbc01fb467" x1="43.376%" x2="50.316%" y1="2.242%" y2="89.03%"><stop offset="0%" stop-color="#FFEA83"></stop><stop offset="8.333%" stop-color="#FFDD35"></stop><stop offset="100%" stop-color="#FFA800"></stop></linearGradient></defs><path fill="url(#IconifyId1813088fe1fbc01fb466)" d="M255.153 37.938L134.897 252.976c-2.483 4.44-8.862 4.466-11.382.048L.875 37.958c-2.746-4.814 1.371-10.646 6.827-9.67l120.385 21.517a6.537 6.537 0 0 0 2.322-.004l117.867-21.483c5.438-.991 9.574 4.796 6.877 9.62Z"></path><path fill="url(#IconifyId1813088fe1fbc01fb467)" d="M185.432.063L96.44 17.501a3.268 3.268 0 0 0-2.634 3.014l-5.474 92.456a3.268 3.268 0 0 0 3.997 3.378l24.777-5.718c2.318-.535 4.413 1.507 3.936 3.838l-7.361 36.047c-.495 2.426 1.782 4.5 4.151 3.78l15.304-4.649c2.372-.72 4.652 1.36 4.15 3.788l-11.698 56.621c-.732 3.542 3.979 5.473 5.943 2.437l1.313-2.028l72.516-144.72c1.215-2.423-.88-5.186-3.54-4.672l-25.505 4.922c-2.396.462-4.435-1.77-3.759-4.114l16.646-57.705c.677-2.35-1.37-4.583-3.769-4.113Z"></path></svg>
\ No newline at end of file
+<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" class="iconify iconify--logos" width="31.88" height="32" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 257"><defs><linearGradient id="IconifyId1813088fe1fbc01fb466" x1="-.828%" x2="57.636%" y1="7.652%" y2="78.411%"><stop offset="0%" stop-color="#41D1FF"></stop><stop offset="100%" stop-color="#BD34FE"></stop></linearGradient><linearGradient id="IconifyId1813088fe1fbc01fb467" x1="43.376%" x2="50.316%" y1="2.242%" y2="89.03%"><stop offset="0%" stop-color="#FFEA83"></stop><stop offset="8.333%" stop-color="#FFDD35"></stop><stop offset="100%" stop-color="#FFA800"></stop></linearGradient></defs><path fill="url(#IconifyId1813088fe1fbc01fb466)" d="M255.153 37.938L134.897 252.976c-2.483 4.44-8.862 4.466-11.382.048L.875 37.958c-2.746-4.814 1.371-10.646 6.827-9.67l120.385 21.517a6.537 6.537 0 0 0 2.322-.004l117.867-21.483c5.438-.991 9.574 4.796 6.877 9.62Z"></path><path fill="url(#IconifyId1813088fe1fbc01fb467)" d="M185.432.063L96.44 17.501a3.268 3.268 0 0 0-2.634 3.014l-5.474 92.456a3.268 3.268 0 0 0 3.997 3.378l24.777-5.718c2.318-.535 4.413 1.507 3.936 3.838l-7.361 36.047c-.495 2.426 1.782 4.5 4.151 3.78l15.304-4.649c2.372-.72 4.652 1.36 4.15 3.788l-11.698 56.621c-.732 3.542 3.979 5.473 5.943 2.437l1.313-2.028l72.516-144.72c1.215-2.423-.88-5.186-3.54-4.672l-25.505 4.922c-2.396.462-4.435-1.77-3.759-4.114l16.646-57.705c.677-2.35-1.37-4.583-3.769-4.113Z"></path></svg>
diff --git a/plotano/frontend/src/app.css b/plotano/frontend/src/app.css
index d24060d..4601d7c 100644
--- a/plotano/frontend/src/app.css
+++ b/plotano/frontend/src/app.css
@@ -547,4 +547,4 @@ button.small {
   }
   button {
 	margin: 5px;
-  }
\ No newline at end of file
+  }
diff --git a/plotano/frontend/src/assets/svelte.svg b/plotano/frontend/src/assets/svelte.svg
index c5e0848..8c056ce 100644
--- a/plotano/frontend/src/assets/svelte.svg
+++ b/plotano/frontend/src/assets/svelte.svg
@@ -1 +1 @@
-<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" class="iconify iconify--logos" width="26.6" height="32" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 308"><path fill="#FF3E00" d="M239.682 40.707C211.113-.182 154.69-12.301 113.895 13.69L42.247 59.356a82.198 82.198 0 0 0-37.135 55.056a86.566 86.566 0 0 0 8.536 55.576a82.425 82.425 0 0 0-12.296 30.719a87.596 87.596 0 0 0 14.964 66.244c28.574 40.893 84.997 53.007 125.787 27.016l71.648-45.664a82.182 82.182 0 0 0 37.135-55.057a86.601 86.601 0 0 0-8.53-55.577a82.409 82.409 0 0 0 12.29-30.718a87.573 87.573 0 0 0-14.963-66.244"></path><path fill="#FFF" d="M106.889 270.841c-23.102 6.007-47.497-3.036-61.103-22.648a52.685 52.685 0 0 1-9.003-39.85a49.978 49.978 0 0 1 1.713-6.693l1.35-4.115l3.671 2.697a92.447 92.447 0 0 0 28.036 14.007l2.663.808l-.245 2.659a16.067 16.067 0 0 0 2.89 10.656a17.143 17.143 0 0 0 18.397 6.828a15.786 15.786 0 0 0 4.403-1.935l71.67-45.672a14.922 14.922 0 0 0 6.734-9.977a15.923 15.923 0 0 0-2.713-12.011a17.156 17.156 0 0 0-18.404-6.832a15.78 15.78 0 0 0-4.396 1.933l-27.35 17.434a52.298 52.298 0 0 1-14.553 6.391c-23.101 6.007-47.497-3.036-61.101-22.649a52.681 52.681 0 0 1-9.004-39.849a49.428 49.428 0 0 1 22.34-33.114l71.664-45.677a52.218 52.218 0 0 1 14.563-6.398c23.101-6.007 47.497 3.036 61.101 22.648a52.685 52.685 0 0 1 9.004 39.85a50.559 50.559 0 0 1-1.713 6.692l-1.35 4.116l-3.67-2.693a92.373 92.373 0 0 0-28.037-14.013l-2.664-.809l.246-2.658a16.099 16.099 0 0 0-2.89-10.656a17.143 17.143 0 0 0-18.398-6.828a15.786 15.786 0 0 0-4.402 1.935l-71.67 45.674a14.898 14.898 0 0 0-6.73 9.975a15.9 15.9 0 0 0 2.709 12.012a17.156 17.156 0 0 0 18.404 6.832a15.841 15.841 0 0 0 4.402-1.935l27.345-17.427a52.147 52.147 0 0 1 14.552-6.397c23.101-6.006 47.497 3.037 61.102 22.65a52.681 52.681 0 0 1 9.003 39.848a49.453 49.453 0 0 1-22.34 33.12l-71.664 45.673a52.218 52.218 0 0 1-14.563 6.398"></path></svg>
\ No newline at end of file
+<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" class="iconify iconify--logos" width="26.6" height="32" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 308"><path fill="#FF3E00" d="M239.682 40.707C211.113-.182 154.69-12.301 113.895 13.69L42.247 59.356a82.198 82.198 0 0 0-37.135 55.056a86.566 86.566 0 0 0 8.536 55.576a82.425 82.425 0 0 0-12.296 30.719a87.596 87.596 0 0 0 14.964 66.244c28.574 40.893 84.997 53.007 125.787 27.016l71.648-45.664a82.182 82.182 0 0 0 37.135-55.057a86.601 86.601 0 0 0-8.53-55.577a82.409 82.409 0 0 0 12.29-30.718a87.573 87.573 0 0 0-14.963-66.244"></path><path fill="#FFF" d="M106.889 270.841c-23.102 6.007-47.497-3.036-61.103-22.648a52.685 52.685 0 0 1-9.003-39.85a49.978 49.978 0 0 1 1.713-6.693l1.35-4.115l3.671 2.697a92.447 92.447 0 0 0 28.036 14.007l2.663.808l-.245 2.659a16.067 16.067 0 0 0 2.89 10.656a17.143 17.143 0 0 0 18.397 6.828a15.786 15.786 0 0 0 4.403-1.935l71.67-45.672a14.922 14.922 0 0 0 6.734-9.977a15.923 15.923 0 0 0-2.713-12.011a17.156 17.156 0 0 0-18.404-6.832a15.78 15.78 0 0 0-4.396 1.933l-27.35 17.434a52.298 52.298 0 0 1-14.553 6.391c-23.101 6.007-47.497-3.036-61.101-22.649a52.681 52.681 0 0 1-9.004-39.849a49.428 49.428 0 0 1 22.34-33.114l71.664-45.677a52.218 52.218 0 0 1 14.563-6.398c23.101-6.007 47.497 3.036 61.101 22.648a52.685 52.685 0 0 1 9.004 39.85a50.559 50.559 0 0 1-1.713 6.692l-1.35 4.116l-3.67-2.693a92.373 92.373 0 0 0-28.037-14.013l-2.664-.809l.246-2.658a16.099 16.099 0 0 0-2.89-10.656a17.143 17.143 0 0 0-18.398-6.828a15.786 15.786 0 0 0-4.402 1.935l-71.67 45.674a14.898 14.898 0 0 0-6.73 9.975a15.9 15.9 0 0 0 2.709 12.012a17.156 17.156 0 0 0 18.404 6.832a15.841 15.841 0 0 0 4.402-1.935l27.345-17.427a52.147 52.147 0 0 1 14.552-6.397c23.101-6.006 47.497 3.037 61.102 22.65a52.681 52.681 0 0 1 9.003 39.848a49.453 49.453 0 0 1-22.34 33.12l-71.664 45.673a52.218 52.218 0 0 1-14.563 6.398"></path></svg>
diff --git a/plotano/frontend/src/normalize.css b/plotano/frontend/src/normalize.css
index 8c331ec..88f69b9 100644
--- a/plotano/frontend/src/normalize.css
+++ b/plotano/frontend/src/normalize.css
@@ -347,4 +347,4 @@
   [hidden] {
     display: none;
   }
-  
\ No newline at end of file
+  
diff --git a/plotano/frontend/src/styles.css b/plotano/frontend/src/styles.css
index 4c654d6..4641cbd 100644
--- a/plotano/frontend/src/styles.css
+++ b/plotano/frontend/src/styles.css
@@ -467,4 +467,4 @@ button.small {
   }
   button {
 	margin: 5px;
-  }
\ No newline at end of file
+  }
diff --git a/plotano/llm/abs_llm.py b/plotano/llm/abs_llm.py
index ab05a05..518758a 100644
--- a/plotano/llm/abs_llm.py
+++ b/plotano/llm/abs_llm.py
@@ -13,9 +13,7 @@ class AbsLlm(abc.ABC):
     """
 
