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Browse files- qachat.py +62 -0
- requirements.txt +3 -0
qachat.py
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from dotenv import load_dotenv
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load_dotenv()
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import streamlit as st
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import os
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import google.generativeai as genai
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# Configure Google Generative AI
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genai.configure(api_key=os.getenv("GOOGLE_KEY"))
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model = genai.GenerativeModel("gemini-pro")
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chat = model.start_chat(history=[])
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def get_gemini_response(prompt):
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try:
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response = chat.send_message(prompt, stream=True)
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return response
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except Exception as e:
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return f"An error occurred: {str(e)}"
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# Streamlit app configuration
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st.set_page_config(page_title = "Med ChatBot")
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st.header = ("ChatBot App")
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# Initialize session state for chat history
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if "chat_history" not in st.session_state:
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st.session_state["chat_history"] = []
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# Input and submission button
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input_text = st.text_input("Input: ", key="input")
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submit = st.button("Ask the question")
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if submit and input_text:
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# Context for the LLM with history included
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chat_history_text = " ".join([f"{role}: {text}" for role, text in st.session_state["chat_history"]])
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context = (
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"You are a medical chatbot designed to assist users in understanding their symptoms. "
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"Provide clear, concise, and informative responses based on NHS guidelines. "
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"Avoid technical jargon and code snippets. If asked a question unrelated to medical topics, "
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"respond with: 'I am a medical bot and I don't have that knowledge.' "
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f"Previous conversation: {chat_history_text} "
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)
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prompt = f"{context} User's latest input: {input_text}" # Include the latest user input
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response = get_gemini_response(prompt)
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# Add user query to session state chat history
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st.session_state['chat_history'].append(("You", input_text))
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st.subheader("The Response is")
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if isinstance(response, str): # Handle cases where response might be a string
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st.write(response)
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st.session_state['chat_history'].append(("Bot", response))
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else:
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for chunk in response:
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st.write(chunk.text)
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st.session_state['chat_history'].append(("Bot", chunk.text))
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st.subheader("The Chat History is")
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for role, text in st.session_state['chat_history']:
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st.write(f"{role}: {text}")
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requirements.txt
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streamlit
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google-generativeai
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python-dotenv
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