app.py

import gradio as gr
from transformers import pipeline
import torch

# Check if GPU is available (Spaces provides GPU)
device = 0 if torch.cuda.is_available() else -1

# Initialize the model
captioner = pipeline("image-to-text", 
                    model="Salesforce/blip-image-captioning-base",
                    device=device)

def generate_caption(image):
    """Generate caption for the given image"""
    try:
        captions = captioner(image)
        return captions[0]['generated_text']
    except Exception as e:
        return f"Error generating caption: {str(e)}"

# Create Gradio interface without examples
interface = gr.Interface(
    fn=generate_caption,
    inputs=gr.Image(type="pil"),
    outputs=gr.Textbox(label="Generated Caption"),
    title="Image Caption Generator",
    description="Upload an image and get an AI-generated caption!",
    article="Built using the BLIP image captioning model from Salesforce."
)

# Launch the app
interface.launch()