app.py

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

# Load your model and processor
processor = BlipProcessor.from_pretrained("quadranttechnologies/Dileep_model")
model = BlipForConditionalGeneration.from_pretrained("quadranttechnologies/Dileep_model")

# Define a function to generate captions for the uploaded image
def generate_caption(image):
    try:
        # Convert the image into the required format for the model
        inputs = processor(image, return_tensors="pt")

        # Generate caption
        outputs = model.generate(**inputs)
        caption = processor.decode(outputs[0], skip_special_tokens=True)
        return caption
    except Exception as e:
        return f"Error generating caption: {e}"

# Set up Gradio interface for image upload and caption generation
interface = gr.Interface(
    fn=generate_caption,
    inputs=gr.Image(type="pil"),  # Accepts uploaded images
    outputs="text",               # Displays the caption as text
    title="Image Captioning Model",
    description="Upload an image to receive a caption generated by the model."
)

# Launch the Gradio app
if __name__ == "__main__":
    interface.launch(share=True)  # Set share=True to enable public link if needed