Create app.py

app.py

@@ -0,0 +1,57 @@
+import gradio as gr
+import torch
+from transformers import ViTForImageClassification, ViTFeatureExtractor
+from PIL import Image
+
+# Load model and feature extractor
+model = ViTForImageClassification.from_pretrained('Dhahlan2000/freshness_detector_updated', num_labels=30, ignore_mismatched_sizes=True)
+feature_extractor = ViTFeatureExtractor.from_pretrained('Dhahlan2000/freshness_detector_updated')
+
+# Move to GPU if available
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = model.to(device)
+
+# Class labels (modify according to your model)
+class_labels = [
+    "Overripe", "Ripe", "Rotten", "Unripe", 
+    # Add all 30 class labels here
+]
+
+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.jpg"],
+    ["banana.jpg"],
+    ["tomato.jpg"]
+]
+
+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()