add streamlit app for better visualization

apps/datascience_assistant/README.md

@@ -0,0 +1,26 @@
+# Data Science Assistant with Streamlit ⭐
+Data Science Assistant (hereinafter referred to as DS Assistant) is a Data Science Assistant developed based on the modelscope-agent framework, which can automatically perform exploratory Data analysis (EDA) in Data Science tasks according to user needs, Data preprocessing, feature engineering, model training, model evaluation and other steps are fully automated.
+
+Detailed information can be found in the [documentation](../../docs/source/agents/data_science_assistant.md).
+
+## Quick Start
+Streamlit is a Python library that makes it easy to create and share beautiful, custom web apps for machine learning and data science.
+
+To run the DS Assistant in streamlit, you need to install the Streamlit library. You can install it using pip:
+```bash
+pip install streamlit streamlit-jupyter
+```
+Then, you need to set
+Then, you can run the DS Assistant using the following command:
+```bash
+streamlit run app.py
+```
+
+After running the command, a new tab will open in your default web browser with the DS Assistant running.
+The following are screenshots of the DS Assistant running in the browser:
+
+![img_2.png](../../resources/data_science_assistant_streamlit_1.png)
+you can view all of the codes and in streamlit
+![img_3.png](../../resources/data_science_assistant_streamlit_2.png)
+After you have finished using the DS Assistant, you can directly convert the running process to a pdf
+![img_5.png](../../resources/data_science_assistant_streamlit_3.png)

apps/datascience_assistant/app.py

@@ -0,0 +1,23 @@
+import os
+
+import streamlit as st
+from modelscope_agent.agents.data_science_assistant import DataScienceAssistant
+from modelscope_agent.tools.metagpt_tools.tool_recommend import \
+ TypeMatchToolRecommender
+
+llm_config = {
+ 'model': 'qwen2-72b-instruct',
+ 'model_server': 'dashscope',
+}
+os.environ['DASHSCOPE_API_KEY'] = input(
+ 'Please input your dashscope api key: ')
+data_science_assistant = DataScienceAssistant(
+ llm=llm_config, tool_recommender=TypeMatchToolRecommender(tools=['<all>']))
+st.title('Data Science Assistant')
+st.write(
+ 'This is a data science assistant that can help you with your data science tasks.'
+)
+st.write('Please input your request below and click the submit button.')
+user_request = st.text_input('User Request')
+if st.button('submit'):
+ data_science_assistant.run(user_request=user_request, streamlit=True)

modelscope_agent/agents/data_science_assistant.py

@@ -1,5 +1,6 @@
 # Implementation inspired by the paper "DATA INTERPRETER: AN LLM AGENT FOR DATA SCIENCE"
 import asyncio
+import copy
 import os
 import time
 from datetime import datetime
@@ -8,7 +9,6 @@
 import json
 import json5
 import nbformat
-import streamlit as st
 from modelscope_agent.agents.role_play import RolePlay
 from modelscope_agent.llm.base import BaseChatModel
 from modelscope_agent.schemas import CodeCell, Plan, Task
@@ -18,14 +18,13 @@
 from modelscope_agent.tools.metagpt_tools.tool_recommend import ToolRecommender
 from modelscope_agent.utils.logger import agent_logger as logger
 from modelscope_agent.utils.utils import parse_code
-from streamlit_agraph import Config, Edge, Node, agraph
 
 try:
  import streamlit as st # noqa
+ from nbconvert import HTMLExporter
+ from traitlets.config import Config
 except Exception as e:
- print(
- f'import streamlit error: {str(e)}, please install streamlit first by running: pip install streamlit '
- )
+ print(f'import error: {str(e)}, please install streamlit and nbconvert')
 PLAN_TEMPLATE = """
 # Context:
 {context}
@@ -235,14 +234,12 @@
 these are the previous code blocks, which have been executed successfully in the previous jupyter notebook code blocks \
 {previous_code_blocks}
 
-Attention: your response should be one of the following:
-- [your step by step thought], correct
-- [your step by step thought], incorrect
-
+at the end of your thought, you need to give the final judgement with a new line( correct or incorrect).
 don't generate code , just give the reason why the code is correct or incorrect.
 
