app.py

import streamlit as st
from dotenv import load_dotenv
from groq import Client

# Load environment variables from .env file
load_dotenv()
GROQ_API_KEY = "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"

def main():
    st.set_page_config(page_title="Gynecology Consultation Chatbot", layout="wide")
    st.title("🩺 Gynecology Consultation Assistant")

    # Load chatbot model
    @st.cache_resource
    def load_chatbot():
        return Client(api_key=GROQ_API_KEY)

    assistant = load_chatbot()

    # Initialize chat history
    if "messages" not in st.session_state:
        st.session_state["messages"] = [{"role": "system", "content": "You are a professional gynecologist assistant. Your role is to provide accurate and up-to-date medical advice related to women's health, pregnancy, menstrual cycles, reproductive health, and gynecological concerns. Always ensure that responses are factual, empathetic, and professional. If a query is **unrelated to gynecology or medical concerns**, politely redirect the user by saying: 'I specialize in gynecology-related assistance. Let me know how I can help with your health concerns.'"}]

    # Sidebar for chat history
    with st.sidebar:
        st.header("Chat History")
        for message in st.session_state["messages"]:
            if message["role"] != "system":
                st.text_area(label=message["role"].capitalize(), value=message["content"], height=100, disabled=True)

    # User input
    user_input = st.chat_input("How can I assist you with your gynecological concerns?")
    
    if user_input:
        # Add user message to session state
        st.session_state["messages"].append({"role": "user", "content": user_input})
        with st.chat_message("user"):
            st.markdown(user_input)

        # Generate response with full conversation history
        response = assistant.chat.completions.create(
            model="llama-3.3-70b-versatile",
            messages=st.session_state["messages"]  # Pass entire chat history
        )

        assistant_reply = response.choices[0].message.content

        # Add assistant's response to session state
        st.session_state["messages"].append({"role": "assistant", "content": assistant_reply})
        with st.chat_message("assistant"):
            st.markdown(assistant_reply)

if __name__ == "__main__":
    main()