app.py

import gradio as gr
from transformers import Pix2StructProcessor, Pix2StructForConditionalGeneration
from PIL import Image
import torch

# Load model & processor
processor = Pix2StructProcessor.from_pretrained("google/deplot")
model = Pix2StructForConditionalGeneration.from_pretrained("google/deplot")

# Move model to GPU if available
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)

# def extract_chart_data(img: Image.Image):
#     inputs = processor(images=img, return_tensors="pt").to(device)
#     outputs = model.generate(**inputs)
#     result = processor.decode(outputs[0], skip_special_tokens=True)
#     return result

def extract_chart_data(img: Image.Image):
    # Provide a header prompt for VQA
    header_text = "Extract chart data"
    
    inputs = processor(images=img, text=header_text, return_tensors="pt").to(device)
    outputs = model.generate(**inputs)
    result = processor.decode(outputs[0], skip_special_tokens=True)
    return result


# Gradio interface
iface = gr.Interface(
    fn=extract_chart_data,
    inputs=gr.Image(type="pil"),
    outputs="text",
    title="DePlot Chart Data Extractor 2",
    description="Upload a chart image and get its extracted data/description."
)

iface.launch()