Create app.py

app.py

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+import gradio as gr
+import numpy as np
+import tensorflow as tf
+from PIL import Image
+
+# Load your pre-trained model (upload the .h5 file to the repo)
+model = tf.keras.models.load_model('best_plant_model.h5')
+
+# Full 38 class names from the dataset
+CLASS_NAMES = [
+    "Apple___Apple_scab",
+    "Apple___Black_rot",
+    "Apple___Cedar_apple_rust",
+    "Apple___healthy",
+    "Blueberry___healthy",
+    "Cherry_(including_sour)___Powdery_mildew",
+    "Cherry_(including_sour)___healthy",
+    "Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot",
+    "Corn_(maize)___Common_rust_",
+    "Corn_(maize)___Northern_Leaf_Blight",
+    "Corn_(maize)___healthy",
+    "Grape___Black_rot",
+    "Grape___Esca_(Black_Measles)",
+    "Grape___Leaf_blight_(Isariopsis_Leaf_Spot)",
+    "Grape___healthy",
+    "Orange___Haunglongbing_(Citrus_greening)",
+    "Peach___Bacterial_spot",
+    "Peach___healthy",
+    "Pepper,_bell___Bacterial_spot",
+    "Pepper,_bell___healthy",
+    "Potato___Early_blight",
+    "Potato___Late_blight",
+    "Potato___healthy",
+    "Raspberry___healthy",
+    "Soybean___healthy",
+    "Squash___Powdery_mildew",
+    "Strawberry___Leaf_scorch",
+    "Strawberry___healthy",
+    "Tomato___Bacterial_spot",
+    "Tomato___Early_blight",
+    "Tomato___Late_blight",
+    "Tomato___Leaf_Mold",
+    "Tomato___Septoria_leaf_spot",
+    "Tomato___Spider_mites Two-spotted_spider_mite",
+    "Tomato___Target_Spot",
+    "Tomato___Tomato_Yellow_Leaf_Curl_Virus",
+    "Tomato___Tomato_mosaic_virus",
+    "Tomato___healthy"
+]
+
+# Prediction function
+def predict_disease(image):
+    try:
+        # Preprocess: resize to 224x224, RGB, normalize to [0,1]
+        img = image.convert('RGB')
+        img = img.resize((224, 224))
+        img_array = np.array(img) / 255.0
+        img_array = np.expand_dims(img_array, axis=0)
+        # Predict
+        prediction = model.predict(img_array)
+        disease_class_idx = np.argmax(prediction[0])
+        confidence = prediction[0][disease_class_idx]
+        disease_class = CLASS_NAMES[disease_class_idx]
+        return {
+            "predicted_disease": disease_class,
+            "confidence": float(confidence)
+        }
+    except Exception as e:
+        return {"error": str(e)}
+
+# Gradio interface
+with gr.Blocks(title="Plant Disease Detector") as demo:
+    gr.Markdown("# 🌿 Plant Disease Detector")
+    gr.Markdown("Upload a plant leaf image to detect diseases across 14 crops (38 classes).")
+   
+    with gr.Row():
+        with gr.Column():
+            image_input = gr.Image(type="pil", label="Upload Leaf Image")
+            predict_btn = gr.Button("Detect Disease", variant="primary")
+       
+        with gr.Column():
+            output = gr.JSON(label="Prediction Result")
+   
+    predict_btn.click(
+        fn=predict_disease,
+        inputs=image_input,
+        outputs=output
+    )
+
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=7860)