import os
import torch
from transformers import AutoModel, AutoTokenizer, BitsAndBytesConfig
import gradio as gr
from PIL import Image
from torchvision.transforms import ToTensor

# Get API token from environment variable
api_token = os.getenv("HF_TOKEN").strip()

# Quantization configuration
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_use_double_quant=True,
    bnb_4bit_compute_dtype=torch.float16
)

# Initialize model and tokenizer
model = AutoModel.from_pretrained(
    "ContactDoctor/Bio-Medical-MultiModal-Llama-3-8B-V1",
    quantization_config=bnb_config,
    device_map="auto",
    torch_dtype=torch.float16,
    trust_remote_code=True,
    attn_implementation="flash_attention_2",
    token=api_token
)

tokenizer = AutoTokenizer.from_pretrained(
    "ContactDoctor/Bio-Medical-MultiModal-Llama-3-8B-V1",
    trust_remote_code=True,
    token=api_token
)

def analyze_input(image, question):
    try:
        if image is not None:
            # Convert to RGB if image is provided
            image = image.convert('RGB')
        
        # Prepare messages in the format expected by the model
        msgs = [{'role': 'user', 'content': [image, question]}]
        
        # Generate response using the chat method
        response_stream = model.chat(
            image=image,
            msgs=msgs,
            tokenizer=tokenizer,
            sampling=True,
            temperature=0.95,
            stream=True
        )
        
        # Collect the streamed response
        generated_text = ""
        for new_text in response_stream:
            generated_text += new_text
            print(new_text, flush=True, end='')
        
        return {"status": "success", "response": generated_text}
    
    except Exception as e:
        import traceback
        error_trace = traceback.format_exc()
        print(f"Error occurred: {error_trace}")
        return {"status": "error", "message": str(e)}

# Create Gradio interface
demo = gr.Interface(
    fn=analyze_input,
    inputs=[
        gr.Image(type="pil", label="Upload Medical Image"),
        gr.Textbox(
            label="Medical Question",
            placeholder="Give the modality, organ, analysis, abnormalities (if any), treatment (if abnormalities are present)?",
            value="Give the modality, organ, analysis, abnormalities (if any), treatment (if abnormalities are present)?"
        )
    ],
    outputs=gr.JSON(label="Analysis"),
    title="Medical Image Analysis Assistant",
    description="Upload a medical image and ask questions about it. The AI will analyze the image and provide detailed responses."
)

# Launch the Gradio app
if __name__ == "__main__":
    demo.launch(
        share=True,
        server_name="0.0.0.0",
        server_port=7860
    )