Add application file

app.py

@@ -1,47 +0,0 @@
-import tensorflow as tf
-import numpy as np
-import gradio as gr
-import imageio
-
-# Load the pre-trained model
-model = tf.keras.models.load_model("D:/asser/CNN model/Tomatoleaf_CNN.h5")
-
-# Define the class labels
-class_names = [
-    'Tomato___Late_blight',
-    'Tomato___healthy',
-    'Tomato___Early_blight',
-    'Tomato___Septoria_leaf_spot',
-    'Tomato___Tomato_Yellow_Leaf_Curl_Virus',
-    'Tomato___Bacterial_spot',
-    'Tomato___Target_Spot',
-    'Tomato___Tomato_mosaic_virus',
-    'Tomato___Leaf_Mold',
-    'Tomato___Spider_mites Two-spotted_spider_mite'
-]
-
-# Define the prediction function
-def predict(image):
-    # Read and preprocess the image
-    img = imageio.imread(image)  # Load the image
-    img = tf.image.resize(img, (128, 128)) / 255.0  # Resize and normalize
-    img = np.expand_dims(img, axis=0)  # Expand dimensions to match model input
-
-    # Predict using the model
-    predictions = model.predict(img)
-    prediction_probabilities = predictions[0]
-
-    # Map the predictions to the class labels
-    return {class_names[i]: float(prediction_probabilities[i]) for i in range(len(class_names))}
-
-# Gradio Interface
-interface = gr.Interface(
-    fn=predict,
-    inputs=gr.Image(type="file"),  # Accept an image file as input
-    outputs=gr.Label(),  # Return class labels with probabilities
-    title="Tomato Leaf Disease Detection",
-    description="Upload an image of a tomato leaf to identify its condition."
-)
-
-if __name__ == "__main__":
-    interface.launch()