app.py

import gradio as gr
from transformers import CLIPProcessor, CLIPModel
from PIL import Image
import torch

# Load the model and processor
model_id = "DGurgurov/clip-vit-base-patch32-oxford-pets"
model = CLIPModel.from_pretrained(model_id)
processor_id = "openai/clip-vit-base-patch32"
processor = CLIPProcessor.from_pretrained(processor_id)

# Define the inference function
def predict(image):
    inputs = processor(images=image, return_tensors="pt")
    outputs = model.get_image_features(**inputs)
    logits_per_image = outputs.logits_per_image
    probs = torch.nn.functional.softmax(logits_per_image, dim=1)
    return {f"Class {i}": prob.item() for i, prob in enumerate(probs[0])}

# Define Gradio interface
image = gr.components.Image(type="pil")
label = gr.components.Label(num_top_classes=5)

interface = gr.Interface(
    fn=predict,
    inputs=image,
    outputs=label,
    title="CLIP Model - Oxford Pets",
    description="Upload an image and get the top 5 class predictions."
)

# Launch the Gradio app
interface.launch(share=True)