From fd81577bef49864d9c10df896d633e0438ce5cf3 Mon Sep 17 00:00:00 2001
From: huia <15220362887@163.com>
Date: Thu, 18 Jul 2024 22:22:26 +0800
Subject: [PATCH] =?UTF-8?q?=E6=B7=BB=E5=8A=A0=E4=B8=80=E4=B8=AA=20web=20?=
 =?UTF-8?q?=E5=BA=94=E7=94=A8?=
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---
 web_demo.py | 81 +++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 81 insertions(+)
 create mode 100644 web_demo.py

diff --git a/web_demo.py b/web_demo.py
new file mode 100644
index 0000000..e2e6a6f
--- /dev/null
+++ b/web_demo.py
@@ -0,0 +1,81 @@
+import json
+import torch
+import streamlit as st
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers.generation.utils import GenerationConfig
+from peft import PeftModel
+
+st.set_page_config(page_title="Baichuan")
+st.title("Baichuan")
+
+model_path = "./baichuan-inc/Baichuan-7B"
+# lora_path = './baichuan-inc/baichuan2-13b-iepile-lora'
+
+@st.cache_resource
+def init_model():
+    model = AutoModelForCausalLM.from_pretrained(
+        model_path,
+        device_map="auto",
+        torch_dtype=torch.float16,
+        trust_remote_code=True,
+    )
+    model.generation_config = GenerationConfig.from_pretrained(
+        model_path
+    )
+    # model = PeftModel.from_pretrained(
+    #     model,
+    #     lora_path,
+    # )
+
+    tokenizer = AutoTokenizer.from_pretrained(
+        model_path,
+        use_fast=False,
+        trust_remote_code=True
+    )
+    return model, tokenizer
+
+
+def clear_chat_history():
+    del st.session_state.messages
+
+
+def init_chat_history():
+    with st.chat_message("assistant", avatar='🤖'):
+        st.markdown("您好，我是百川大模型，很高兴为您服务🥰")
+
+    if "messages" in st.session_state:
+        for message in st.session_state.messages:
+            avatar = '🧑‍💻' if message["role"] == "user" else '🤖'
+            with st.chat_message(message["role"], avatar=avatar):
+                st.markdown(message["content"])
+    else:
+        st.session_state.messages = []
+
+    return st.session_state.messages
+
+
+def main():
+    model, tokenizer = init_model()
+    messages = init_chat_history()
+
+    if prompt := st.chat_input("Shift + Enter 换行, Enter 发送"):
+        with st.chat_message("user", avatar='🧑‍💻'):
+            st.markdown(prompt)
+        messages.append({"role": "user", "content": prompt})
+        print(f"[user] {prompt}", flush=True)
+
+        inputs = tokenizer(prompt, return_tensors='pt').to(model.device)
+        outputs = model.generate(**inputs, max_new_tokens=64, repetition_penalty=1.1)
+        response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+        with st.chat_message("assistant", avatar='🤖'):
+            st.markdown(response)
+        messages.append({"role": "assistant", "content": response})
+
+        print(json.dumps(messages, ensure_ascii=False), flush=True)
+
+        st.button("清空对话", on_click=clear_chat_history)
+
+
+if __name__ == "__main__":
+    main()