     @abc.abstractmethod
-    def predict(self,
-                message: str,
-                num_of_response: int):
+    def predict(self, message: str, num_of_response: int):
         """Predict the next word based on the input message.
 
         This method must be implemented by any subclass of `AbsLlm`.
diff --git a/plotano/llm/constants.py b/plotano/llm/constants.py
index dfb9393..bf1dbf0 100644
--- a/plotano/llm/constants.py
+++ b/plotano/llm/constants.py
@@ -10,5 +10,6 @@ class LlmName(Enum):
         OPENAI (str): Represents the OpenAI model.
         HUGGINGFACE (str): Represents the HuggingFace model.
     """
+
     OPENAI = "openai"
     HUGGINGFACE = "huggingface"
diff --git a/plotano/llm/huggingface.py b/plotano/llm/huggingface.py
index c3cc508..257599d 100644
--- a/plotano/llm/huggingface.py
+++ b/plotano/llm/huggingface.py
@@ -1,7 +1,5 @@
 """Hubbingface model for Language Model (LLM)."""
-from transformers import (
-    AutoModelForCausalLM,
-    AutoTokenizer)
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
 from plotano.llm.abs_llm import AbsLlm
 
@@ -12,12 +10,14 @@ class HuggingfaceModel(AbsLlm):
     It inherits from the abstract base class AbsLlm.
     """
 
-    def __init__(self,
-                 pretrained_model_name_or_path: str,
-                 trust_remote_code: bool = True,
-                 load_in_8bit: bool = True,
-                 max_length: int = 100,
-                 device_map: str = "auto"):
+    def __init__(
+        self,
+        pretrained_model_name_or_path: str,
+        trust_remote_code: bool = True,
+        load_in_8bit: bool = True,
+        max_length: int = 100,
+        device_map: str = "auto",
+    ):
         """
         Initialize the Huggingface model.
 
@@ -34,19 +34,19 @@ def __init__(self,
             pretrained_model_name_or_path,
             trust_remote_code=trust_remote_code,
             load_in_8bit=load_in_8bit,
-            device_map=device_map)
+            device_map=device_map,
+        )
         print("[HuggingfaceModel] loading tokenizer...")
         self._tokenizer = AutoTokenizer.from_pretrained(
             pretrained_model_name_or_path,
             trust_remote_code=trust_remote_code,
             load_in_8bit=load_in_8bit,
-            device_map=device_map)
+            device_map=device_map,
+        )
         self._max_length = max_length
         super().__init__()
 
-    def predict(self,
-                message: str,
-                num_of_response: int = 1):
+    def predict(self, message: str, num_of_response: int = 1):
         """
         Predict the next word based on the input message.
 
@@ -58,18 +58,20 @@ def predict(self,
             List[str]: List of response.
         """
         print("[HuggingfaceModel] encode...")
-        input_ids = self._tokenizer.encode(message,
-                                           return_tensors="pt")
-        input_ids = input_ids.to('cuda')
+        input_ids = self._tokenizer.encode(message, return_tensors="pt")
+        input_ids = input_ids.to("cuda")
         print("[HuggingfaceModel] generate...")
-        output_ids = self._model.generate(input_ids,
-                                          max_length=self._max_length,
-                                          num_return_sequences=num_of_response,
-                                          do_sample=True,
-                                          temperature=0.3)
+        output_ids = self._model.generate(
+            input_ids,
+            max_length=self._max_length,
+            num_return_sequences=num_of_response,
+            do_sample=True,
+            temperature=0.3,
+        )
         print("[HuggingfaceModel] decode...")
-        response = [self._tokenizer.decode(
-            ids, skip_special_tokens=True) for ids in output_ids]
+        response = [
+            self._tokenizer.decode(ids, skip_special_tokens=True) for ids in output_ids
+        ]
 
         response = [resp.split("\n")[1] for resp in response if "\n" in resp]
 
diff --git a/plotano/llm/model_factory.py b/plotano/llm/model_factory.py
index 699dfa7..ec8cdd0 100644
--- a/plotano/llm/model_factory.py
+++ b/plotano/llm/model_factory.py
@@ -2,30 +2,30 @@
 
 from typing import Union
 
-from plotano.llm.constants import LlmName
 from plotano.llm.abs_llm import AbsLlm
+from plotano.llm.constants import LlmName
 
 
 class ModelFactory:
     """
     A factory class for creating language models.
 
-    This class provides a static method `create_model` which creates a 
+    This class provides a static method `create_model` which creates a
     language model instance based on the given name.
 
     Methods:
-        create_model(model_name: Union[str, LlmName], **kwargs) -> AbsLlm: 
+        create_model(model_name: Union[str, LlmName], **kwargs) -> AbsLlm:
             Creates a language model based on the given name.
     """
+
     @staticmethod
-    def create_model(model_name: Union[str, LlmName],
-                     **kwargs) -> AbsLlm:
+    def create_model(model_name: Union[str, LlmName], **kwargs) -> AbsLlm:
         """
         Create a language model based on the given name.
 
-        This method tries to match the given model name with the names defined 
-        in the `LlmName` enumeration. If a match is found, it creates an 
-        instance of the corresponding language model. If no match is found, 
+        This method tries to match the given model name with the names defined
+        in the `LlmName` enumeration. If a match is found, it creates an
+        instance of the corresponding language model. If no match is found,
         it raises a ValueError.
 
         Args:
@@ -48,7 +48,6 @@ def create_model(model_name: Union[str, LlmName],
 
                 return HuggingfaceModel(**kwargs)
             else:
-                raise ValueError(f"[llm_factory]: Unknown model "
-                                 f"{model_name}")
+                raise ValueError(f"[llm_factory]: Unknown model " f"{model_name}")
         except ValueError as ex:
             raise ValueError("[llm_factory]: initialize model failure") from ex
diff --git a/plotano/llm/openai.py b/plotano/llm/openai.py
index e8ab5ef..d87fa3e 100644
--- a/plotano/llm/openai.py
+++ b/plotano/llm/openai.py
@@ -1,5 +1,6 @@
 """This module provides a wrapper for the OpenAI model."""
 import openai
+
 from plotano.llm.abs_llm import AbsLlm
 
 
@@ -17,11 +18,13 @@ class OpenAIModel(AbsLlm):
         predict(self, message: str): Predicts the next word based on the given message.
     """
 
-    def __init__(self,
-                 api_key: str,
-                 engine: str = "davinci",
-                 max_tokens: int = 100,
-                 temperature: float = 0.5):
+    def __init__(
+        self,
+        api_key: str,
+        engine: str = "davinci",
+        max_tokens: int = 100,
+        temperature: float = 0.5,
+    ):
         """
         Initializes the OpenAI model with the given parameters.
 
@@ -37,9 +40,7 @@ def __init__(self,
         self._temperature = temperature
         super().__init__()
 
-    def predict(self,
-                message: str,
-                num_of_response: int = 1):
+    def predict(self, message: str, num_of_response: int = 1):
         """
         Predicts the next word based on the given message.
 
diff --git a/plotano/rlhf/rlhf.py b/plotano/rlhf/rlhf.py
index 8353ecc..4532763 100644
--- a/plotano/rlhf/rlhf.py
+++ b/plotano/rlhf/rlhf.py
@@ -13,45 +13,25 @@
 # limitations under the License.
 
