app.py

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

# ---------------------------
# Helper Functions
# ---------------------------
def progress_bar_html(label: str, primary_color: str = "#4B0082", secondary_color: str = "#9370DB") -> str:
    """
    Returns an HTML snippet for a thin animated progress bar with a label.
    """
    return f'''
<div style="display: flex; align-items: center;">
    <span style="margin-right: 10px; font-size: 14px;">{label}</span>
    <div style="width: 110px; height: 5px; background-color: {secondary_color}; border-radius: 2px; overflow: hidden;">
        <div style="width: 100%; height: 100%; background-color: {primary_color}; animation: loading 1.5s linear infinite;"></div>
    </div>
</div>
<style>
@keyframes loading {{
    0% {{ transform: translateX(-100%); }}
    100% {{ transform: translateX(100%); }}
}}
</style>
    '''

# Model and Processor Setup - 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()

# Main Inference Function
def extract_medicines(image_files):
    """Extract medicine names from prescription images."""
    if not image_files:
        return "Please upload a prescription image."
    
    images = [load_image(image) for image in image_files]
    
    # Specific prompt to extract only medicine names
    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} for image in images],
            {"type": "text", "text": text},
        ],
    }]
    
    prompt_full = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
    inputs = processor(
        text=[prompt_full],
        images=images,
        return_tensors="pt",
        padding=True,
    ).to("cpu")
    
    streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True)
    generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=1024)
    
    thread = Thread(target=model.generate, kwargs=generation_kwargs)
    thread.start()
    
    buffer = ""
    yield progress_bar_html("Extracting Medicine Names")
    
    for new_text in streamer:
        buffer += new_text
        buffer = buffer.replace("<|im_end|>", "")
        time.sleep(0.01)
        yield buffer

# Gradio Interface
with gr.Blocks() as demo:
    gr.Markdown("# Medicine Name Extractor")
    gr.Markdown("Upload prescription images to extract medicine names")
    
    with gr.Row():
        with gr.Column():
            image_input = gr.File(
                label="Upload Prescription Image(s)", 
                file_count="multiple",
                file_types=["image"]
            )
            extract_btn = gr.Button("Extract Medicine Names", variant="primary")
        
        with gr.Column():
            output = gr.Markdown(label="Extracted Medicine Names")
    
    extract_btn.click(
        fn=extract_medicines,
        inputs=image_input,
        outputs=output
    )
    
    gr.Examples(
        examples=[
            ["examples/prescription1.jpg"],
            ["examples/prescription2.jpg"],
        ],
        inputs=image_input,
        outputs=output,
        fn=extract_medicines,
        cache_examples=True,
    )
    
    gr.Markdown("""
    ### Notes:
    - This app is optimized to run on CPU
    - Upload clear images of prescriptions for best results
    - Only medicine names will be extracted
    """)

demo.queue()
demo.launch(debug=True)