Create app.py file

app.py

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+import gradio as gr
+import numpy as np
+from PIL import Image
+import tensorflow as tf
+from keras.preprocessing import image
+from keras.models import load_model
+
+# Load the trained model
+model_path = 'SCDSNet-H10K_Model-1.keras'
+model = load_model(model_path)
+
+# Define the class labels
+classes = ['akiec', 'bcc', 'bkl', 'df', 'melanoma', 'nv', 'vasc']
+
+# Function to preprocess the image
+def preprocess_image(image_bytes):
+    img = Image.open(image_bytes).convert('RGB')
+    img = img.resize((32, 32))
+    img_array = image.img_to_array(img)
+    img_array = np.expand_dims(img_array, axis=0)
+    img_array = img_array / 255.0
+    return img_array
+
+# Function to predict class label and probability
+def predict_image(image_bytes):
+    img_array = preprocess_image(image_bytes)
+    predictions = model.predict(img_array)
+    score = tf.nn.softmax(predictions[0])
+    predicted_class = classes[np.argmax(score)]
+    return predicted_class
+
+# Create a Gradio interface
+iface = gr.Interface(
+    fn=predict_image, 
+    inputs="file",
+    outputs=["label"],
+    title="Skin Cancer Classification",
+    description="Upload an image of a skin lesion for classification."
+)
+
+# Launch the interface
+iface.launch()