app.py

from transformers import AutoProcessor, AutoModelForCausalLM
from PIL import Image
import gradio as gr

# Load the processor and model
processor = AutoProcessor.from_pretrained("microsoft/git-base-coco")
model = AutoModelForCausalLM.from_pretrained("microsoft/git-base-coco")

# Define the captioning function
def caption_image(image):
    # Process the image
    pixel_values = processor(images=image, return_tensors="pt").pixel_values
    # Generate captions
    generated_ids = model.generate(pixel_values=pixel_values, max_length=50)
    generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
    return generated_caption

# Define Gradio interface components
inputs = [
    gr.inputs.Image(type='pil', label='Upload Image')
]

outputs = [
    gr.outputs.Textbox(label='Generated Caption')
]

# Define Gradio app properties
title = "Image Captioning Application"
description = "Upload an image to see the caption generated by the model"

# Create and launch the Gradio interface
gr.Interface(
    fn=caption_image,
    inputs=inputs,
    outputs=outputs,
    title=title,
    description=description,
).launch(debug=True)