app.py

import gradio as gr
import torch
from transformers import ViTForImageClassification, ViTFeatureExtractor
from PIL import Image

# Load model and feature extractor
model = ViTForImageClassification.from_pretrained('shahmi0519/mango_artificial', num_labels=2, ignore_mismatched_sizes=True)
feature_extractor = ViTFeatureExtractor.from_pretrained('shahmi0519/mango_artificial')

# Move to GPU if available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)
model.eval()

# Class labels (modify according to your model)
class_labels = [
    "Artificial",
    "Natural"
]

def predict_freshness(image):
    # Preprocess image
    inputs = feature_extractor(images=image, return_tensors="pt").to(device)
    
    # Predict
    model.eval()
    with torch.no_grad():
        outputs = model(**inputs)
        logits = outputs.logits
        predicted_class_idx = logits.argmax(-1).item()
    
    # Get label
    try:
        label = class_labels[predicted_class_idx]
    except IndexError:
        label = f"Class {predicted_class_idx}"
    
    return label

# Create Gradio interface
title = "Freshness Detector"
description = "Upload an image of fruit/vegetable to detect its freshness state"
examples = [
    ["apple.jpeg"],
    ["banana.jpeg"],
    ["tomato.jpeg"]
]

iface = gr.Interface(
    fn=predict_freshness,
    inputs=gr.Image(type="pil", label="Upload Image"),
    outputs=gr.Label(label="Freshness State"),
    title=title,
    description=description,
    examples=examples
)

iface.launch(share=True)