app.py

import gradio as gr
import torch
from PIL import Image
import time
from threading import Thread
from transformers import (
    Qwen2VLForConditionalGeneration,
    AutoProcessor,
    TextIteratorStreamer,
)

# Load model and processor - CPU version
MODEL_ID = "prithivMLmods/Qwen2-VL-OCR-2B-Instruct"
processor = AutoProcessor.from_pretrained(MODEL_ID, trust_remote_code=True)
model = Qwen2VLForConditionalGeneration.from_pretrained(
    MODEL_ID,
    trust_remote_code=True,
    torch_dtype=torch.float32  # Using float32 for CPU compatibility
).to("cpu").eval()

def extract_medicines(image):
    """Extract medicine names from prescription images."""
    if image is None:
        return "Please upload a prescription image."
    
    # Process the image
    text = "Extract ONLY the names of medications/medicines from this prescription image. Format the output as a numbered list of medicine names only, without dosages or instructions."
    
    messages = [{
        "role": "user",
        "content": [
            {"type": "image", "image": Image.open(image)},
            {"type": "text", "text": text},
        ],
    }]
    
    prompt_full = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
    inputs = processor(
        text=[prompt_full],
        images=[Image.open(image)],
        return_tensors="pt",
        padding=True,
    ).to("cpu")
    
    # Generate response
    with torch.no_grad():
        output = model.generate(**inputs, max_new_tokens=512)
    
    # Decode and return response
    response = processor.decode(output[0], skip_special_tokens=True)
    
    # Clean up the response to get just the model's answer
    if "<|assistant|>" in response:
        response = response.split("<|assistant|>")[1].strip()
    
    return response

# Create a simple Gradio interface
demo = gr.Interface(
    fn=extract_medicines,
    inputs=gr.Image(type="filepath", label="Upload Prescription Image"),
    outputs=gr.Textbox(label="Extracted Medicine Names"),
    title="Medicine Name Extractor",
    description="Upload prescription images to extract medicine names",
    examples=[["examples/prescription1.jpg"]], # Update with your actual example paths or remove if not available
    cache_examples=True,
)

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