app.py

# app.py
import gradio as gr
from transformers import AutoProcessor, AutoModelForVision2Seq
import torch

# -------------------
# 1️⃣ Load Model
# -------------------
def load_model():
    device = "cuda" if torch.cuda.is_available() else "cpu"
    dtype = torch.float16 if device == "cuda" else torch.float32

    # Load model and processor from Hugging Face
    processor = AutoProcessor.from_pretrained("Muhammadidrees/RaiyaChatDoc", trust_remote_code=True)
    model = AutoModelForVision2Seq.from_pretrained(
        "Muhammadidrees/RaiyaChatDoc",
        torch_dtype=dtype,
        device_map="auto"  # automatically assigns to GPU if available
    )
    model.to(device)
    return processor, model, device

processor, model, device = load_model()

# -------------------
# 2️⃣ Chat Logic
# -------------------
def process_message(message, history, question_count):
    if not message.strip():
        return history, history, question_count
    
    history.append([message, None])
    question_count += 1

    # Decide if analysis is needed
    should_analyze = question_count >= 6 or any(
        word in message.lower() for word in ["analysis", "diagnose", "what do you think", "causes"]
    )

    # System prompt
    system_prompt = (
        "You are a medical doctor. "
        "Provide a comprehensive analysis of potential causes for symptoms."
        if should_analyze else
        "You are a medical doctor conducting a patient interview. Ask ONE specific question."
    )

    # Build conversation context
    dialogue = []
    for user_msg, bot_msg in history[:-1]:
        if user_msg: dialogue.append(f"Patient: {user_msg}")
        if bot_msg: dialogue.append(f"Doctor: {bot_msg}")
    dialogue.append(f"Patient: {message}")
    prompt = f"{system_prompt}\n\nConversation:\n" + "\n".join(dialogue) + "\nDoctor:"

    # Prepare input
    inputs = processor(text=prompt, images=None, return_tensors="pt").to(device)
    max_tokens = 400 if should_analyze else 25

    with torch.inference_mode():
        outputs = model.generate(
            **inputs,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=0.6,
            top_p=0.9,
            repetition_penalty=1.1,
            pad_token_id=processor.tokenizer.eos_token_id
        )

    # Decode response
    input_length = inputs["input_ids"].shape[1]
    response = processor.batch_decode(outputs[:, input_length:], skip_special_tokens=True)[0].strip()
    if response.lower().startswith("doctor:"):
        response = response[7:].strip()

    # Concise question formatting
    if not should_analyze:
        response = response.split('?')[0].strip() + '?'

    history[-1][1] = response
    if should_analyze: question_count = 0

    return history, history, question_count

def force_analysis(history, question_count):
    return history, 10

def clear_chat():
    return [], [], 0

# -------------------
# 3️⃣ Gradio Interface
# -------------------
with gr.Blocks(title="ChatDOC") as demo:
    question_count_state = gr.State(0)
    
    gr.Markdown("# 🩺 Chat with ChatDOC\nDescribe your symptoms and get guidance.")
    chatbot = gr.Chatbot(value=[], height=400, show_label=False)
    
    with gr.Row():
        msg = gr.Textbox(placeholder="Describe your symptoms...", scale=4, container=False, show_label=False)
        send_btn = gr.Button("Send", variant="primary", scale=1)
    
    with gr.Row():
        analysis_btn = gr.Button("Request Analysis", variant="secondary")
        clear_btn = gr.Button("Clear Chat", variant="stop")
    
    send_event = send_btn.click(
        process_message, inputs=[msg, chatbot, question_count_state], outputs=[chatbot, chatbot, question_count_state]
    ).then(lambda: "", outputs=[msg])
    
    msg.submit(
        process_message, inputs=[msg, chatbot, question_count_state], outputs=[chatbot, chatbot, question_count_state]
    ).then(lambda: "", outputs=[msg])
    
    analysis_btn.click(force_analysis, inputs=[chatbot, question_count_state], outputs=[chatbot, question_count_state])
    clear_btn.click(clear_chat, outputs=[chatbot, chatbot, question_count_state])

# -------------------
# 4️⃣ Launch
# -------------------
if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0", server_port=7860, share=False, debug=True)