app.py

import gradio as gr
import torch
from transformers import AutoModelForImageClassification, AutoImageProcessor
from PIL import Image

# Load model and processor
model = AutoModelForImageClassification.from_pretrained("jazzmacedo/fruits-and-vegetables-detector-36")
processor = AutoImageProcessor.from_pretrained("jazzmacedo/fruits-and-vegetables-detector-36")
labels = list(model.config.id2label.values())

def classify_image(image):
    # Preprocess the image
    inputs = processor(images=image, return_tensors="pt")
    with torch.no_grad():
        outputs = model(**inputs)
    predicted_idx = torch.argmax(outputs.logits, dim=1).item()
    predicted_label = labels[predicted_idx]
    return predicted_label

# Create Gradio interface
interface = gr.Interface(
    fn=classify_image,
    inputs=gr.Image(type="pil"),
    outputs=gr.Text(label="Detected Label"),
    title="Fruit & Vegetable Detector",
    description="Upload an image of a fruit or vegetable and get the predicted label."
)

if __name__ == "__main__":
    interface.launch()