Update app.py

app.py

@@ -1,23 +1,25 @@
-import gradio as gr
 import os
+import gradio as gr
 import tensorflow as tf
-from PIL import Image
 import numpy as np
-from tensorflow.keras.preprocessing.image import img_to_array, load_img
+
+os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
 
 # Loading saved model
 model = tf.keras.models.load_model('plant_disease_classifier.h5')
 
-# Defining function for predictions
 def predict(input_image):
     try:
+        # Preprocessing
         input_image = tf.convert_to_tensor(input_image)
         input_image = tf.image.resize(input_image, [256, 256])
         input_image = tf.expand_dims(input_image, 0) / 255.0
 
+     
         predictions = model.predict(input_image)
         labels = ['Healthy', 'Powdery', 'Rust']
 
+       
         class_idx = np.argmax(predictions)
         class_label = labels[class_idx]
         confidence = predictions[0][class_idx]
@@ -30,14 +32,14 @@ def predict(input_image):
 examples = ["Healthy.png", "Powdery.png", "Rust.png"]
 
 iface = gr.Interface(
-    fn = predict,
-    inputs = gr.inputs.Image(shape=(256,256)),
-    outputs = "text",
-    title = "🌿 Plant Disease Detection",
-    description='<br> This is a specialized Image Classification model engineered to identify the health status of plants, specifically detecting conditions of Powdery Mildew or Rust. <br> \
-    This model is based on a Convolutional Neural Network that I have trained, evaluated, and validated on my Kaggle Notebook: <a href="https://www.kaggle.com/code/lusfernandotorres/convolutional-neural-network-from-scratch">🧠 Convolutional Neural Network From Scratch</a>. <br> \
-    <br> Upload a photo of a plant to see how the model classifies its status!',
-    examples = examples
+    fn=predict,
+    inputs=gr.Interface.Image(shape=(256, 256)),
+    outputs="text",
+    title="🌿 Plant Disease Detection",
+    description='<br> This specialized Image Classification model identifies the health status of plants, pinpointing conditions like Powdery Mildew or Rust. <br>\
+    The model is engineered on a Convolutional Neural Network and has been rigorously trained, evaluated, and validated. Kaggle Notebook: <a href="https://www.kaggle.com/code/lusfernandotorres/convolutional-neural-network-from-scratch">🧠 Convolutional Neural Network From Scratch</a>. <br> \
+    <br> Upload a photo of a plant for classification!',
+    examples=examples
 )
 
-iface.launch
+iface.launch()