 
-import os
 import json
+import os
 from dataclasses import dataclass, field
 from typing import Any, Dict, List, Optional
 
 import evaluate
 import numpy as np
 import torch
-
 from accelerate import Accelerator
 from datasets import load_dataset
-from peft import (
-    LoraConfig, 
-    PeftConfig,
-    PeftModel,
-    TaskType,
-    get_peft_model
-)
+from peft import LoraConfig, PeftConfig, PeftModel, TaskType, get_peft_model
 from tqdm import tqdm
-from transformers import (
-    Adafactor,
-    AutoModelForCausalLM, 
-    AutoModelForSequenceClassification,
-    AutoTokenizer, 
-    Trainer,
-    TrainerCallback,
-    TrainingArguments, 
-    logging, 
-    pipeline,
-    set_seed
-    )
+from transformers import (Adafactor, AutoModelForCausalLM,
+                          AutoModelForSequenceClassification, AutoTokenizer,
+                          Trainer, TrainerCallback, TrainingArguments, logging,
+                          pipeline, set_seed)
 from transformers.utils import PushToHubMixin
-from trl import (
-    AutoModelForCausalLMWithValueHead, 
-    PPOConfig, 
-    PPOTrainer, 
-    SFTTrainer
-    )
-    
+from trl import (AutoModelForCausalLMWithValueHead, PPOConfig, PPOTrainer,
+                 SFTTrainer)
 from trl.core import LengthSampler
 from trl.trainer.utils import ConstantLengthDataset, PeftSavingCallback
 
@@ -66,7 +46,7 @@ def read_json_file(file_path):
     Returns:
         dict: The contents of the JSON file as a dictionary.
     """
-    with open(file_path, 'r') as file:
+    with open(file_path, "r") as file:
         data = json.load(file)
     return data
 
@@ -74,115 +54,151 @@ def read_json_file(file_path):
 @dataclass
 class RLHFConfig:
     """
-    This file contains the configuration parameters for the RLHF (Reinforcement Learning for Humans Feedback) model. 
-    The parameters are divided into three steps: 
+    This file contains the configuration parameters for the RLHF (Reinforcement Learning for Humans Feedback) model.
+    The parameters are divided into three steps:
         - Step 1: SFT (Supervised Fine-Tuning) parameters
         - Step 2: Reward Modeling parameters
         - Step 3: Reinforcement Learning parameters
     """
+
     base_model_path: str = field(
-        default="meta-llama/Llama-2-7b-hf", 
-        metadata={"help": "Huggingface model name or a local path to the base model."})
+        default="meta-llama/Llama-2-7b-hf",
+        metadata={"help": "Huggingface model name or a local path to the base model."},
+    )
     dataset_type: Optional[str] = field(
         default="csv",
-        metadata={"help": "'csv':load from a local csv path; 'huggingface': load a huggingface dataset."})
+        metadata={
+            "help": "'csv':load from a local csv path; 'huggingface': load a huggingface dataset."
+        },
+    )
     dataset_name: Optional[str] = field(
-        default="lvwerra/stack-exchange-paired", 
-        metadata={"help": "Huggingface dataset name or a local path to the dataset."})
+        default="lvwerra/stack-exchange-paired",
+        metadata={"help": "Huggingface dataset name or a local path to the dataset."},
+    )
     train_test_split_ratio: Optional[float] = field(
         default=0.1,
-        metadata={"help": "The ratio represents the proportion of the test dataset to \
-                  include in the train and test split."}
+        metadata={
+            "help": "The ratio represents the proportion of the test dataset to \
+                  include in the train and test split."
+        },
     )
     streaming: Optional[bool] = field(
-        default=False, 
-        metadata={"help": "Whether to use streaming."})
+        default=False, metadata={"help": "Whether to use streaming."}
+    )
     shuffle_buffer: Optional[int] = field(
-        default=5000, 
-        metadata={"help": "Size of the shuffle buffer."})
+        default=5000, metadata={"help": "Size of the shuffle buffer."}
+    )
     max_seq_length: Optional[int] = field(
-        default=512, 
-        metadata={"help": "Maximum sequence length."})
+        default=512, metadata={"help": "Maximum sequence length."}
+    )
     evaluation_strategy: Optional[str] = field(
         default="steps",
-        metadata={"help": "The evaluation strategy to adopt during training."})
+        metadata={"help": "The evaluation strategy to adopt during training."},
+    )
     # batch_size: int = field(
-    #     default=8, 
+    #     default=8,
     #     metadata={"help": "Batch size."})
     per_device_train_batch_size: Optional[int] = field(
-        default=2, metadata={"help": "Batch size per device for training."})
+        default=2, metadata={"help": "Batch size per device for training."}
+    )
     per_device_eval_batch_size: Optional[int] = field(
-        default=8, metadata={"help": "Batch size per device for evaluation."})
+        default=8, metadata={"help": "Batch size per device for evaluation."}
+    )
     gradient_accumulation_steps: Optional[int] = field(
-        default=4, 
-        metadata={"help": "Number of steps for gradient accumulation."})
+        default=4, metadata={"help": "Number of steps for gradient accumulation."}
+    )
     eos_token_id: Optional[int] = field(
-        default=49152, 
-        metadata={"help": "End-of-sequence token ID."})
+        default=49152, metadata={"help": "End-of-sequence token ID."}
+    )
     learning_rate: Optional[float] = field(
-        default=1e-5, metadata={"help": "Learning rate."})
+        default=1e-5, metadata={"help": "Learning rate."}
+    )
     weight_decay: Optional[float] = field(
-        default=0.01, 
-        metadata={"help": "Weight decay."})
-    local_rank: Optional[int] = field(default=-1, metadata={"help": "Used for multi-gpu."})
+        default=0.01, metadata={"help": "Weight decay."}
+    )
+    local_rank: Optional[int] = field(
+        default=-1, metadata={"help": "Used for multi-gpu."}
+    )
     fp16: Optional[bool] = field(default=True, metadata={"help": "Enable FP16."})
     bf16: Optional[bool] = field(default=False, metadata={"help": "Enable BF16."})
-    load_in_8bit: Optional[bool] = field(default=True, metadata={"help": "Whether load the model weights in 8-bit or not."})
+    load_in_8bit: Optional[bool] = field(
+        default=True,
+        metadata={"help": "Whether load the model weights in 8-bit or not."},
+    )
     device_map: Optional[dict] = field(
         default_factory=lambda: {"": Accelerator().process_index},
-        metadata={"help": "specify the mapping of model layers to specific devices, such as different GPUs \
+        metadata={
+            "help": "specify the mapping of model layers to specific devices, such as different GPUs \
                   in a multi-GPU setup. This can be helpful for distributing the computational load of a \
-                  large model across multiple GPUs."})
+                  large model across multiple GPUs."
+        },
+    )
     gradient_checkpointing: Optional[bool] = field(
-        default=False, 
-        metadata={"help": "Enable gradient checkpointing."})
+        default=False, metadata={"help": "Enable gradient checkpointing."}
+    )
     seed: Optional[int] = field(default=0, metadata={"help": "Random seed."})
-    num_workers: Optional[int] = field(default=None, metadata={"help": "Number of workers."})
+    num_workers: Optional[int] = field(
+        default=None, metadata={"help": "Number of workers."}
+    )
     output_dir: Optional[str] = field(
-        default="./rlhf_checkpoints", 
-        metadata={"help": "Output directory for all model weights."})
+        default="./rlhf_checkpoints",
+        metadata={"help": "Output directory for all model weights."},
+    )
     log_freq: Optional[int] = field(default=1, metadata={"help": "Logging frequency."})
-    eval_freq: Optional[int] = field(default=1000, metadata={"help": "Evaluation frequency."})
-    save_freq: Optional[int] = field(default=1000, metadata={"help": "Model saving frequency."})
+    eval_freq: Optional[int] = field(
+        default=1000, metadata={"help": "Evaluation frequency."}
+    )
+    save_freq: Optional[int] = field(
+        default=1000, metadata={"help": "Model saving frequency."}
+    )
     push_to_hub: Optional[bool] = field(
-        default=False, metadata={"help": "Whether push to Huggingface Hub or not."})
-    
+        default=False, metadata={"help": "Whether push to Huggingface Hub or not."}
+    )
+
     ## Step 1 SFT parameters
     max_steps: Optional[int] = field(
-        default=1000, 
-        metadata={"help": "Maximum number of training steps."})
+        default=1000, metadata={"help": "Maximum number of training steps."}
+    )
     dataset_subset_sft: Optional[str] = field(
-        default="data/finetune", 
-        metadata={"help": "Subset folder of the dataset to use."})
+        default="data/finetune",
+        metadata={"help": "Subset folder of the dataset to use."},
+    )
     dataset_subset_sft_train: Optional[int] = field(
-        default=10000, 
-        metadata={"help": "The size of the subset of the training data to use."})
+        default=10000,
+        metadata={"help": "The size of the subset of the training data to use."},
+    )
     split: Optional[str] = field(
-        default="train", 
-        metadata={"help": "Dataset split to use."})
+        default="train", metadata={"help": "Dataset split to use."}
+    )
     question_title: Optional[str] = field(
         default="Question",
-        metadata={"help": "the column name of questions from the database."}
+        metadata={"help": "the column name of questions from the database."},
     )
     answer_title: Optional[str] = field(
         default="Answer",
-        metadata={"help": "the column name of answers from the database."}
+        metadata={"help": "the column name of answers from the database."},
     )
     size_valid_set: Optional[int] = field(
-        default=4000, 
-        metadata={"help": "Size of the validation/eval set."})
+        default=4000, metadata={"help": "Size of the validation/eval set."}
+    )
     sft_lora_path: Optional[str] = field(
-        default="step1_supervised_finetuning_lora_final/", 
-        metadata={"help": "Output directory for step 1 supervised finetuning's Lora weights."})
+        default="step1_supervised_finetuning_lora_final/",
+        metadata={
+            "help": "Output directory for step 1 supervised finetuning's Lora weights."
+        },
+    )
     sft_merged_path: Optional[str] = field(
-        default="step1_supervised_finetuning_merged/", 
-        metadata={"help": "Output directory for step 1 supervised finetuning's merged weights."})
+        default="step1_supervised_finetuning_merged/",
+        metadata={
+            "help": "Output directory for step 1 supervised finetuning's merged weights."
+        },
+    )
     lr_scheduler_type_sft: Optional[str] = field(
-        default="cosine", 
-        metadata={"help": "Type of learning rate scheduler."})
+        default="cosine", metadata={"help": "Type of learning rate scheduler."}
+    )
     num_warmup_steps: Optional[int] = field(
-        default=100, 
-        metadata={"help": "Number of warmup steps for the scheduler."})
+        default=100, metadata={"help": "Number of warmup steps for the scheduler."}
+    )
     lora_config_rl: Optional[LoraConfig] = field(
         default=LoraConfig(
             r=32,
@@ -196,57 +212,75 @@ class RLHFConfig:
 
