app.py

import torch
from transformers import BlipProcessor, BlipForConditionalGeneration
import gradio as gr
from PIL import Image

# Load the AI Image Captioning Model
device = "cuda" if torch.cuda.is_available() else "cpu"
processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base").to(device)

# Define Caption Generation Function
def generate_caption(image):
    if isinstance(image, Image.Image) and image.mode != "RGB":
        image = image.convert("RGB")
    inputs = processor(image, return_tensors="pt").to(device)
    caption = model.generate(**inputs)
    return processor.decode(caption[0], skip_special_tokens=True)

# Gradio Interface
iface = gr.Interface(
    fn=generate_caption,
    inputs=gr.Image(type="pil"),
    outputs=gr.Textbox(label="Generated Caption"),
    title="🖼️ Auto Image Captioning",
    description="Upload an image, and let AI caption it with BLIP!"
)

# Launch Gradio App (no share=True needed for Hugging Face Spaces)
iface.launch()