Create app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+import tensorflow_hub as hub
+from PIL import Image
+import numpy as np
+from tensorflow.keras.preprocessing.image import img_to_array, load_img
+
+# Loading Saved Model
+model = tf.keras.models.load_model('plant-disease-classifier.h5')
+
+# Defining function for predictions
+def predict(input_image):
+    try:
+        input_image = img_to_array(input_image) # Converting picture to array
+
+        # Preprocessing 
+        input_image = np.resize(input_image, (256,256,3))
+        input_image = np.array(input_image).astype(np.float32) / 255.0
+        input_image = np.expand_dims(input_image, axis = 0)
+
+        prediction = model.predict(input_image)
+
+        labels = ['Healthy', 'Powdery', 'Rust']
+
+        preds_class = np.argmax(preds)
+        preds_label = labels[preds_class]
+
+        output = f"Predicted Class: {preds_label} <br><br> Confidence Score: {preds[0][preds_class]}"
+
+        return output
+    except Exception as e:
+        return str(e)
+
+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 classify its status!"""",
+    examples = examples
+)
+
+iface.launch