Create app.py

app.py

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+import gradio as gr
+from PIL import Image
+from transformers import ViTFeatureExtractor, ViTForImageClassification
+
+# Load the model and feature extractor
+feature_extractor = ViTFeatureExtractor.from_pretrained('wambugu1738/crop_leaf_diseases_vit')
+model = ViTForImageClassification.from_pretrained('wambugu1738/crop_leaf_diseases_vit')
+
+# Define prediction function
+def predict(image):
+    image = Image.fromarray(image)  # Convert image from numpy array to PIL Image
+    inputs = feature_extractor(images=image, return_tensors="pt")
+    outputs = model(**inputs)
+    logits = outputs.logits
+    predicted_class_idx = logits.argmax(-1).item()
+    return model.config.id2label[predicted_class_idx]
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.inputs.Image(type="numpy"),  # Input type as a numpy array
+    outputs="text",
+    title="Crop Disease Detection",
+    description="Upload an image of a crop leaf to detect diseases."
+)
+
+iface.launch()