Add app and model scripts

app.py

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+import gradio as gr
+from model import predict
+
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Label(num_top_classes=1),
+    title="Plant Disease Classifier",
+    description="Upload a leaf image and the model predicts the disease."
+)
+
+if __name__ == "__main__":
+    iface.launch()

model.py

@@ -0,0 +1,34 @@
+from tensorflow.keras.applications import MobileNetV2
+from tensorflow.keras.layers import GlobalAveragePooling2D, Dense
+from tensorflow.keras.models import Model
+
+import tensorflow as tf
+import numpy as np
+from PIL import Image  # <-- Add this import
+
+
+def build_model(num_classes):
+    base_model = MobileNetV2(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
+    base_model.trainable = False
+
+    x = base_model.output
+    x = GlobalAveragePooling2D()(x)
+    x = Dense(128, activation='relu')(x)
+    output = Dense(num_classes, activation='softmax')(x)
+
+    model = Model(inputs=base_model.input, outputs=output)
+    return model
+
+def predict(image: Image.Image):
+    """
+    Predict the class of a given PIL image.
+    """
+    image = image.resize((224, 224))
+    img_array = np.array(image) / 255.0
+    img_array = np.expand_dims(img_array, axis=0)
+
+    predictions = model.predict(img_array)
+    predicted_class = class_names[np.argmax(predictions)]
+    confidence = float(np.max(predictions))
+
+    return {predicted_class: confidence}