fix requirements

app.py

@@ -5,217 +5,92 @@ import gradio as gr
 import uuid
 from utils.chat import ChatLaborLaw
 
-# ==============================================================================
-# 1. GLOBAL INITIALIZATION (ทำครั้งเดียวตอนแอปเริ่มทำงาน)
-# ==============================================================================
-# --- Langfuse Handler ---
-LANGFUSE_HANDLER = CallbackHandler()
-
-# --- LLM Initialization ---
-# (ปรับแก้ส่วนนี้เพื่อเลือกว่าจะใช้โมเดลไหนเป็น default)
-MODEL_NAME_LLM = "jai-chat-1-3-2" 
-TEMPERATURE = 0
-
-if MODEL_NAME_LLM == "jai-chat-1-3-2":
-    LLM_MAIN = ChatOpenAI(
-        model=MODEL_NAME_LLM,
-        api_key=os.getenv("JAI_API_KEY"),
-        base_url=os.getenv("CHAT_BASE_URL"),
-        temperature=TEMPERATURE,
-        max_tokens=2048,
-        max_retries=2,
-        seed=13
-    )
-elif MODEL_NAME_LLM == "gemini-2.0-flash":
-    LLM_MAIN = ChatGoogleGenerativeAI(
-        model="gemini-1.5-flash", 
-        google_api_key=os.getenv("GOOGLE_API_KEY"),
-        temperature=TEMPERATURE,
-        max_output_tokens=2048, 
-        convert_system_message_to_human=True, 
-    )
-else:
-    raise ValueError(f"Unsupported LLM model '{MODEL_NAME_LLM}'.")
-
-# --- Database and Retriever Initialization ---
-MONGO_CONNECTION_STR = os.getenv('MONGO_CONNECTION_STRING')
-MONGO_DATABASE = os.getenv('MONGO_DATABASE')
-MONGO_COLLECTION = os.getenv('MONGO_COLLECTION')
-
-MONGO_CLIENT = MongoClient(MONGO_CONNECTION_STR)
-DB = MONGO_CLIENT[MONGO_DATABASE]
-MONGO_COLLECTION_INSTANCE = DB[MONGO_COLLECTION]
-RETRIEVER = RerankRetriever()
-
-print("Global objects initialized successfully.")
-
-# ==============================================================================
-# 2. HELPER FUNCTIONS (แปลงมาจากเมธอดใน Class)
-# ==============================================================================
-
-def format_main_context(list_of_documents):
-    formatted_docs = []
-    for i, doc in enumerate(list_of_documents):
-        metadata = doc.metadata
-        formatted = f"Doc{i}\n{metadata.get('law_name', '-')}\nมาตรา\t{metadata.get('section_number', '-')}\n{doc.page_content}\nประกาศ\t{metadata.get('publication_date', '-')}\nเริ่มใช้\t{metadata.get('effective_date', '-')}"
-        formatted_docs.append(formatted)
-    return "\n\n".join(formatted_docs)
-
-def format_ref_context(list_of_docs):
-    formatted_ref_docs = []
-    for i, doc in enumerate(list_of_docs):
-        formatted = f"{doc.get('law_name', '-')}\nมาตรา\t{doc.get('section_number', '-')}\n{doc.get('text', '-')}"
-        formatted_ref_docs.append(formatted)
-    return "\n\n".join(formatted_ref_docs)
-
-def get_main_context(user_query, **kwargs):
-    compression_retriever = RETRIEVER.get_compression_retriever(**kwargs)
-    return compression_retriever.invoke(user_query)
-
-def get_ref_context(main_context_docs):
-    all_reference_ids = set()
-    for context in main_context_docs:
-        references_list = context.metadata.get('references', [])
-        if isinstance(references_list, list):
-            for ref_str in references_list:
-                all_reference_ids.add(ref_str.replace("มาตรา", "").strip())
-    
-    if not all_reference_ids:
-        return []
-
-    mongo_query = {"law_type": "summary", "section_number": {"$in": list(all_reference_ids)}}
-    projection = {"text": 1, "law_name": 1, "section_number": 1}
-    return list(MONGO_COLLECTION_INSTANCE.find(mongo_query, projection))
-
-# ==============================================================================
-# 3. CORE LOGIC FUNCTIONS (RAG / Non-RAG)
-# ==============================================================================
-
-async def call_rag(user_input: str, langchain_history: list) -> str:
-    context_docs = get_main_context(user_input, law_type="summary")
-    main_context_str = format_main_context(context_docs)
-
-    ref_context_docs = get_ref_context(context_docs)
-    ref_context_str = format_ref_context(ref_context_docs) if ref_context_docs else "-"
-
-    rag_input_data = {
-        "question": user_input,
-        "main_context": main_context_str,
-        "ref_context": ref_context_str,
-        "history": langchain_history
-    }
-    
-    try:
-        prompt_messages = RAG_CHAT_PROMPT.format_messages(**rag_input_data)
-        response = await LLM_MAIN.ainvoke(prompt_messages, config={"callbacks": [LANGFUSE_HANDLER]})
-        clean_response = re.sub(r"<[^>]+>|#+", "", response.content).strip()
-        return clean_response
-    except Exception as e:
-        print(f"Error during RAG LLM call: {e}")
-        return "ขออภัย ระบบขัดข้องขณะประมวลผลคำตอบ"
-
-async def call_non_rag(user_input: str) -> str:
-    prompt_messages = NON_RAG_PROMPT.format(user_input=user_input)
-    response = await LLM_MAIN.ainvoke(prompt_messages, config={"callbacks": [LANGFUSE_HANDLER]})
-    return response.content.strip() if response and response.content else "ขออภัย ระบบไม่สามารถตอบคำถามได้ในขณะนี้"
-
-# ==============================================================================
-# 4. GRADIO EVENT HANDLERS
-# ==============================================================================
 