     ## Step 2 reward modeling parameters
     reward_model_path: Optional[str] = field(
-        default="databricks/dolly-v2-3b", 
-        metadata={"help": "Huggingface model name or a local path to the reward model."})
+        default="databricks/dolly-v2-3b",
+        metadata={
+            "help": "Huggingface model name or a local path to the reward model."
+        },
+    )
     reward_lora_path: Optional[str] = field(
-        default="step1_supervised_finetuning_lora_final/", 
-        metadata={"help": "Output directory for step 1 supervised finetuning's Lora weights."})
+        default="step1_supervised_finetuning_lora_final/",
+        metadata={
+            "help": "Output directory for step 1 supervised finetuning's Lora weights."
+        },
+    )
     reward_merged_path: Optional[str] = field(
-        default="step1_supervised_finetuning_merged/", 
-        metadata={"help": "Output directory for step 1 supervised finetuning's merged weights."})
+        default="step1_supervised_finetuning_merged/",
+        metadata={
+            "help": "Output directory for step 1 supervised finetuning's merged weights."
+        },
+    )
     resume_from_checkpoint: Optional[bool] = field(
         default=False,
         metadata={"help": "If you want to resume training where it left off."},
     )
     dataset_reward_folder: Optional[str] = field(
-        default="data/reward", 
-        metadata={"help": "Subset folder of the reward dataset to use."})
+        default="data/reward",
+        metadata={"help": "Subset folder of the reward dataset to use."},
+    )
     dataset_eval_folder: Optional[str] = field(
-        default="data/evaluation", 
-        metadata={"help": "Subset folder of the evaluation dataset to use."})
+        default="data/evaluation",
+        metadata={"help": "Subset folder of the evaluation dataset to use."},
+    )
     reward_num_of_data: Optional[int] = field(
-        default=1000, 
-        metadata={"help": "The size of the subset of the training data to use."})
+        default=1000,
+        metadata={"help": "The size of the subset of the training data to use."},
+    )
     max_seq_length_reward: Optional[int] = field(
-        default=512, 
-        metadata={"help": "Maximum sequence length."})
+        default=512, metadata={"help": "Maximum sequence length."}
+    )
     # dataset_subset_reward_eval: Optional[int] = field(
-    #     default=400, 
+    #     default=400,
     #     metadata={"help": "The size of the subset of the validation/eval data to use."})
     reward_epochs: Optional[int] = field(
-        default=10, metadata={"help": "The number of training epochs for reward modeling."})
+        default=10,
+        metadata={"help": "The number of training epochs for reward modeling."},
+    )
     deepspeed: Optional[str] = field(
-        default=None, ## TODO
-        metadata={"help": "Path to deepspeed config if using deepspeed."})
+        default=None,  ## TODO
+        metadata={"help": "Path to deepspeed config if using deepspeed."},
+    )
     remove_unused_columns: Optional[bool] = field(
-        default=False, 
-        metadata={"help": "Whether to remove unused columns from the dataset."})
+        default=False,
+        metadata={"help": "Whether to remove unused columns from the dataset."},
+    )
     label_names: Optional[List[str]] = field(
-        default_factory=list, 
-        metadata={"help": "List of column names in the dataset to use as labels."})
+        default_factory=list,
+        metadata={"help": "List of column names in the dataset to use as labels."},
+    )
     logging_strategy: Optional[str] = field(
-        default="steps", 
-        metadata={"help": "The strategy used for logging during training."})
+        default="steps",
+        metadata={"help": "The strategy used for logging during training."},
+    )
     logging_steps: Optional[int] = field(
-        default=10, 
-        metadata={"help": "The number of steps between each logging."})
+        default=10, metadata={"help": "The number of steps between each logging."}
+    )
     # callbacks: Optional[List[TrainerCallback]] = field(
     #     default=[], ## PeftSavingCallback()
     #     metadata={"help": "The callbacks to use for training."}),
     # optim: Optional[str] = field(
     #     default="adamw_hf", metadata={"help": "The optimizer to use."})
     # lr_scheduler_type_rw: str = field(
-    #     default="linear", 
+    #     default="linear",
     #     metadata={"help": "Type of learning rate scheduler."})
     lora_config_reward: Optional[LoraConfig] = field(
         default=LoraConfig(
@@ -261,53 +295,66 @@ class RLHFConfig:
 
     ## Step 3 RL parameters
     dataset_subset_rl: Optional[str] = field(
-        default="data/finetune", 
-        metadata={"help": "Subset folder of the dataset to use."})
+        default="data/finetune",
+        metadata={"help": "Subset folder of the dataset to use."},
+    )
     dataset_subset_rl_train: Optional[int] = field(
-        default=10000, 
-        metadata={"help": "The size of the subset of the training data to use."})
+        default=10000,
+        metadata={"help": "The size of the subset of the training data to use."},
+    )
     adafactor: Optional[bool] = field(
-        default=False, 
-        metadata={"help": "whether to use the adafactor optimizer"})
-    top_k: Optional[float] = field(
-        default=0.0, metadata={"help": "Value for top_k"})
-    top_p: Optional[float] = field(
-        default=1.0, metadata={"help": "Value for top_p"})
+        default=False, metadata={"help": "whether to use the adafactor optimizer"}
+    )
+    top_k: Optional[float] = field(default=0.0, metadata={"help": "Value for top_k"})
+    top_p: Optional[float] = field(default=1.0, metadata={"help": "Value for top_p"})
     do_sample: Optional[bool] = field(
-        default=True, metadata={"help": "Flag for sampling"})
+        default=True, metadata={"help": "Flag for sampling"}
+    )
     eos_token_id: Optional[int] = field(
-        default=100_000, metadata={"help": "End of sentence token id"})
+        default=100_000, metadata={"help": "End of sentence token id"}
+    )
     output_max_length: Optional[int] = field(
-        default=128, 
-        metadata={"help": "maximum length for generation"})
+        default=128, metadata={"help": "maximum length for generation"}
+    )
     mini_batch_size: Optional[int] = field(
-        default=1, metadata={"help": "the PPO minibatch size"})
+        default=1, metadata={"help": "the PPO minibatch size"}
+    )
     ppo_batch_size: Optional[int] = field(
-        default=8, metadata={"help": "the PPO batch size"})
+        default=8, metadata={"help": "the PPO batch size"}
+    )
     ppo_epochs: Optional[int] = field(
-        default=4, metadata={"help": "the number of ppo epochs"})
+        default=4, metadata={"help": "the number of ppo epochs"}
+    )
     total_ppo_epochs: Optional[int] = field(
-        default=20000, metadata={"help": "number of total epochs"}) 
+        default=20000, metadata={"help": "number of total epochs"}
+    )
     ## TODO: differences between total_ppo_epochs and ppo_epochs
     early_stopping: Optional[bool] = field(
-        default=False, metadata={"help": "whether to early stop"})
-    target_kl: Optional[float] = field(default=0.1, metadata={"help": "kl target for early stopping"})
+        default=False, metadata={"help": "whether to early stop"}
+    )
+    target_kl: Optional[float] = field(
+        default=0.1, metadata={"help": "kl target for early stopping"}
+    )
     reward_baseline: Optional[float] = field(
         default=0.0,
         metadata={"help": "a baseline value that is subtracted from the reward"},
     )
     init_kl_coef: Optional[float] = field(
         default=0.2,
-        metadata={"help": "Initial KL penalty coefficient (used for adaptive and linear control)"},
+        metadata={
+            "help": "Initial KL penalty coefficient (used for adaptive and linear control)"
+        },
     )
     adap_kl_ctrl: Optional[bool] = field(
-        default=True, metadata={"help": "Use adaptive KL control, otherwise linear"})
+        default=True, metadata={"help": "Use adaptive KL control, otherwise linear"}
+    )
 