 ## Attention
 don't use the word 'incorrect' in your step by step thought.
+your answer should be short and clear, don't need to be too long.
 """
 
 CHECK_DATA_PROMPT = """
@@ -322,33 +319,6 @@ def __init__(self,
  self.total_token = 0
  self.streamlit = False
 
- def create_agraph_from_json(tasks):
- # 解析 JSON 字符串
- # 初始化节点和边的列表
- nodes = []
- edges = []
-
- # 为每个任务创建节点
- for task in tasks:
- task_id = task['task_id']
- nodes.append(
- Node(
- id=task_id, label='TASK ' + task_id, size=50, shape='box'))
-
- # 为每个依赖任务创建边
- for dependent_task_id in task['dependent_task_ids']:
- edges.append(Edge(
- source=dependent_task_id,
- target=task_id,
- ))
-
- # 配置图形
- config = Config(
- width=750, height=950, directed=True, hierarchical=False)
-
- # 返回 agraph 对象
- return agraph(nodes=nodes, edges=edges, config=config)
-
  def _update_plan(self, user_request: str, curr_plan: Plan = None) -> Plan:
  call_llm_success = False
  call_llm_count = 0
@@ -599,8 +569,6 @@ def _check_data(self):
 
  def _judge_code(self, task, previous_code_blocks, code,
  code_interpreter_resp):
- success = True
- failed_reason = ''
  judge_prompt = JUDGE_TEMPLATE.format(
  instruction=task.instruction,
  previous_code_blocks=previous_code_blocks,
@@ -621,13 +589,12 @@ def _judge_code(self, task, previous_code_blocks, code,
  self._get_total_tokens()
  if 'Error code' in judge_result:
  call_llm_count += 1
- time.sleep(10)
+ time.sleep(5)
  else:
  call_llm_success = True
  if not call_llm_success:
  raise Exception('call llm failed')
  logger.info(f'judge result for task{task.task_id}: \n {judge_result}')
-
  if 'incorrect' in judge_result.split('\n')[-1]:
  success = False
  failed_reason = (
@@ -636,7 +603,7 @@ def _judge_code(self, task, previous_code_blocks, code,
  return success, failed_reason
 
  else:
- return True, 'The code logic is correct'
+ return True, judge_result
 
  def _run(self, user_request, save: bool = True, **kwargs):
  before_time = time.time()
@@ -645,7 +612,7 @@ def _run(self, user_request, save: bool = True, **kwargs):
  if self.streamlit:
  st.write("""# DataScience Assistant """)
  st.write("""### The user request is: \n""")
- st.write("""{user_request}""")
+ st.write(user_request)
  print('streamlit: ', self.streamlit)
  self.plan = self._update_plan(user_request=user_request)
  jupyter_file_path = ''
@@ -659,7 +626,9 @@ def _run(self, user_request, save: bool = True, **kwargs):
 