 def initialize_session():
-    """รีเซ็ต State ทั้งหมดสำหรับ Session ใหม่"""
+    """
+    สร้าง session ID ใหม่ และสร้าง instance ใหม่ของ ChatLaborLaw
+    """
     session_id = str(uuid.uuid4())[:8]
-    return "", session_id, [], []  # user_input, session_id, ui_history, langchain_history
+    chatbot_instance = ChatLaborLaw() 
+    return "", session_id, chatbot_instance, []
 
-async def chat_orchestrator(prompt: str, ui_history: list, langchain_history: list):
+async def chat_function(prompt: str, history_ui: list, chatbot_instance: ChatLaborLaw):
     """
-    ฟังก์ชันหลักที่จัดการการสนทนาทั้งหมด
+    จัดการการสนทนา
     """
-    if not prompt.strip():
-        return ui_history, langchain_history, ""
-
-    # 1. อัปเดต Langchain History ด้วยข้อความใหม่
-    langchain_history.append(HumanMessage(content=prompt))
-
-    # 2. จำแนกประเภทของ Input
-    try:
-        history_content_list = [msg.content for msg in langchain_history]
-        input_type = classify_input_type(prompt, history=history_content_list)
-    except Exception as e:
-        print(f"Error classifying input type: {e}. Defaulting to Non-RAG.")
-        input_type = "Non-RAG"
-
-    # 3. เรียกใช้ Flow ที่เหมาะสม
-    if input_type == "RAG":
-        ai_response = await call_rag(prompt, langchain_history)
-    else:
-        ai_response = await call_non_rag(prompt)
-
-    # 4. อัปเดต History ทั้งสองรูปแบบ
-    langchain_history.append(AIMessage(content=ai_response))
-    ui_history.append((prompt, ai_response))
-    
-    # 5. ส่งค่ากลับไปอัปเดต UI และ State
-    return ui_history, langchain_history, "" # ui_history, langchain_history, user_input (ให้เป็นค่าว่าง)
+    if chatbot_instance is None or not prompt.strip():
+        return history_ui, chatbot_instance, ""
+
+    response = await chatbot_instance.chat(prompt)
+    history_ui.append((prompt, response))
+    return history_ui, chatbot_instance, ""
 
