Update app.py
Browse files
app.py
CHANGED
@@ -1,47 +1,58 @@
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import gradio as gr
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from pipeline import run_with_chain
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from my_memory_logic import memory, restatement_chain
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def chat_history_fn(user_input, history):
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"""
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message dicts that Gradio's ChatInterface accepts.
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"""
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#
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# 2) Restate the new user question with chat history
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reformulated_q = restatement_chain.run({
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"chat_history": memory.chat_memory.messages,
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"input": user_input
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})
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#
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answer = run_with_chain(reformulated_q)
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memory.chat_memory.add_user_message(user_input)
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memory.chat_memory.add_ai_message(answer)
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history.append((user_input, answer))
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#
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# We'll build that from our (user_msg, ai_msg) pairs in 'history'.
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message_dicts = []
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for usr_msg, ai_msg in history:
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message_dicts.append({"role": "user", "content": usr_msg})
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message_dicts.append({"role": "assistant", "content": ai_msg})
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#
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return message_dicts
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demo = gr.ChatInterface(
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fn=chat_history_fn,
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title="DailyWellnessAI with Memory",
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import os
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import gradio as gr
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# Suppose 'run_with_chain' is your pipeline function from pipeline.py
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from pipeline import run_with_chain
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# Suppose 'memory' and 'restatement_chain' come from my_memory_logic.py
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from my_memory_logic import memory, restatement_chain
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def chat_history_fn(user_input, history):
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"""
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Rely on LangChain memory to store the entire conversation across calls.
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DO NOT re-add old messages from 'history' each time.
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Also, handle potential None or invalid strings for user_input/answer
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to avoid Pydantic validation errors.
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"""
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# -- 0) Sanitize user_input to ensure it's a valid string
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if not user_input or not isinstance(user_input, str):
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user_input = "" if user_input is None else str(user_input)
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# -- 1) Restate the new user question using existing LangChain memory
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reformulated_q = restatement_chain.run({
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"chat_history": memory.chat_memory.messages,
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"input": user_input
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})
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# -- 2) Pass the reformulated question into your pipeline
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answer = run_with_chain(reformulated_q)
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# also sanitize if needed
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if answer is None or not isinstance(answer, str):
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answer = "" if answer is None else str(answer)
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# -- 3) Add this new user->assistant turn to memory
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memory.chat_memory.add_user_message(user_input)
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memory.chat_memory.add_ai_message(answer)
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# -- 4) Update Gradio’s 'history' so the UI shows the new turn
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history.append((user_input, answer))
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# -- 5) Convert the entire 'history' to message dictionaries:
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# [{"role":"user","content":...},{"role":"assistant","content":...},...]
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message_dicts = []
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for usr_msg, ai_msg in history:
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if not isinstance(usr_msg, str):
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usr_msg = str(usr_msg) if usr_msg else ""
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if not isinstance(ai_msg, str):
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ai_msg = str(ai_msg) if ai_msg else ""
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message_dicts.append({"role": "user", "content": usr_msg})
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message_dicts.append({"role": "assistant", "content": ai_msg})
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# -- 6) Return the message dictionary list
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return message_dicts
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# Build your ChatInterface with the function
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demo = gr.ChatInterface(
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fn=chat_history_fn,
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title="DailyWellnessAI with Memory",
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