  while self.plan.current_task_id:
  task = self.plan.task_map.get(self.plan.current_task_id)
- # write_and_execute_code(self)
+ if self.streamlit:
+ st.write(
+ f"""### Task {task.task_id}: {task.instruction}\n""")
  logger.info(
  f'new task starts: task_{task.task_id} , instruction: {task.instruction}'
  )
@@ -671,7 +640,6 @@ def _run(self, user_request, save: bool = True, **kwargs):
  code_execute_success = False
  code_logic_success = False
  temp_code_interpreter = CodeInterpreter()
-
  temp_code_interpreter.call(
  params=json.dumps({
  'code':
@@ -682,26 +650,60 @@ def _run(self, user_request, save: bool = True, **kwargs):
  # generate code
  code = self._generate_code(code_counter, task,
  user_request)
+ code = '%matplotlib inline \n' + code
+ # if self.streamlit:
+ # st.divider()
+ # st.write("We have generated the code for the current task")
+ # st.code(code, language='python')
  code_execute_success, code_interpreter_resp = temp_code_interpreter.call(
  params=json.dumps({'code': code}),
  nb_mode=True,
  silent_mode=True)
- # 删除临时 jupyter环境
- temp_code_interpreter.terminate()
+ if self.streamlit:
+ st.divider()
+ st_notebook = nbformat.v4.new_notebook()
+ st_notebook.cells = [
+ temp_code_interpreter.nb.cells[-1]
+ ]
+ c = Config()
+ c.HTMLExporter.preprocessors = [
+ 'nbconvert.preprocessors.ConvertFiguresPreprocessor'
+ ]
+ # create the new exporter using the custom config
+ html_exporter_with_figs = HTMLExporter(config=c)
+ (html, resources_with_fig
+ ) = html_exporter_with_figs.from_notebook_node(
+ st_notebook)
+ st.write(
+ 'We have generated the code for the current task')
+ st.html(html)
  judge_resp = ''
  if not code_execute_success:
  logger.error(
  f'code execution failed, task{task.task_id} code_counter{code_counter}:\n '
  f'{code_interpreter_resp}')
+ if self.streamlit:
+ st.write(
+ 'The code execution failed. Now we will take a reflection and regenerate the code.'
+ )
  else:
  logger.info(
  f'code execution success, task{task.task_id} code_counter{code_counter}:\n '
  f'{code_interpreter_resp}')
+ if self.streamlit:
+ st.write(
+ 'The code execution is successful. Now we will ask the judge to check the code.'
+ )
  code_logic_success, judge_resp = self._judge_code(
  task=task,
  previous_code_blocks=previous_code_blocks,
  code=code,
  code_interpreter_resp=code_interpreter_resp)
+ if self.streamlit:
+ st.write(
+ 'The judge has checked the code, here is the result.'
+ )
+ st.write(judge_resp)
  success = code_execute_success and code_logic_success
  task.code_cells.append(
  CodeCell(
@@ -713,16 +715,13 @@ def _run(self, user_request, save: bool = True, **kwargs):
  self.code_interpreter.call(
  params=json.dumps({'code': code}), nb_mode=True)
  if self.streamlit:
- st.divider()
  st.write(
- f"""### Task {task.task_id}: {task.instruction}\n"""
+ 'The code is correct, we will move to the next task.'
  )
- st.write(
- 'Now we generate the code for the current task'
- )
- st.code(f"""{code}""", language='python')
  task.code = code
  task.result = code_interpreter_resp
+ else:
+ self.code_interpreter.nb.cells.pop()
  code_counter += 1
 
  # save the successful code in jupyter notebook
@@ -759,12 +758,10 @@ def _run(self, user_request, save: bool = True, **kwargs):
  print(f'json write error: {str(e)}')
  if self.streamlit:
  st.divider()
- st.write('#### We have finished all the tasks! ')
- st.write(
- f'you can check the details in the jupyter notebook \"{jupyter_file_path}\"'
- )
+ st.write('### We have finished all the tasks! ')
+ st.balloons()
  st.write(
- f"you can check the plan in the json file \"{dir_name + 'plan.json'}\""
+ f"""#### The total time cost is: {time_cost}\n #### The total token cost is: {total_token}"""
  )
 
  except Exception as e:

modelscope_agent/tools/metagpt_tools/task_type.py

@@ -54,6 +54,13 @@
 - Ensure that the evaluated data is same processed as the training data.
 - Use trained model from previous task result directly, do not mock or reload model yourself.
 """
+OCR_PROMPT = """
+The current task is about OCR, please note the following:
+- you can follow the following code to get the OCR result:
+from paddleocr import PaddleOCR
+ocr = PaddleOCR(use_angle_cls=True, lang='en')
+result = ocr.ocr('/path/to/the/pic', cls=True) # please replace the path with the real path
+"""
 
 
 class TaskTypeDef(BaseModel):
@@ -92,7 +99,8 @@ class TaskType(Enum):
  desc='Only for evaluating model.',
  guidance=MODEL_EVALUATE_PROMPT,
  )
-
+ OCR = TaskTypeDef(
+ name='ocr', desc='For performing OCR tasks', guidance=OCR_PROMPT)
  OTHER = TaskTypeDef(
  name='other', desc='Any tasks not in the defined categories')