Add application file

app.py

@@ -0,0 +1,77 @@
+# -*- coding: utf-8 -*-
+"""gradio.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1B520JUHmyofueyUqotN2yj6Gad69uavo
+"""
+
+import gradio as gr
+import tensorflow as tf
+
+model = tf.keras.models.load_model('my_final_model.keras')
+
+def predict_disease(image):
+    # Preprocess the image
+    img = image.reshape((-1, image.shape[0], image.shape[1], 3))
+    # Assuming your model expects images with 3 color channels
+
+    # Perform prediction
+    prediction = model.predict(img)
+
+    # Assuming prediction is a list of probabilities
+    labels = ['Early Blight', 'Late Blight', 'Healthy']
+    # Replace with your actual class labels
+
+    # Get predicted class
+    predicted_class = labels[prediction.argmax()]
+    return predicted_class
+
+iface = gr.Interface(
+    fn=predict_disease,
+    inputs=gr.Image(shape=(224, 224)),  # Adjust shape according to model's input
+    outputs="text",
+    title="Potato Disease Classification",
+    description="Upload an image of a potato leaf to classify its disease.",
+)
+iface.launch()
+
+def predict_disease(image):
+    """
+    Preprocesses the image, performs prediction, and returns the predicted class.
+
+    Args:
+        image: The input image as a NumPy array.
+
+    Returns:
+        The predicted class label as a string.
+    """
+
+    # Resize the image using TensorFlow
+    img = tf.image.resize(image, [224, 224]) # Resize image using TensorFlow
+    # Add batch dimension
+    img = tf.expand_dims(img, axis=0)
+
+    # Assuming your model expects images with 3 color channels
+    # Perform prediction
+    prediction = model.predict(img)
+
+    # Assuming prediction is a list of probabilities
+    labels = ['Early Blight', 'Late Blight', 'Healthy']
+    # Replace with your actual class labels
+
+    # Get predicted class
+    predicted_class = labels[prediction.argmax()]
+    return predicted_class
+
+# ... (Rest of your code) ...
+
+iface = gr.Interface(
+    fn=predict_disease,
+    inputs=gr.Image(),  # Remove shape argument
+    outputs="text",
+    title="Potato Disease Classification",
+    description="Upload an image of a potato leaf to classify its disease.",
+)
+iface.launch()