增强AI助手功能：添加学习中心支持聊天记录上传和知识文档投喂

README.md

@@ -12,4 +12,23 @@ hf_oauth_scopes:
 - inference-api
 ---
 
-一个使用[Gradio](https://gradio.app)、[`huggingface_hub`](https://huggingface.co/docs/huggingface_hub/v0.22.2/en/index)和[Hugging Face推理API](https://huggingface.co/docs/api-inference/index)的中文聊天机器人示例。
+# 我的AI助手
+
+一个支持持续学习的中文聊天机器人，具备以下功能：
+
+## 主要特性
+- 🗣️ 中文对话交流
+- 📚 知识学习能力
+- 📝 聊天记录上传学习
+- 📄 知识文档投喂学习
+- 🔄 对话历史记录
+
+## 使用说明
+1. 在"聊天"标签页中与AI助手进行对话
+2. 在"学习中心"标签页中上传聊天记录或知识文档
+3. AI助手会根据上传的材料不断改进回答质量
+
+## 技术细节
+- 使用 THUDM/chatglm3-6b 模型
+- 基于 Gradio 构建界面
+- 支持 Hugging Face OAuth 认证

app.py

@@ -1,7 +1,38 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
+import json
+import os
+from datetime import datetime
 
 
+# 存储对话历史的文件
+HISTORY_FILE = "conversation_history.json"
+KNOWLEDGE_FILE = "knowledge_base.txt"
+
+def load_history():
+    """加载对话历史"""
+    if os.path.exists(HISTORY_FILE):
+        with open(HISTORY_FILE, 'r', encoding='utf-8') as f:
+            return json.load(f)
+    return []
+
+def save_history(history):
+    """保存对话历史"""
+    with open(HISTORY_FILE, 'w', encoding='utf-8') as f:
+        json.dump(history, f, ensure_ascii=False, indent=2)
+
+def load_knowledge():
+    """加载知识库"""
+    if os.path.exists(KNOWLEDGE_FILE):
+        with open(KNOWLEDGE_FILE, 'r', encoding='utf-8') as f:
+            return f.read()
+    return ""
+
+def save_knowledge(content):
+    """保存知识库"""
+    with open(KNOWLEDGE_FILE, 'w', encoding='utf-8') as f:
+        f.write(content)
+
 def respond(
     message,
     history: list[dict[str, str]],
@@ -14,6 +45,14 @@ def respond(
     """
     更多关于 `huggingface_hub` 推理API的信息，请查看文档: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
     """
+    # 保存对话历史
+    conversation_data = {
+        "timestamp": datetime.now().isoformat(),
+        "message": message,
+        "history": history,
+        "response": ""
+    }
+    
     # 使用一个更适合中文的模型
     client = InferenceClient(token=hf_token.token, model="THUDM/chatglm3-6b")
     messages = [{"role": "system", "content": system_message}]
@@ -37,36 +76,122 @@ def respond(
 
         response += token
         yield response
+    
+    # 保存响应到历史记录
+    conversation_data["response"] = response
+    history_list = load_history()
+    history_list.append(conversation_data)
+    save_history(history_list)
+
+
+def upload_chat_history(file):
+    """处理上传的聊天记录"""
+    if file is None:
+        return "请上传文件"
+    
+    try:
+        # 读取上传的文件
+        with open(file.name, 'r', encoding='utf-8') as f:
+            content = f.read()
+        
+        # 保存到历史记录
+        history_list = load_history()
+        history_entry = {
+            "timestamp": datetime.now().isoformat(),
+            "uploaded_chat": content,
+            "type": "uploaded_history"
+        }
+        history_list.append(history_entry)
+        save_history(history_list)
+        
+        return f"成功上传聊天记录，共{len(content)}个字符"
+    except Exception as e:
+        return f"上传失败: {str(e)}"
+
+
+def upload_knowledge_document(file):
+    """处理上传的知识文档"""
+    if file is None:
+        return "请上传文件"
+    
+    try:
+        # 读取上传的文件
+        with open(file.name, 'r', encoding='utf-8') as f:
+            content = f.read()
+        
+        # 追加到知识库
+        existing_knowledge = load_knowledge()
+        updated_knowledge = existing_knowledge + "\n\n" + content
+        save_knowledge(updated_knowledge)
+        
+        return f"成功添加知识文档，共{len(content)}个字符"
+    except Exception as e:
+        return f"上传失败: {str(e)}"
 
 
 """
 有关如何自定义聊天界面的信息，请查看gradio文档: https://www.gradio.app/docs/chatinterface
 """
-chatbot = gr.ChatInterface(
-    respond,
-    type="messages",
-    title="我的AI助手",
-    description="与AI助手进行中文对话",
-    # 移除示例以避免需要登录用户进行缓存
-    # examples=[["你好"], ["你能帮我做什么？"], ["今天天气怎么样？"]],
-    additional_inputs=[
-        gr.Textbox(value="你是一个友好的中文聊天机器人。", label="系统消息"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="最大新令牌数"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="温度"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (核采样)",
-        ),
-    ],
-)
-
 with gr.Blocks() as demo:
-    with gr.Sidebar():
-        gr.LoginButton()
-    chatbot.render()
+    with gr.Tab("聊天"):
+        chatbot = gr.ChatInterface(
+            respond,
+            type="messages",
+            title="我的AI助手",
+            description="与AI助手进行中文对话",
+            additional_inputs=[
+                gr.Textbox(value="你是一个友好的中文聊天机器人。", label="系统消息"),
+                gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="最大新令牌数"),
+                gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="温度"),
+                gr.Slider(
+                    minimum=0.1,
+                    maximum=1.0,
+                    value=0.95,
+                    step=0.05,
+                    label="Top-p (核采样)",
+                ),
+            ],
+        )
+        with gr.Sidebar():
+            gr.LoginButton()
+    
+    with gr.Tab("学习中心"):
+        gr.Markdown("## 上传学习材料")
+        
+        with gr.Row():
+            with gr.Column():
+                chat_file = gr.File(label="上传聊天记录", file_types=[".txt", ".json"])
+                upload_chat_btn = gr.Button("处理聊天记录")
+                chat_output = gr.Textbox(label="处理结果")
+                
+            with gr.Column():
+                knowledge_file = gr.File(label="上传知识文档", file_types=[".txt", ".md", ".pdf"])
+                upload_knowledge_btn = gr.Button("处理知识文档")
+                knowledge_output = gr.Textbox(label="处理结果")
+        
+        upload_chat_btn.click(
+            upload_chat_history,
+            inputs=[chat_file],
+            outputs=[chat_output]
+        )
+        
+        upload_knowledge_btn.click(
+            upload_knowledge_document,
+            inputs=[knowledge_file],
+            outputs=[knowledge_output]
+        )
+        
+        gr.Markdown("## 当前知识状态")
+        knowledge_display = gr.Textbox(label="知识库内容", max_lines=10)
+        refresh_btn = gr.Button("刷新知识库")
+        
+        def refresh_knowledge():
+            return load_knowledge()
+            
+        refresh_btn.click(
+            refresh_knowledge,
+            outputs=[knowledge_display]
+        )
 
 
 if __name__ == "__main__":

requirements.txt

@@ -0,0 +1,2 @@
+gradio>=5.42.0
+huggingface_hub>=0.22.2