app.py

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

# Load Qwen-VL model and processor
model_id = "Qwen/Qwen-VL-Chat"
processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForVision2Seq.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")

# Inference function
def ocr_with_qwen(image):
    prompt = "<|im_start|>system\nYou are a helpful assistant. Extract all text from the image and output only the text.<|im_end|>\n<|im_start|>user\n"
    inputs = processor(images=image, text=prompt, return_tensors="pt").to(model.device)
    outputs = model.generate(**inputs, max_new_tokens=512)
    result = processor.batch_decode(outputs, skip_special_tokens=True)[0]
    return result.strip()

# Gradio UI
gr.Interface(
    fn=ocr_with_qwen,
    inputs=gr.Image(type="pil", label="Upload Image (test.jpg)"),
    outputs=gr.Textbox(label="Extracted Text"),
    title="OCR with Qwen2.5-VL",
    description="Upload an image to extract text using Qwen-VL model."
).launch()