app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
import torch
import time

# =======================================================
# Global session state for multi-step questioning
# =======================================================
session_answers = {}

# =======================================================
# Load Model
# =======================================================
model_name = "augtoma/qCammel-13"
print("Loading tokenizer and model...")

tokenizer = AutoTokenizer.from_pretrained(model_name)
if tokenizer.pad_token is None:
    tokenizer.pad_token = tokenizer.eos_token

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    device_map="auto",
    torch_dtype=torch.float16,
    trust_remote_code=True,
    low_cpu_mem_usage=True
)
model.eval()

print("Model loaded successfully!")
print(f"Device map: {model.hf_device_map}")
print(f"Model device: {next(model.parameters()).device}")

# =======================================================
# Generate Doctor Response
# =======================================================
def generate_doctor_response(history):
    global session_answers
    user_message = history[-1]["content"]

    if not user_message.strip():
        history.append({"role": "assistant", "content": "⚠️ Please describe your symptoms or ask a question."})
        yield history
        return

    # Build prompt with context
    prompt = """ You are ChatDoctor, an AI medical assistant.
    Respond only as the DOCTOR.
    Do not include patient responses or any dialogue tags.
    Provide clear, medically accurate answers in a single message.
    You are a highly knowledgeable and professional medical expert. Your role is to:
    1. Act as a doctor, nutritionist, and medical teacher simultaneously.
    2. Explain complex medical terms and conditions in simple, understandable language when asked.
    3. Provide accurate advice on lifestyle, diet, and general health when requested.
    4. Answer patient questions carefully, professionally, and concisely.
    5. Only ask follow-up question if required to gather relevant information before giving detailed recommendations.
    6. Be conversational, empathetic, and supportive, making the patient feel heard and guided.
    7. Provide disclaimers when needed: "⚕️ *This is AI-generated information and not a substitute for professional medical advice. Please consult a healthcare provider for proper diagnosis and treatment.*"
    
    Use this expertise to respond naturally to any patient message, balancing teaching, advice, and medical guidance.
    """
    recent_history = history[-10:-1] if len(history) > 10 else history[:-1]
    for msg in recent_history:
        role = "Patient" if msg["role"] == "user" else "Doctor"
        content = msg['content'].replace("⚕️ *Note: This is AI-generated information*", "").strip()
        prompt += f"{role}: {content}\n"
    prompt += f"Patient: {user_message}\nDoctor:"

    # Tokenize input
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

    # Generation configuration for concise, interactive answers
    gen_config = GenerationConfig(
        temperature=0.7,
        top_p=0.9,
        do_sample=True,
        max_new_tokens=500,  # short answers
        pad_token_id=tokenizer.pad_token_id,
        eos_token_id=tokenizer.eos_token_id,
        repetition_penalty=1.2
    )

    input_len = inputs["input_ids"].shape[1]

    with torch.no_grad():
        output_ids = model.generate(**inputs, generation_config=gen_config)

    generated_ids = output_ids[0][input_len:]
    response = tokenizer.decode(generated_ids, skip_special_tokens=True).strip()

    # Take only first 2-3 sentences to make it concise
    response = ". ".join(response.split(". ")[:3]).strip()
    if response.lower().startswith("doctor:"):
        response = response[7:].strip()
    if len(response) < 10:
        response = "I understand your concern. Could you please provide more details about your symptoms?"

    # Add assistant placeholder for streaming
    history.append({"role": "assistant", "content": ""})

    # Stream response token by token
    for i in range(0, len(response), 4):
        chunk = response[:i+4]
        history[-1]["content"] = chunk + "▌"
        yield history.copy()
        time.sleep(0.015)

    # Final response
    history[-1]["content"] = response
    yield history

# =======================================================
# Gradio Interface
# =======================================================
with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown("# 🩺 AI Doctor Chat Assistant")

    chatbot = gr.Chatbot(
        label="💬 Doctor Consultation",
        type='messages',
        avatar_images=(
            "https://cdn-icons-png.flaticon.com/512/706/706830.png",  # Patient
            "https://cdn-icons-png.flaticon.com/512/3774/3774299.png"   # Doctor
        ),
        height=500
    )

    with gr.Row():
        user_input = gr.Textbox(
            placeholder="Type your symptoms or question here...",
            label="🧍 Your Message",
            lines=2,
            scale=4
        )

    with gr.Row():
        send_btn = gr.Button("💬 Send", variant="primary", scale=1)
        clear_btn = gr.Button("🧹 Clear Chat", scale=1)

    gr.Examples(
        examples=[
            "I have a fever of 102°F since yesterday",
            "I've been having headaches for the past week",
            "I feel very tired all the time",
            "I have a sore throat and body aches",
        ],
        inputs=user_input,
        label="💡 Example Questions"
    )

    def respond(message, history):
        if history is None:
            history = []
        if not message.strip():
            return "", history
        history.append({"role": "user", "content": message})
        for updated_history in generate_doctor_response(history):
            yield "", updated_history

    send_btn.click(respond, [user_input, chatbot], [user_input, chatbot])
    user_input.submit(respond, [user_input, chatbot], [user_input, chatbot])
    clear_btn.click(lambda: [], None, chatbot, queue=False)

# Launch
if __name__ == "__main__":
    demo.queue()
    demo.launch(share=True)