 
 ############################################################
 ## Step 1: Supervised Finetuning Trainer
 ############################################################
 
+
 class SFT(Trainer):
     """
     A class representing the supervised finetuning trainer.
@@ -322,6 +369,7 @@ class SFT(Trainer):
         model (AutoModelForCausalLM): The model to train.
         trainer (SFTTrainer): The trainer object used for training the model.
     """
+
     def __init__(self, rlhf_config: RLHFConfig):
         """
         Initializes the SFTTrainer object.
@@ -331,7 +379,9 @@ def __init__(self, rlhf_config: RLHFConfig):
         """
         self._rlhf_config = rlhf_config
         self.tokenizer = AutoTokenizer.from_pretrained(rlhf_config.base_model_path)
-        self.num_proc = self._rlhf_config.num_workers if not self._rlhf_config.streaming else None
+        self.num_proc = (
+            self._rlhf_config.num_workers if not self._rlhf_config.streaming else None
+        )
         self.dataset = self.create_datasets(self.tokenizer, self._rlhf_config)
         self.torch_dtype = torch.bfloat16 if self._rlhf_config.bf16 else torch.float16
         # self.torch_dtype = torch.bfloat16 if bf16 else (torch.float16 if fp16 else torch.float32)
@@ -357,9 +407,9 @@ def __init__(self, rlhf_config: RLHFConfig):
             ddp_find_unused_parameters=False,
         )
         self.model = AutoModelForCausalLM.from_pretrained(
-            self._rlhf_config.base_model_path, 
-            load_in_8bit=self._rlhf_config.load_in_8bit, 
-            device_map=self._rlhf_config.device_map
+            self._rlhf_config.base_model_path,
+            load_in_8bit=self._rlhf_config.load_in_8bit,
+            device_map=self._rlhf_config.device_map,
         )
         self.trainer = SFTTrainer(
             model=self.model,
@@ -370,25 +420,23 @@ def __init__(self, rlhf_config: RLHFConfig):
             packing=True,
         )
 
-
     def train(self):
         """
         Trains the model using the SFTTrainer object.
         """
         self.trainer.train()
 
-
     def load_lora(self, base_model_path=None, lora_model_path=None):
 
         if base_model_path is None:
             base_model_path = self._rlhf_config.base_model_path
 
-        ## Load lora config    
+        ## Load lora config
         if lora_model_path is None:
             lora_config = self.trainer.model.config
         else:
             lora_config = PeftConfig.from_pretrained(lora_model_path)
-        
+
         ## Load the base tokenizer and model
         tokenizer = AutoTokenizer.from_pretrained(base_model_path)
         if lora_config.task_type == "SEQ_CLS":
@@ -396,39 +444,37 @@ def load_lora(self, base_model_path=None, lora_model_path=None):
             base_model = AutoModelForSequenceClassification.from_pretrained(
                 base_model_path, num_labels=1, torch_dtype=self._rlhf_config.torch_dtype
             )
-        elif lora_config.task_type == "CAUSAL_LM": 
+        elif lora_config.task_type == "CAUSAL_LM":
             base_model = AutoModelForCausalLM.from_pretrained(
-                base_model_path, return_dict=True, torch_dtype=self._rlhf_config.torch_dtype
+                base_model_path,
+                return_dict=True,
+                torch_dtype=self._rlhf_config.torch_dtype,
             )
         else:
             raise ValueError("Invalid task_type in lora_config")
-        
+
         # Merge the base model and the Lora model
         model = PeftModel.from_pretrained(base_model, lora_config)
         return model, tokenizer
 
-
-    def save(self, output_path=None):    
+    def save(self, output_path=None):
 
         if output_path is None:
             output_path = os.path.join(
-                self._rlhf_config.output_dir, 
-                self._rlhf_config.sft_lora_path)
+                self._rlhf_config.output_dir, self._rlhf_config.sft_lora_path
+            )
         self.trainer.save_model(output_path)
 
-
     def train_and_save(self, output_path=None):
         self.trainer.train()
         self.save(output_path)
-        
-        
+
     def prepare_sample_text(self, example):
         """Prepare the text from a sample of the dataset."""
         text = f"Question: {example[self._rlhf_config.question_title]}\n\n\
             Answer: {example[self._rlhf_config.answer_title]}"
         return text
 
-
     def create_datasets(self, tokenizer, args):
         if args.dataset_type == "huggingface":
             dataset = load_dataset(
@@ -440,19 +486,24 @@ def create_datasets(self, tokenizer, args):
                 streaming=args.streaming,
             )
         elif args.dataset_type == "csv":
-            dataset = load_dataset('csv', data_files=args.dataset_name) 
+            dataset = load_dataset("csv", data_files=args.dataset_name)
         else:
-            raise FileNotFoundError(f"No (supported) data files or dataset script found {args.dataset_type}")
+            raise FileNotFoundError(
+                f"No (supported) data files or dataset script found {args.dataset_type}"
+            )
 
-        dataset = dataset[args.split] # Convert DatasetDict to Dataset
-        dataset = dataset.train_test_split(test_size=args.train_test_split_ratio, 
-                                           seed=args.seed)
-        print(f"Size of the train set: {len(dataset['train'])}. \
-              Size of the validation set: {len(dataset['test'])}")
+        dataset = dataset[args.split]  # Convert DatasetDict to Dataset
+        dataset = dataset.train_test_split(
+            test_size=args.train_test_split_ratio, seed=args.seed
+        )
+        print(
+            f"Size of the train set: {len(dataset['train'])}. \
+              Size of the validation set: {len(dataset['test'])}"
+        )
 
         train_dataset = ConstantLengthDataset(
             tokenizer,
-            dataset['train'],
+            dataset["train"],
             formatting_func=self.prepare_sample_text,
             infinite=True,
             seq_length=args.max_seq_length,
@@ -460,7 +511,7 @@ def create_datasets(self, tokenizer, args):
         )
         eval_dataset = ConstantLengthDataset(
             tokenizer,
-            dataset['test'],
+            dataset["test"],
             formatting_func=self.prepare_sample_text,
             infinite=False,
             seq_length=args.max_seq_length,
@@ -474,22 +525,22 @@ def create_datasets(self, tokenizer, args):
 ############################################################
 
 ## TODO: LOAD FROM THE DATABASE.PY FILE
-RANKING_CSV_HEADER_ID = 'ID'
-RANKING_CSV_HEADER_QUESTION = 'Question'
-RANKING_CSV_HEADER_UP_RANKING_ANSWER = 'Up Ranking Answer'
-RANKING_CSV_HEADER_LOW_RANKING_ANSWER = 'Low Ranking Answer'
+RANKING_CSV_HEADER_ID = "ID"
+RANKING_CSV_HEADER_QUESTION = "Question"
+RANKING_CSV_HEADER_UP_RANKING_ANSWER = "Up Ranking Answer"
+RANKING_CSV_HEADER_LOW_RANKING_ANSWER = "Low Ranking Answer"
 RANKING_CSV_HEADER = (
-    RANKING_CSV_HEADER_ID, 
-    RANKING_CSV_HEADER_QUESTION, 
-    RANKING_CSV_HEADER_UP_RANKING_ANSWER, 
-    RANKING_CSV_HEADER_LOW_RANKING_ANSWER
-    )
+    RANKING_CSV_HEADER_ID,
+    RANKING_CSV_HEADER_QUESTION,
+    RANKING_CSV_HEADER_UP_RANKING_ANSWER,
+    RANKING_CSV_HEADER_LOW_RANKING_ANSWER,
+)
 
