app.py

import gradio as gr
import spaces
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

# Model configuration
LLAMA_MODEL = "meta-llama/Llama-2-7b-chat-hf"
MEDITRON_MODEL = "epfl-llm/meditron-7b"

SYSTEM_PROMPT = """You are a professional virtual doctor. Your goal is to collect detailed information about the user's health condition, symptoms, medical history, medications, lifestyle, and other relevant data.
Ask 1-2 follow-up questions at a time to gather more details about:
- Detailed description of symptoms
- Duration (when did it start?)
- Severity (scale of 1-10)
- Aggravating or alleviating factors
- Related symptoms
- Medical history
- Current medications and allergies
After collecting sufficient information (4-5 exchanges), summarize findings and suggest when they should seek professional care. Do NOT make specific diagnoses or recommend specific treatments.
Respond empathetically and clearly. Always be professional and thorough."""

MEDITRON_PROMPT = """<|im_start|>system
You are a specialized medical assistant focusing ONLY on suggesting over-the-counter medicines and home remedies based on patient information.
Based on the following patient information, provide ONLY:
1. One specific over-the-counter medicine with proper adult dosing instructions
2. One practical home remedy that might help
3. Clear guidance on when to seek professional medical care
Be concise, practical, and focus only on general symptom relief. Do not diagnose. Include a disclaimer that you are not a licensed medical professional.
<|im_end|>
<|im_start|>user
Patient information: {patient_info}
<|im_end|>
<|im_start|>assistant
"""

print("Loading Llama-2 model...")
tokenizer = AutoTokenizer.from_pretrained(LLAMA_MODEL)
model = AutoModelForCausalLM.from_pretrained(
    LLAMA_MODEL,
    torch_dtype=torch.float16,
    device_map="auto"
)
print("Llama-2 model loaded successfully!")

print("Loading Meditron model...")
meditron_tokenizer = AutoTokenizer.from_pretrained(MEDITRON_MODEL)
meditron_model = AutoModelForCausalLM.from_pretrained(
    MEDITRON_MODEL,
    torch_dtype=torch.float16,
    device_map="auto"
)
print("Meditron model loaded successfully!")

# Conversation state tracking
conversation_turns = {}
patient_data = {}

def build_llama2_prompt(system_prompt, history, user_input):
    """Format the conversation history and user input for Llama-2 chat models."""
    prompt = f"<s>[INST] <<SYS>>\n{system_prompt}\n<</SYS>>\n\n"
    
    # Add conversation history
    for user_msg, assistant_msg in history:
        prompt += f"{user_msg} [/INST] {assistant_msg} </s><s>[INST] "
    
    # Add the current user input
    prompt += f"{user_input} [/INST] "
    
    return prompt

def get_meditron_suggestions(patient_info):
    """Use Meditron model to generate medicine and remedy suggestions."""
    prompt = MEDITRON_PROMPT.format(patient_info=patient_info)
    inputs = meditron_tokenizer(prompt, return_tensors="pt").to(meditron_model.device)
    
    with torch.no_grad():
        outputs = meditron_model.generate(
            inputs.input_ids,
            attention_mask=inputs.attention_mask,
            max_new_tokens=256,
            temperature=0.7,
            top_p=0.9,
            do_sample=True
        )
    
    suggestion = meditron_tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
    return suggestion

@spaces.GPU
def generate_response(message, history):
    """Generate a response using both models."""
    # Track conversation turns
    session_id = "default-session"
    if session_id not in conversation_turns:
        conversation_turns[session_id] = 0
    conversation_turns[session_id] += 1
    
    # Store the entire conversation for reference
    if session_id not in patient_data:
        patient_data[session_id] = []
    patient_data[session_id].append(message)
    
    # Build the prompt with proper Llama-2 formatting
    prompt = build_llama2_prompt(SYSTEM_PROMPT, history, message)
    
    # Add summarization instruction after 4 turns
    if conversation_turns[session_id] >= 4:
        prompt = prompt.replace("[/INST] ", "[/INST] Now summarize what you've learned and suggest when professional care may be needed. ")
    
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    
    # Generate the Llama-2 response
    with torch.no_grad():
        outputs = model.generate(
            inputs.input_ids,
            attention_mask=inputs.attention_mask,
            max_new_tokens=512,
            temperature=0.7,
            top_p=0.9,
            do_sample=True,
            pad_token_id=tokenizer.eos_token_id
        )
    
    # Decode and extract Llama-2's response
    full_response = tokenizer.decode(outputs[0], skip_special_tokens=False)
    llama_response = full_response.split('[/INST]')[-1].split('</s>')[0].strip()
    
    # After 4 turns, add medicine suggestions from Meditron
    if conversation_turns[session_id] >= 4:
        # Collect full patient conversation
        full_patient_info = "\n".join(patient_data[session_id]) + "\n\nSummary: " + llama_response
        
        # Get medicine suggestions
        medicine_suggestions = get_meditron_suggestions(full_patient_info)
        
        # Format final response
        final_response = (
            f"{llama_response}\n\n"
            f"--- MEDICATION AND HOME CARE SUGGESTIONS ---\n\n"
            f"{medicine_suggestions}"
        )
        return final_response
    
    return llama_response

# Create the Gradio interface
demo = gr.ChatInterface(
    fn=generate_response,
    title="Medical Assistant with Medicine Suggestions",
    description="Tell me about your symptoms, and after gathering enough information, I'll suggest potential remedies.",
    examples=[
        "I have a cough and my throat hurts",
        "I've been having headaches for a week",
        "My stomach has been hurting since yesterday"
    ],
    theme="soft"
)

if __name__ == "__main__":
    demo.launch()