import gradio as gr
from transformers import AutoProcessor, AutoModelForVision2Seq
from PIL import Image
import torch

# Load Model & Processor
model_name = "ds4sd/SmolDocling-256M-preview"
processor = AutoProcessor.from_pretrained(model_name)
model = AutoModelForVision2Seq.from_pretrained(model_name, torch_dtype=torch.bfloat16).to("cuda" if torch.cuda.is_available() else "cpu")

def process_image(image):
    if image is None:
        return "Error: No image provided."

    # Convert image to RGB format to ensure compatibility
    image = image.convert("RGB")

    # Process the image
    inputs = processor(images=[image], return_tensors="pt").to(model.device)

    # Generate output (remove unnecessary kwargs)
    output = model.generate(**inputs)
    
    # Decode output text
    result = processor.batch_decode(output, skip_special_tokens=True)[0]
    return result

# Create Gradio Interface
iface = gr.Interface(
    fn=process_image,
    inputs=gr.Image(type="pil"),  # Fixed input format
    outputs="text",
    title="SmolDocling Document Processing",
    description="Upload a document image to extract text."
)

iface.launch(server_name="0.0.0.0", server_port=7860)