insight_genie.py

import gradio as gr
import json
import requests
import time

# --- Configuration for LM Studio API ---
# Make sure your LM Studio server is running with the specified model
LM_STUDIO_API_URL = "http://192.168.1.245:1234/v1/chat/completions"
# Make sure to replace this with your model name from LM Studio exactly
LM_MODEL_NAME = "google/gemma-3-27b"

# --- Advanced System Prompt for Pharma Market Research ---
system_prompt_content = """

You are InsightGenie, an AI-powered qualitative research assistant specialized in pharmaceutical and healthcare market research. Your purpose is to conduct a structured interview to understand patient, caregiver, or healthcare professional (HCP) experiences with a specific health condition or treatment.



**Instructions:**

1. **Persona:** You are a professional, neutral, and empathetic research interviewer. Use clear, simple language when speaking with patients and caregivers, and appropriate medical terminology when speaking with HCPs. Maintain a supportive and curious tone.

2. **Goal:** Your primary goal is to gather rich, detailed qualitative data. Ask open-ended questions that encourage detailed responses about personal experiences, emotional impact, and decision-making processes.

3. **Compliance:** Avoid providing any medical advice, diagnoses, or treatment recommendations. State that you are a research tool and not a substitute for a healthcare professional.

4. **Conversation Flow:**

    - After each user response, analyze the sentiment and key themes.

    - Based on your analysis, generate **one** follow-up question to probe deeper. Do not ask multiple questions.

    - You must keep the conversation focused on the specified health topic.

5. **Structured Output:** After each user turn, you must respond with a JSON object. The JSON should contain two fields:

    - `next_question`: The text of your next question for the user.

    - `summary`: A brief, neutral summary of the user's last response, including key terms or concepts.



**Example JSON Response for a patient interview:**

```json

{

  "next_question": "Can you describe the biggest challenges you faced when you were first diagnosed with this condition?",

  "summary": "The patient shared their initial diagnosis experience, mentioning feelings of uncertainty."

}

"""

# Global variable to store the conversation log for the current session
conversation_log = []

# --- Helper Functions ---

def log_conversation_turn(user_message, ai_response, ai_summary):
    """Appends a single turn to the in-memory conversation log."""
    global conversation_log
    conversation_log.append({
        "user_message": user_message,
        "ai_response": ai_response,
        "ai_summary": ai_summary,
        "timestamp": time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
    })

def save_conversation_log():
    """Saves the entire conversation log to a JSON file."""
    global conversation_log
    if not conversation_log:
        return "No conversation to save."
    
    file_name = f"conversation_log_{int(time.time())}.json"
    try:
        with open(file_name, 'w', encoding='utf-8') as f:
            json.dump(conversation_log, f, indent=4, ensure_ascii=False)
        return f"Conversation saved to {file_name}"
    except Exception as e:
        return f"Failed to save conversation: {e}"

def start_new_session(chatbot_history):
    """

    Saves the current conversation and starts a new, empty session.

    """
    global conversation_log
    
    # Save the current conversation log
    save_message = save_conversation_log()
    
    # Reset the in-memory log for the new session
    conversation_log = []
    
    # Clear the Gradio chatbot history for a fresh start
    return [], gr.Textbox(value=save_message, visible=True)

# --- Core Chat Logic Function ---

# --- Core Chat Logic Function ---
def chat_with_lm_studio(message, history):
    # This line ensures 'messages' is always defined at the start of the function.
    messages = [{"role": "system", "content": system_prompt_content}]

    for user_msg, assistant_msg in history:
        messages.append({"role": "user", "content": user_msg})
        messages.append({"role": "assistant", "content": assistant_msg})
    
    messages.append({"role": "user", "content": message})

    # The rest of the function remains the same, using the now-defined 'messages' variable.
    try:
        # In your chat_with_lm_studio function
# ...
        response = requests.post(
        LM_STUDIO_API_URL,
        json={
            "model": LM_MODEL_NAME,
            "messages": messages,
            "max_tokens": 150,
            "temperature": 0.7,
        # Remove or comment out this line:
        # "response_format": {"type": "json_object"}
    }
)
# ...
        
        response.raise_for_status()

        api_response_data = response.json()
        
        if 'choices' in api_response_data and len(api_response_data['choices']) > 0:
            raw_content = api_response_data['choices'][0]['message']['content']
            
            try:
                parsed_response = json.loads(raw_content)
                next_question = parsed_response.get("next_question", "Thank you for your response.")
                summary = parsed_response.get("summary", "No summary provided.")
                
                log_conversation_turn(message, next_question, summary)
                print(f"User: {message}\nAI Summary: {summary}\nAI Question: {next_question}\n---")

                # The fix is here: Return both the user message and the AI response
                history.append((message, next_question))
                
                # To clear the user input textbox, you need to return an empty string
                return "", history

            except json.JSONDecodeError:
                print("LLM failed to produce valid JSON. Raw output:", raw_content)
                history.append((message, "I'm sorry, I couldn't process that response. Can you please rephrase?"))
                return "", history

        else:
            error_message = api_response_data.get('error', 'Unknown API error.')
            print(f"API Error Response: {error_message}")
            history.append((message, f"An error occurred with the API: {error_message}. Please check the console."))
            return "", history

    except requests.exceptions.RequestException as e:
        history.append((message, f"An API error occurred: {e}. Please ensure LM Studio server is running and accessible."))
        return "", history
    except Exception as e:
        history.append((message, f"An unexpected error occurred: {e}"))
        return "", history
# --- Gradio Interface Layout ---
with gr.Blocks(theme=gr.themes.Soft(), title="InsightGenie Live Demo") as demo:
    gr.Markdown("# InsightGenie: Your AI-powered Qualitative Assistant 🧠")
    gr.Markdown(
        "Start a conversation with our AI researcher. The conversation data is "
        "automatically structured for analysis and can be saved to a file. "
        "Try asking about a patient's journey or an HCP's experience with a treatment."
    )
    
    # Textbox to display status messages (e.g., "Conversation saved!")
    status_message = gr.Textbox(label="Status", interactive=False, visible=False)

    chatbot = gr.Chatbot(height=500, placeholder="Type your first message to begin the interview...")
    msg = gr.Textbox(label="Your message")

    with gr.Row():
        chat_submit_btn = gr.Button("Send")
        chat_clear_btn = gr.Button("Clear Chat")
        new_session_btn = gr.Button("Start New Session")

    # Event handlers
    msg.submit(chat_with_lm_studio, [msg, chatbot], [msg, chatbot], concurrency_limit=None)
    chat_submit_btn.click(chat_with_lm_studio, [msg, chatbot], [msg, chatbot], concurrency_limit=None)
    chat_clear_btn.click(lambda: [], None, [chatbot]) # Updated to correctly clear the chatbot history
    
    new_session_btn.click(start_new_session, [chatbot], [chatbot, status_message])

# --- Launch the Demo ---
if __name__ == "__main__":
    demo.launch(inbrowser=True)