Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+from torchvision import transforms
+from PIL import Image
+
+# Load the model using Hugging Face pipeline
+MODEL_NAME = "dwililiya/sugarcane-plant-diseases-classification"
+classifier = pipeline("image-classification", model=MODEL_NAME)
+
+# Define class names based on your dataset
+class_names = ['Bacterial Blight', 'Healthy', 'Mosaic', 'Red Rot', 'Rust', 'Yellow']
+
+def predict(image):
+    # Use the classifier to predict
+    predictions = classifier(image)
+    
+    # Get the predicted class and confidence score
+    predicted_class = predictions[0]['label']
+    confidence = predictions[0]['score']
+
+    return predicted_class, confidence
+
+# Gradio interface
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.inputs.Image(type="file", label="Upload Sugarcane Leaf Image"),
+    outputs=[gr.outputs.Label(num_top_classes=1, label="Predicted Class"),
+             gr.outputs.Textbox(label="Confidence Score")],
+    title="Sugarcane Plant Diseases Classification",
+    description="Upload an image of a sugarcane leaf to classify its disease.",
+)
+
+if __name__ == "__main__":
+    iface.launch()