Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+from PIL import Image
+import re
+
+# Load the pre-trained pipeline
+pipe = pipeline("image-classification", model="gianlab/swin-tiny-patch4-window7-224-finetuned-plantdisease")
+
+def predict_disease(image):
+    """Predicts the plant disease from an image."""
+    predictions = pipe(image)
+    
+    # Extract disease name using regex and get the top prediction
+    for pred in predictions:
+        label = pred["label"]
+        score = pred["score"]
+        match = re.search(r"___(.*)", label)
+        if match:
+            disease_name = match.group(1)
+            return {disease_name: score}  # Return only the top prediction as a dictionary
+        else:
+            return {label: score}  # If no '___' found, return original label
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=predict_disease,
+    inputs=gr.Image(type="pil"),
+    outputs="label",
+    title="Plant Disease Classifier",
+    description="Upload an image of a plant leaf to predict its disease."
+)
+
+# Launch the interface
+iface.launch()