Replace template with Gemini-powered medical chatbot

src/streamlit_app.py

@@ -1,40 +1,196 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import os
+import google.generativeai as genai
+from typing import Optional
 
-"""
-# Welcome to Streamlit!
+# Set page configuration
+st.set_page_config(
+    page_title="Medical Q/A Chatbot",
+    page_icon="🩺",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
 
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
+def main():
+    """Main function for the Medical Q/A Chatbot"""
+    
+    # Title and description
+    st.title("🩺 Medical Q/A Chatbot")
+    st.markdown(
+        """
+        Welcome to the Medical Q/A Chatbot! This application provides informational responses 
+        to medical questions using Google's Gemini 2.5 Flash API. Please note that this is for 
+        educational purposes only and should not replace professional medical advice.
+        """
+    )
+    
+    # Sidebar configuration
+    with st.sidebar:
+        st.header("Configuration")
+        
+        # API Key configuration
+        st.subheader("API Settings")
+        api_key = st.text_input(
+            "Gemini API Key",
+            type="password",
+            help="Enter your Google Gemini API key",
+            placeholder="AIzaSy..."
+        )
+        
+        # If API key is provided, set it as environment variable
+        if api_key:
+            os.environ['GEMINI_API_KEY'] = api_key
+            st.success("API Key configured successfully!")
+        else:
+            # Use default API key from environment if available
+            default_key = os.getenv('GEMINI_API_KEY', 'AIzaSyBEyc7iQCLXfry6V7pA0TDR1k0eriX_nDo')
+            if default_key:
+                os.environ['GEMINI_API_KEY'] = default_key
+                st.info("Using default API key from environment")
+        
+        st.divider()
+        
+        model_choice = st.selectbox(
+            "Select Model",
+            ["Gemini 2.5 Flash", "Gemini Pro"],
+            index=0
+        )
+        
+        temperature = st.slider(
+            "Temperature",
+            min_value=0.0,
+            max_value=1.0,
+            value=0.7,
+            step=0.1,
+            help="Controls randomness in responses. Lower values are more focused and deterministic."
+        )
+        
+        max_tokens = st.number_input(
+            "Max Tokens",
+            min_value=100,
+            max_value=4000,
+            value=500,
+            step=100,
+            help="Maximum number of tokens in the response."
+        )
+    
+    # Chat interface
+    st.header("Ask your medical question:")
+    
+    # Initialize chat history
+    if "messages" not in st.session_state:
+        st.session_state.messages = []
+    
+    # Display chat messages from history
+    for message in st.session_state.messages:
+        with st.chat_message(message["role"]):
+            st.markdown(message["content"])
+    
+    # Chat input
+    if prompt := st.chat_input("What is your medical question?"):
+        # Add user message to chat history
+        st.session_state.messages.append({"role": "user", "content": prompt})
+        
+        # Display user message
+        with st.chat_message("user"):
+            st.markdown(prompt)
+        
+        # Generate and display assistant response
+        with st.chat_message("assistant"):
+            with st.spinner("Thinking..."):
+                response = generate_medical_response(prompt, model_choice, temperature, max_tokens)
+                st.markdown(response)
+        
+        # Add assistant response to chat history
+        st.session_state.messages.append({"role": "assistant", "content": response})
+    
+    # Clear chat button
+    if st.button("Clear Chat History"):
+        st.session_state.messages = []
+        st.rerun()
 
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
+def generate_medical_response(question: str, model: str, temperature: float, max_tokens: int) -> str:
+    """
+    Generate a medical response using Google's Gemini API.
+    """
+    
+    # Disclaimer message
+    disclaimer = "\n\n**Disclaimer**: This response is for informational purposes only and should not replace professional medical advice, diagnosis, or treatment. Always consult with a qualified healthcare provider for medical concerns."
+    
+    try:
+        # Get API key from environment
+        api_key = os.getenv('GEMINI_API_KEY')
+        if not api_key:
+            return "Error: No API key configured. Please set your Gemini API key in the sidebar." + disclaimer
+        
+        # Configure the API
+        genai.configure(api_key=api_key)
+        
+        # Select model based on choice
+        model_name = "gemini-2.5-flash" if "Flash" in model else "gemini-pro"
+        
+        # Create the model with configuration
+        generation_config = {
+            "temperature": temperature,
+            "top_p": 1,
+            "top_k": 40,
+            "max_output_tokens": max_tokens,
+        }
+        
+        model_instance = genai.GenerativeModel(
+            model_name=model_name,
+            generation_config=generation_config
+        )
+        
+        # Create a medical-focused prompt
+        medical_prompt = f"""
+        You are a knowledgeable medical AI assistant. Please provide an informative and helpful response to the following medical question. 
+        Your response should be:
+        - Medically accurate and evidence-based
+        - Clear and easy to understand
+        - Comprehensive but concise
+        - Include relevant medical terminology with explanations
+        - Always emphasize when professional medical consultation is needed
+        
+        Question: {question}
+        
+        Please provide a detailed medical response while always reminding the user that this is for educational purposes only and cannot replace professional medical advice.
+        """
+        
+        # Generate response
+        response = model_instance.generate_content(medical_prompt)
+        
+        if response and response.text:
+            return response.text + disclaimer
+        else:
+            return "I apologize, but I couldn't generate a response at this time. Please try again or rephrase your question." + disclaimer
+            
+    except Exception as e:
+        error_msg = f"Error connecting to Gemini API: {str(e)}"
+        
+        # Provide fallback response with helpful information
+        fallback_response = f"""
+        {error_msg}
+        
+        I understand you're seeking medical information about: "{question}"
+        
+        While I'm currently unable to provide a detailed response due to technical issues, I recommend:
+        
+        1. **For urgent medical concerns**: Contact your healthcare provider immediately or call emergency services
+        2. **For general health questions**: Consult with your primary care physician
+        3. **For medication questions**: Speak with a pharmacist or your prescribing doctor
+        4. **For reliable medical information**: Visit reputable sources like:
+           - Mayo Clinic (mayoclinic.org)
+           - WebMD (webmd.com)
+           - MedlinePlus (medlineplus.gov)
+        
+        **Current Configuration:**
+        - Model: {model}
+        - Temperature: {temperature}
+        - Max Tokens: {max_tokens}
+        """
+        
+        return fallback_response + disclaimer
 
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+if __name__ == "__main__":
+    main()