 # We need to define a special data collator that batches the ranking data.
 @dataclass
 class RewardDataCollatorWithPadding:
     def __init__(self, tokenizer, max_length):
-        self.tokenizer=tokenizer
+        self.tokenizer = tokenizer
         self.max_length: Optional[int] = max_length
         self.padding = True
         self.pad_to_multiple_of: Optional[int] = None
@@ -497,15 +548,17 @@ def __init__(self, tokenizer, max_length):
 
     def __call__(self, features: List[Dict[str, Any]]) -> Dict[str, Any]:
         def extract_and_pad(key_ids, key_mask):
-            extracted_features = [{"input_ids": f[key_ids], 
-                                   "attention_mask": f[key_mask]} for f in features]
+            extracted_features = [
+                {"input_ids": f[key_ids], "attention_mask": f[key_mask]}
+                for f in features
+            ]
             return self.tokenizer.pad(
-                extracted_features, 
-                padding=self.padding, 
-                max_length=self.max_length, 
-                pad_to_multiple_of=self.pad_to_multiple_of, 
-                return_tensors=self.return_tensors
-                )
+                extracted_features,
+                padding=self.padding,
+                max_length=self.max_length,
+                pad_to_multiple_of=self.pad_to_multiple_of,
+                return_tensors=self.return_tensors,
+            )
 
         batch_x = extract_and_pad("input_ids_x", "attention_mask_x")
         batch_y = extract_and_pad("input_ids_y", "attention_mask_y")
@@ -518,7 +571,7 @@ def extract_and_pad(key_ids, key_mask):
             "return_loss": True,
         }
 
-    
+
 class RewardTrainer(Trainer):
     def __init__(self, rlhf_config: RLHFConfig):
         self._rlhf_config = rlhf_config
@@ -533,7 +586,7 @@ def __init__(self, rlhf_config: RLHFConfig):
             save_strategy=rlhf_config.evaluation_strategy,
             gradient_accumulation_steps=rlhf_config.gradient_accumulation_steps,
             gradient_checkpointing=rlhf_config.gradient_checkpointing,
-            # deepspeed=rlhf_config.deepspeed, 
+            # deepspeed=rlhf_config.deepspeed,
             local_rank=rlhf_config.local_rank,
             remove_unused_columns=rlhf_config.remove_unused_columns,
             label_names=rlhf_config.label_names,
@@ -546,62 +599,76 @@ def __init__(self, rlhf_config: RLHFConfig):
         self.torch_dtype = torch.bfloat16 if rlhf_config.bf16 else torch.float16
         # self.torch_dtype = torch.bfloat16 if bf16 else (torch.float16 if fp16 else torch.float32)
 
-        ## Load the tokenizer and the model 
+        ## Load the tokenizer and the model
         self.tokenizer = AutoTokenizer.from_pretrained(rlhf_config.reward_model_path)
         self.tokenizer.pad_token = self.tokenizer.eos_token
-        
+
         self.base_model = AutoModelForSequenceClassification.from_pretrained(
-            rlhf_config.reward_model_path, 
+            rlhf_config.reward_model_path,
             num_labels=1,
             torch_dtype=self.torch_dtype,
             load_in_8bit=rlhf_config.load_in_8bit,
-            device_map=rlhf_config.device_map
+            device_map=rlhf_config.device_map,
         )
         self.model = get_peft_model(self.base_model, rlhf_config.lora_config_reward)
         self.model.print_trainable_parameters()
         self.model.config.pad_token_id = self.tokenizer.eos_token_id
         self.model.config.use_cache = not rlhf_config.gradient_checkpointing
-        self.num_proc = self._rlhf_config.num_workers if not self._rlhf_config.streaming else None
+        self.num_proc = (
+            self._rlhf_config.num_workers if not self._rlhf_config.streaming else None
+        )
 
         self.dataset = self.create_datasets()
 
         self.compute_metrics = self._compute_metrics
-        self.data_collator=RewardDataCollatorWithPadding(
-                tokenizer=self.tokenizer, 
-                max_length=self._rlhf_config.max_seq_length_reward)
+        self.data_collator = RewardDataCollatorWithPadding(
+            tokenizer=self.tokenizer, max_length=self._rlhf_config.max_seq_length_reward
+        )
         super().__init__(
             model=self.model,
             args=self.args,
             data_collator=self.data_collator,
-            train_dataset=self.dataset['train'],
-            eval_dataset=self.dataset['eval'],
+            train_dataset=self.dataset["train"],
+            eval_dataset=self.dataset["eval"],
             tokenizer=self.tokenizer,
             compute_metrics=self.compute_metrics,
         )
-        
+
     def _preprocess_function(self, examples):
         """
-        Turn the dataset into pairs of question and answer, where 
-        "text_x = question + preferred answer" and "text_y = question + not preferred answer". 
+        Turn the dataset into pairs of question and answer, where
+        "text_x = question + preferred answer" and "text_y = question + not preferred answer".
         Then tokenize the dataset.
 
         Returns:
             A dictionary containing the processed data.
         """
+
         def tokenize_and_store(question, answer, key_ids, key_mask):
-            tokenized = self.tokenizer(f"Question: {question}\n\nAnswer: {answer}", truncation=True)
+            tokenized = self.tokenizer(
+                f"Question: {question}\n\nAnswer: {answer}", truncation=True
+            )
             new_examples[key_ids].append(tokenized["input_ids"])
             new_examples[key_mask].append(tokenized["attention_mask"])
 
-        new_examples = {"input_ids_x": [], "attention_mask_x": [], 
-                        "input_ids_y": [], "attention_mask_y": []}
-        
+        new_examples = {
+            "input_ids_x": [],
+            "attention_mask_x": [],
+            "input_ids_y": [],
+            "attention_mask_y": [],
+        }
+
         for question, better_answer, worse_answer in zip(
-            examples[RANKING_CSV_HEADER_QUESTION], 
-            examples[RANKING_CSV_HEADER_UP_RANKING_ANSWER], 
-            examples[RANKING_CSV_HEADER_LOW_RANKING_ANSWER]):
-            tokenize_and_store(question, better_answer, "input_ids_x", "attention_mask_x")
-            tokenize_and_store(question, worse_answer, "input_ids_y", "attention_mask_y")
+            examples[RANKING_CSV_HEADER_QUESTION],
+            examples[RANKING_CSV_HEADER_UP_RANKING_ANSWER],
+            examples[RANKING_CSV_HEADER_LOW_RANKING_ANSWER],
+        ):
+            tokenize_and_store(
+                question, better_answer, "input_ids_x", "attention_mask_x"
+            )
+            tokenize_and_store(
+                question, worse_answer, "input_ids_y", "attention_mask_y"
+            )
 
         return new_examples
 
@@ -623,9 +690,11 @@ def create_datasets(self):
                 streaming=self._rlhf_config.streaming,
             )
         elif self._rlhf_config.dataset_type == "csv":
-            dataset = load_dataset('csv', data_files=self._rlhf_config.dataset_name) 
+            dataset = load_dataset("csv", data_files=self._rlhf_config.dataset_name)
         else:
-            raise FileNotFoundError(f"No (supported) data files or dataset script found {self._rlhf_config.dataset_type}")
+            raise FileNotFoundError(
+                f"No (supported) data files or dataset script found {self._rlhf_config.dataset_type}"
+            )
 
         # Preprocess the dataset and filter out QAs that are longer than max_length
         dataset = dataset.map(
@@ -635,33 +704,42 @@ def create_datasets(self):
         )
         dataset = dataset.filter(
             lambda x: len(x["input_ids_x"]) <= self._rlhf_config.max_seq_length_reward
-                        and len(x["input_ids_y"]) <= self._rlhf_config.max_seq_length_reward
+            and len(x["input_ids_y"]) <= self._rlhf_config.max_seq_length_reward
         )
 
-        dataset = dataset[self._rlhf_config.split] # Convert DatasetDict to Dataset
+        dataset = dataset[self._rlhf_config.split]  # Convert DatasetDict to Dataset
         ## load desired amount of data
         if self._rlhf_config.reward_num_of_data > 0:
-            dataset = dataset.select(range(min(self._rlhf_config.reward_num_of_data, len(dataset))))
+            dataset = dataset.select(
+                range(min(self._rlhf_config.reward_num_of_data, len(dataset)))
+            )
 
         ## split to train and test datasets
-        dataset = dataset.train_test_split(test_size=self._rlhf_config.train_test_split_ratio, 
-                                            seed=self._rlhf_config.seed)
-        print(f"Size of the train set: {len(dataset['train'])}. \
-                Size of the validation set: {len(dataset['test'])}")
+        dataset = dataset.train_test_split(
+            test_size=self._rlhf_config.train_test_split_ratio,
+            seed=self._rlhf_config.seed,
+        )
+        print(
+            f"Size of the train set: {len(dataset['train'])}. \
+                Size of the validation set: {len(dataset['test'])}"
+        )
 
-        return {"train": dataset['train'], "eval": dataset['test']}
-    
+        return {"train": dataset["train"], "eval": dataset["test"]}
 
-    # Define how to compute the reward loss. We use the InstructGPT pairwise logloss: 
+    # Define how to compute the reward loss. We use the InstructGPT pairwise logloss:
     # https://arxiv.org/abs/2203.02155
     def compute_loss(self, model, inputs, return_outputs=False):
-        rewards_x = model(input_ids=inputs["input_ids_x"], attention_mask=inputs["attention_mask_x"])[0]
-        rewards_y = model(input_ids=inputs["input_ids_y"], attention_mask=inputs["attention_mask_y"])[0]
+        rewards_x = model(
+            input_ids=inputs["input_ids_x"], attention_mask=inputs["attention_mask_x"]
+        )[0]
+        rewards_y = model(
+            input_ids=inputs["input_ids_y"], attention_mask=inputs["attention_mask_y"]
+        )[0]
         loss = -torch.nn.functional.logsigmoid(rewards_x - rewards_y).mean()
         if return_outputs:
             return loss, {"rewards_x": rewards_x, "rewards_y": rewards_y}
         return loss
-    
+
     def _compute_metrics(self, eval_pred):
         """
         Compute the accuracy of the model.
@@ -680,8 +758,7 @@ def _compute_metrics(self, eval_pred):
         accuracy = evaluate.load("accuracy")
         return accuracy.compute(predictions=predictions, references=labels)
 
-
-    def save(self, output_path=None):   
+    def save(self, output_path=None):
         """
         Save the model.
 