-def send_feedback(feedback: str, history: list, session_id: str):
-    """บันทึก Feedback"""
-    if not feedback.strip(): return ""
+def save_feedback(feedback: str, history_ui: list, session_id: str):
+    """
+    บันทึก Feedback
+    """
+    if not feedback.strip():
+        return ""
     os.makedirs("feedback", exist_ok=True)
     filename = f"feedback/feedback_{session_id}.txt"
     with open(filename, "a", encoding="utf-8") as f:
-        f.write(f"=== Feedback Received ===\nSession ID: {session_id}\nFeedback: {feedback}\nChat History:\n")
-        for user_msg, assistant_msg in history:
-            f.write(f"User: {user_msg}\nAssistant: {assistant_msg}\n")
-        f.write("\n--------------------------\n\n")
+        f.write("=== Feedback Received ===\n")
+        f.write(f"Session ID: {session_id}\n")
+        f.write(f"Feedback: {feedback}\n\n")
+        f.write("Chat History:\n")
+        for user_msg, assistant_msg in history_ui:
+            f.write(f"User: {user_msg}\n")
+            f.write(f"Assistant: {assistant_msg}\n")
+            f.write("-" * 20 + "\n")
+        f.write("\n==========================\n\n")
     gr.Info("ขอบคุณสำหรับข้อเสนอแนะ!")
     return ""
 
-# ==============================================================================
-# 5. GRADIO UI DEFINITION
-# ==============================================================================
-
+# --------------------------------------------------------------------------
+# สร้าง Gradio Interface
+# --------------------------------------------------------------------------
 with gr.Blocks(theme=gr.themes.Soft(primary_hue="amber")) as demo:
     gr.Markdown("# สอบถามเรื่องกฎหมายแรงงาน")
 
-    # --- States ---
-    # session_id_state: เก็บ ID ของ session ปัจจุบัน
-    # langchain_history_state: เก็บประวัติการสนทนาในรูปแบบ Langchain Message (HumanMessage, AIMessage)
     session_id_state = gr.State()
-    langchain_history_state = gr.State([])
-
-    # --- UI Components ---
-    chatbot_interface = gr.Chatbot(label="ประวัติการสนทนา", height=550, bubble_styling=False, show_copy_button=True)
+    chatbot_instance_state = gr.State()
+    
+    chatbot_interface = gr.Chatbot(
+        label="ประวัติการสนทนา", 
+        height=550, 
+        bubble_styling=False,
+        show_copy_button=True
+    )
     user_input = gr.Textbox(placeholder="พิมพ์คำถามของคุณที่นี่...", label="คำถาม", lines=2)
+
     with gr.Row():
         submit_button = gr.Button("ส่ง", variant="primary", scale=4)
         clear_button = gr.Button("เริ่มการสนทนาใหม่", scale=1)
 
-    # --- Event Wiring ---
-    submit_button.click(
-        fn=chat_orchestrator,
-        inputs=[user_input, chatbot_interface, langchain_history_state],
-        outputs=[chatbot_interface, langchain_history_state, user_input]
+    submit_event = submit_button.click(
+        fn=chat_function,
+        inputs=[user_input, chatbot_interface, chatbot_instance_state],
+        outputs=[chatbot_interface, chatbot_instance_state, user_input]
     )
     user_input.submit(
-        fn=chat_orchestrator,
-        inputs=[user_input, chatbot_interface, langchain_history_state],
-        outputs=[chatbot_interface, langchain_history_state, user_input]
+        fn=chat_function,
+        inputs=[user_input, chatbot_interface, chatbot_instance_state],
+        outputs=[chatbot_interface, chatbot_instance_state, user_input]
     )
+
     clear_button.click(
         fn=initialize_session,
         inputs=[],
-        outputs=[user_input, session_id_state, chatbot_interface, langchain_history_state],
+        outputs=[user_input, session_id_state, chatbot_instance_state, chatbot_interface],
         queue=False
     )
-    
+
     with gr.Accordion("ส่งข้อเสนอแนะ (Feedback)", open=False):
         feedback_input = gr.Textbox(placeholder="ความคิดเห็นของคุณมีความสำคัญต่อการพัฒนาของเรา...", label="Feedback", lines=2, scale=4)
         send_feedback_button = gr.Button("ส่ง Feedback")
-    
+
     send_feedback_button.click(
-        fn=send_feedback,
+        fn=save_feedback,
         inputs=[feedback_input, chatbot_interface, session_id_state],
         outputs=[feedback_input],
         queue=False
@@ -224,7 +99,7 @@ with gr.Blocks(theme=gr.themes.Soft(primary_hue="amber")) as demo:
     demo.load(
         fn=initialize_session,
         inputs=[],
-        outputs=[user_input, session_id_state, chatbot_interface, langchain_history_state]
+        outputs=[user_input, session_id_state, chatbot_instance_state, chatbot_interface]
     )
 
 demo.queue().launch()