Create app.py

app.py

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+import torch
+from transformers import ViTForImageClassification, ViTFeatureExtractor
+import gradio as gr
+from PIL import Image
+
+# Check if GPU is available
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Load pre-trained ViT model from Hugging Face
+model = ViTForImageClassification.from_pretrained('Dhahlan2000/ripeness_detection', num_labels=20)
+model.to(device)
+model.eval()
+
+# Load ViT feature extractor
+feature_extractor = ViTFeatureExtractor.from_pretrained('Dhahlan2000/ripeness_detection')
+
+# Class labels
+predicted_classes = [
+    'FreshApple', 'FreshBanana', 'FreshBellpepper', 'FreshCarrot', 'FreshCucumber', 'FreshMango', 'FreshOrange', 
+    'FreshPotato', 'FreshStrawberry', 'FreshTomato', 'RottenApple', 'RottenBanana', 'RottenBellpepper', 'RottenCarrot', 
+    'RottenCucumber', 'RottenMango', 'RottenOrange', 'RottenPotato', 'RottenStrawberry', 'RottenTomato']
+
+# Function for inference
+def classify_fruit(image):
+    inputs = feature_extractor(images=image, return_tensors="pt").to(device)
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits
+        predicted_class_idx = logits.argmax(-1).item()
+    return predicted_classes[predicted_class_idx]
+
+# Gradio UI
+demo = gr.Interface(
+    fn=classify_fruit,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Label(),
+    title="Fruit Ripeness Detection",
+    description="Upload an image of a fruit to determine whether it's fresh or rotten."
+)
+
+demo.launch()