app.py

import gradio as gr
from transformers import VisionEncoderDecoderModel, ViTImageProcessor, AutoTokenizer
from PIL import Image
import torch

# Load ViT-GPT2 (Apache-2.0 licensed, safe to use)
model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
feature_extractor = ViTImageProcessor.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
tokenizer = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning")

device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)

def caption_image(image):
    # Convert image to tensor
    pixel_values = feature_extractor(images=image, return_tensors="pt").pixel_values.to(device)

    # Generate caption
    output_ids = model.generate(pixel_values, max_length=50, num_beams=4)
    caption = tokenizer.decode(output_ids[0], skip_special_tokens=True)
    return caption

# Build Gradio app
demo = gr.Interface(
    fn=caption_image,
    inputs=gr.Image(type="pil"),
    outputs="text",
    title="Chart Analyzer",
    description="Upload a chart/visualization image and get a description of it."
)

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