Implement Gemini 2.5 Flash API integration for medical chatbot

- Added Google Gemini 2.5 Flash API integration
- Implemented secure API key management via environment variables
- Added sidebar option for API key configuration
- Replaced placeholder response logic with live API calls
- Configured temperature and max tokens parameters for API requests
- Added comprehensive error handling with fallback responses
- Updated model selection to include Gemini options
- Improved user interface with loading spinner and enhanced descriptions

main.py

@@ -1,5 +1,6 @@
 import streamlit as st
 import os
+import google.generativeai as genai
 from typing import Optional
 
 # Set page configuration
@@ -18,17 +19,40 @@ def main():
     st.markdown(
         """
         Welcome to the Medical Q/A Chatbot! This application provides informational responses 
-        to medical questions. Please note that this is for educational purposes only and should 
-        not replace professional medical advice.
+        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",
-            ["GPT-3.5", "GPT-4", "Claude", "Local Model"],
+            ["Gemini 2.5 Flash", "Gemini Pro"],
             index=0
         )
         
@@ -37,7 +61,8 @@ def main():
             min_value=0.0,
             max_value=1.0,
             value=0.7,
-            step=0.1
+            step=0.1,
+            help="Controls randomness in responses. Lower values are more focused and deterministic."
         )
         
         max_tokens = st.number_input(
@@ -45,7 +70,8 @@ def main():
             min_value=100,
             max_value=4000,
             value=500,
-            step=100
+            step=100,
+            help="Maximum number of tokens in the response."
         )
     
     # Chat interface
@@ -71,8 +97,9 @@ def main():
         
         # Generate and display assistant response
         with st.chat_message("assistant"):
-            response = generate_medical_response(prompt, model_choice, temperature, max_tokens)
-            st.markdown(response)
+            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})
@@ -84,34 +111,86 @@ def main():
 
 def generate_medical_response(question: str, model: str, temperature: float, max_tokens: int) -> str:
     """
-    Generate a medical response based on the user's question.
-    This is a placeholder function that would integrate with actual AI models.
+    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."""
-    
-    # Placeholder response - in a real implementation, this would call an AI model
-    response = f"""
-    Thank you for your medical question: "{question}"
-    
-    I understand you're seeking medical information. While I'd like to help, I'm currently a template 
-    application that needs to be configured with proper medical AI models and knowledge bases.
-    
-    To get this chatbot fully functional, you would need to:
+    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."
     
-    1. **Integrate AI Models**: Connect to medical AI models (like BioBERT, ClinicalBERT, or specialized medical LLMs)
-    2. **Add Medical Knowledge Base**: Include verified medical databases and references
-    3. **Implement Safety Filters**: Add content moderation for medical accuracy
-    4. **Add Authentication**: Consider user verification for sensitive medical queries
-    
-    **Current Configuration:**
-    - Model: {model}
-    - Temperature: {temperature}
-    - Max Tokens: {max_tokens}
-    """
-    
-    return response + disclaimer
+    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
 
 if __name__ == "__main__":
-    main()
+    main()