@@ -691,10 +768,9 @@ def save(self, output_path=None):
         """
         if output_path is None:
             output_path = os.path.join(
-                self._rlhf_config.output_dir, 
-                self._rlhf_config.reward_merged_path)
-        self.save_model(output_path) 
-
+                self._rlhf_config.output_dir, self._rlhf_config.reward_merged_path
+            )
+        self.save_model(output_path)
 
     def train_and_save(self, output_path=None):
         """
diff --git a/tests/component/test_base.py b/tests/component/test_base.py
index 07e1017..2f2f234 100644
--- a/tests/component/test_base.py
+++ b/tests/component/test_base.py
@@ -1,6 +1,7 @@
 """Tests for the base module."""
 import unittest
-from plotano.component.base import DataSource, Component
+
+from plotano.component.base import Component, DataSource
 
 
 class TestDataSource(unittest.TestCase):
@@ -26,6 +27,7 @@ def test_init(self):
         This method creates a DataSource instance with a specific id and a fetch function.
         It then asserts that the id and the data fetched by the fetch function are as expected.
         """
+
         def fetch_func():
             return "data"
 
@@ -59,6 +61,7 @@ def test_init(self):
         and properties. It then asserts that the id, the data fetched by the fetch function, the svelte
         component, and the properties are as expected.
         """
+
         def fetch_func():
             return "data"
 
diff --git a/tests/db/test_qa_database.py b/tests/db/test_qa_database.py
index 3cb3f1e..827fe0d 100644
--- a/tests/db/test_qa_database.py
+++ b/tests/db/test_qa_database.py
@@ -1,8 +1,8 @@
 """Test the QuestionAnswerDatabase class"""
 import datetime
-import unittest
 import os
 import sqlite3
+import unittest
 
 from plotano.db.qa_database import QuestionAnswerDatabase
 
@@ -14,6 +14,7 @@ class TestQuestionAnswerDatabase(unittest.TestCase):
     """
     Test the QuestionAnswerDatabase class.
     """
+
     def setUp(self):
         # Create a temporary database for testing
         self.qadb = QuestionAnswerDatabase(db_file=TEST_DB_FILE, debug=True)
@@ -31,7 +32,9 @@ def test_create_table(self):
         conn = sqlite3.connect(TEST_DB_FILE)
         cursor = conn.cursor()
 
-        cursor.execute("SELECT name FROM sqlite_master WHERE type='table' AND name='question_answer'")
+        cursor.execute(
+            "SELECT name FROM sqlite_master WHERE type='table' AND name='question_answer'"
+        )
         table_exists = cursor.fetchone()
 
         self.assertTrue(table_exists)
@@ -111,10 +114,12 @@ def test_save_to_csv(self):
         self.assertEqual(lines[0].strip(), "ID,Question,Answer,Vote Status,Timestamp")
         self.assertEqual(
             lines[1].strip()[:-timestamp_trim],
-            f"1,{question1},{answer1},n/a,{timestamp}"[:-timestamp_trim])  # Default vote status
+            f"1,{question1},{answer1},n/a,{timestamp}"[:-timestamp_trim],
+        )  # Default vote status
         self.assertEqual(
             lines[2].strip()[:-timestamp_trim],
-            f"2,{question2},{answer2},n/a,{timestamp}"[:-timestamp_trim])  # Default vote status
+            f"2,{question2},{answer2},n/a,{timestamp}"[:-timestamp_trim],
+        )  # Default vote status
 
         # Clean up
         os.remove("test_csv_file.csv")
diff --git a/tests/db/test_ranking_database.py b/tests/db/test_ranking_database.py
index 5be148f..0ca24f6 100644
--- a/tests/db/test_ranking_database.py
+++ b/tests/db/test_ranking_database.py
@@ -1,9 +1,10 @@
 """
 Test the RankingDatabase class
 """
-import unittest
 import os
 import sqlite3
+import unittest
+
 from plotano.db.ranking_database import RankingDatabase
 
 # Define a temporary database file for testing
@@ -32,7 +33,8 @@ def test_create_table(self):
         cursor = conn.cursor()
 
         cursor.execute(
-            "SELECT name FROM sqlite_master WHERE type='table' AND name='ranking'")
+            "SELECT name FROM sqlite_master WHERE type='table' AND name='ranking'"
+        )
         table_exists = cursor.fetchone()
 
         self.assertTrue(table_exists)
@@ -51,7 +53,8 @@ def test_insert_and_retrieve_ranking(self):
 
         # Insert data and get the ID
         ranking_id = self.ranking_db.insert_ranking(
-            question, up_ranking_answer, low_ranking_answer)
+            question, up_ranking_answer, low_ranking_answer
+        )
 
         # Retrieve the data
         rows = self.ranking_db.retrieve_all_question_answers()
@@ -76,9 +79,11 @@ def test_save_to_csv(self):
 
         # Insert data
         self.ranking_db.insert_ranking(
-            question1, up_ranking_answer1, low_ranking_answer1)
+            question1, up_ranking_answer1, low_ranking_answer1
+        )
         self.ranking_db.insert_ranking(
-            question2, up_ranking_answer2, low_ranking_answer2)
+            question2, up_ranking_answer2, low_ranking_answer2
+        )
 
         # Save to CSV
         self.ranking_db.save_to_csv("test_csv_file.csv")
@@ -92,11 +97,16 @@ def test_save_to_csv(self):
         # Verify the CSV file content
         self.assertEqual(len(lines), 3)  # Header + 2 rows
         self.assertEqual(
-            lines[0].strip(), "ID,Question,Up Ranking Answer,Low Ranking Answer")
+            lines[0].strip(), "ID,Question,Up Ranking Answer,Low Ranking Answer"
+        )
         self.assertEqual(
-            lines[1].strip(), f"1,{question1},{up_ranking_answer1},{low_ranking_answer1}")
+            lines[1].strip(),
+            f"1,{question1},{up_ranking_answer1},{low_ranking_answer1}",
+        )
         self.assertEqual(
-            lines[2].strip(), f"2,{question2},{up_ranking_answer2},{low_ranking_answer2}")
+            lines[2].strip(),
+            f"2,{question2},{up_ranking_answer2},{low_ranking_answer2}",
+        )
 
         # Clean up
         os.remove("test_csv_file.csv")
diff --git a/tests/llm/test_abs_llm.py b/tests/llm/test_abs_llm.py
index 24f1c5f..02788ba 100644
--- a/tests/llm/test_abs_llm.py
+++ b/tests/llm/test_abs_llm.py
@@ -2,6 +2,7 @@
 Test the AbsLlm class.
 """
 import unittest
+
 from plotano.llm.abs_llm import AbsLlm
 
 
diff --git a/tests/llm/test_constants.py b/tests/llm/test_constants.py
index c87a074..1194b89 100644
--- a/tests/llm/test_constants.py
+++ b/tests/llm/test_constants.py
@@ -2,6 +2,7 @@
 Test the constants of the LLM module.
 """
 import unittest
+
 from plotano.llm.constants import LlmName
 
 
diff --git a/tests/llm/test_huggingface.py b/tests/llm/test_huggingface.py
index 1703d69..91aa361 100644
--- a/tests/llm/test_huggingface.py
+++ b/tests/llm/test_huggingface.py
@@ -2,7 +2,8 @@
 Test the HuggingfaceModel class.
 """
 import unittest
-from unittest.mock import patch, MagicMock
+from unittest.mock import MagicMock, patch
+
 from plotano.llm.huggingface import HuggingfaceModel
 
 
@@ -29,10 +30,16 @@ def test_init(self, mock_tokenizer, mock_model):
 
         # Check if the model and tokenizer were initialized correctly
         mock_model.from_pretrained.assert_called_once_with(
-            pretrained_model_name_or_path, trust_remote_code=True, load_in_8bit=True, device_map="auto"
+            pretrained_model_name_or_path,
+            trust_remote_code=True,
+            load_in_8bit=True,
+            device_map="auto",
         )
         mock_tokenizer.from_pretrained.assert_called_once_with(
-            pretrained_model_name_or_path, trust_remote_code=True, load_in_8bit=True, device_map="auto"
+            pretrained_model_name_or_path,
+            trust_remote_code=True,
+            load_in_8bit=True,
+            device_map="auto",
         )
         self.assertEqual(huggingface_model._model, model_instance_mock)
         self.assertEqual(huggingface_model._tokenizer, tokenizer_instance_mock)
@@ -61,9 +68,13 @@ def test_predict(self):
         result = huggingface_model.predict(input_message)
 
         # Check if the tokenizer and model methods were called with the correct arguments
-        mock_tokenizer.encode.assert_called_once_with(input_message, return_tensors="pt")
+        mock_tokenizer.encode.assert_called_once_with(
+            input_message, return_tensors="pt"
+        )
         mock_model.generate.assert_called_once_with([1, 2, 3, 4, 5], max_length=100)
-        mock_tokenizer.decode.assert_called_once_with([6, 7, 8, 9, 10], skip_special_tokens=True)
+        mock_tokenizer.decode.assert_called_once_with(
+            [6, 7, 8, 9, 10], skip_special_tokens=True
+        )
 
         self.assertEqual(result, output_message)
 
diff --git a/tests/llm/test_model_factory.py b/tests/llm/test_model_factory.py
index 04b019a..a8d4f56 100644
--- a/tests/llm/test_model_factory.py
+++ b/tests/llm/test_model_factory.py
@@ -3,8 +3,9 @@
 """
 
 import unittest
-from unittest.mock import patch, MagicMock
-from plotano.llm.model_factory import ModelFactory, LlmName
+from unittest.mock import MagicMock, patch
+
+from plotano.llm.model_factory import LlmName, ModelFactory
 
 
 class TestModelFactory(unittest.TestCase):
@@ -41,7 +42,9 @@ def test_create_model_huggingface(self):
         huggingface_model_mock = MagicMock(return_value=mock_huggingface_model)
 
         # Patch the HuggingfaceModel class to use the mocked version
-        with patch("plotano.llm.model_factory.HuggingfaceModel", huggingface_model_mock):
+        with patch(
+            "plotano.llm.model_factory.HuggingfaceModel", huggingface_model_mock
+        ):
             result = ModelFactory.create_model(model_name)
 
         # Check if the HuggingfaceModel class was called with the correct arguments
diff --git a/tests/llm/test_openai.py b/tests/llm/test_openai.py
index 56d1de5..98bb121 100644
--- a/tests/llm/test_openai.py
+++ b/tests/llm/test_openai.py
@@ -2,7 +2,8 @@
 Test the OpenAIModel class
 """
 import unittest
-from unittest.mock import patch, MagicMock
+from unittest.mock import MagicMock, patch
+
 from plotano.llm.openai import OpenAIModel
 
 
@@ -26,8 +27,15 @@ def test_predict(self):
         openai_completion_create_mock = MagicMock(return_value=mock_response)
 
         # Patch the OpenAI.Completion.create method to use the mocked version
-        with patch("plotano.llm.openai.openai.Completion.create", openai_completion_create_mock):
-            openai_model = OpenAIModel(api_key="fake_api_key", engine="davinci", max_tokens=100, temperature=0.5)
+        with patch(
+            "plotano.llm.openai.openai.Completion.create", openai_completion_create_mock
+        ):
+            openai_model = OpenAIModel(
+                api_key="fake_api_key",
+                engine="davinci",
+                max_tokens=100,
+                temperature=0.5,
+            )
             result = openai_model.predict(message, 1)
 
         # Check if the OpenAI.Completion.create method was called with the correct arguments
diff --git a/tests/rlhf/test_rlhf.py b/tests/rlhf/test_rlhf.py
index 40b936c..bab81f6 100644
--- a/tests/rlhf/test_rlhf.py
+++ b/tests/rlhf/test_rlhf.py
@@ -1,11 +1,12 @@
-import unittest
-import os
 import json
+import os
+import unittest
 from dataclasses import asdict
+
 from plotano.rlhf.rlhf import RLHFConfig, read_json_file
 
-class TestRLHFConfig(unittest.TestCase):
 
+class TestRLHFConfig(unittest.TestCase):
     def test_read_json_file_valid(self):
         """
         Test reading a valid json file.
@@ -32,7 +33,7 @@ def test_RLHFConfig_default(self):
         """
         config = RLHFConfig()
         config_dict = asdict(config)
-        
+
         # Check a subset of the fields to verify they are correctly initialized
         self.assertEqual(config_dict["base_model_path"], "meta-llama/Llama-2-7b-hf")
         self.assertEqual(config_dict["dataset_type"], "csv")
@@ -53,7 +54,7 @@ def test_RLHFConfig_custom(self):
         }
         config = RLHFConfig(**custom_values)
         config_dict = asdict(config)
-        
+
         # Check that the fields are correctly initialized with the custom values
         self.assertEqual(config_dict["base_model_path"], "custom_model")
         self.assertEqual(config_dict["dataset_type"], "huggingface")
@@ -61,5 +62,6 @@ def test_RLHFConfig_custom(self):
         self.assertEqual(config_dict["shuffle_buffer"], 6000)
         # Add checks for the rest of the fields as needed
 
+
 if __name__ == "__main__":
     unittest.main()
diff --git a/tests/test_application.py b/tests/test_application.py
index cadab35..a679f8a 100644
--- a/tests/test_application.py
+++ b/tests/test_application.py
@@ -2,9 +2,10 @@
 Test the Application class.
 """
 
-import unittest
 import os
+import unittest
 from unittest.mock import MagicMock, patch
+
 from plotano.application import Application
 
 
@@ -12,6 +13,7 @@ class TestApplication(unittest.TestCase):
     """
     Unit test class for the Application class.
     """
+
     def setUp(self):
         self.app = Application(share=False, debug=False)
 
@@ -36,8 +38,13 @@ def test_run(self, send_from_directory_mock, CORS_mock, Flask_mock):
         """
         Tests running the application.
         """
-        self.app.data_sources = {"data_source1": MagicMock(), "data_source2": MagicMock()}
-        self.app.components = [{"id": "test_id", "component": MagicMock(), "svelte_component": "Chatbot"}]
+        self.app.data_sources = {
+            "data_source1": MagicMock(),
+            "data_source2": MagicMock(),
+        }
+        self.app.components = [
+            {"id": "test_id", "component": MagicMock(), "svelte_component": "Chatbot"}
+        ]
 
         self.app.run()
 
@@ -49,18 +56,30 @@ def test_run(self, send_from_directory_mock, CORS_mock, Flask_mock):
         self.assertEqual(len(Flask_mock.return_value.route.call_args_list), 11)
 
         # Check if data routes are created correctly
-        Flask_mock.return_value.route.assert_any_call("/data/data_source1", methods=["GET"], endpoint="data_source1")
-        Flask_mock.return_value.route.assert_any_call("/data/data_source2", methods=["GET"], endpoint="data_source2")
+        Flask_mock.return_value.route.assert_any_call(
+            "/data/data_source1", methods=["GET"], endpoint="data_source1"
+        )
+        Flask_mock.return_value.route.assert_any_call(
+            "/data/data_source2", methods=["GET"], endpoint="data_source2"
+        )
 
         # Check if chatbot routes are created correctly
-        Flask_mock.return_value.route.assert_any_call("/chat/<message>", methods=["POST"])
-        Flask_mock.return_value.route.assert_any_call("/chat/qa_table/update", methods=["POST"])
-        Flask_mock.return_value.route.assert_any_call("/chat/qa_table/close", methods=["GET"])
+        Flask_mock.return_value.route.assert_any_call(
+            "/chat/<message>", methods=["POST"]
+        )
+        Flask_mock.return_value.route.assert_any_call(
+            "/chat/qa_table/update", methods=["POST"]
+        )
+        Flask_mock.return_value.route.assert_any_call(
+            "/chat/qa_table/close", methods=["GET"]
+        )
 
         # Check if the base and home routes are created correctly
         if os.path.exists("frontend/dist"):
             Flask_mock.return_value.route.assert_any_call("/", methods=["GET"])
-            Flask_mock.return_value.route.assert_any_call("/<path:path>", methods=["GET"])
+            Flask_mock.return_value.route.assert_any_call(
+                "/<path:path>", methods=["GET"]
+            )
 
         # Check if the app.run() method is called with the correct arguments
         app_run_args = Flask_mock.return_value.run.call_args[1]
diff --git a/tests/test_state.py b/tests/test_state.py
index 777e916..a895def 100644
--- a/tests/test_state.py
+++ b/tests/test_state.py
@@ -2,6 +2,7 @@
 Tests for the State class.
 """
 import unittest
+
 from plotano.state import State, Store
 
 
@@ -9,6 +10,7 @@ class TestState(unittest.TestCase):
     """
     Unit test class for the State class.
     """
+
     def test_get_attribute(self):
         """
         Tests getting an attribute from the state.
@@ -51,6 +53,7 @@ class TestStore(unittest.TestCase):
     """
     Unit test class for the Store class.
     """
+
     def test_increment(self):
         """
         Tests incrementing the count.