Create app.py

app.py

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+import gradio as gr
+import numpy as np
+import tensorflow as tf
+from tensorflow import keras
+from PIL import Image
+
+model = keras.models.load_model("skinCancerClassification_UsningCNN.h5")
+
+class_labels = {
+    0: 'dermatofibroma',
+    1: 'melanoma',
+    2: 'nevus',
+    3: 'seborrheic keratosis',
+    4: 'squamous cell carcinoma',
+    5: 'pigmented benign keratosis',
+    6: 'basal cell carcinoma',
+    7: 'vascular lesion',
+    8: 'actinic keratosis'
+}
+
+def classify_skin_cancer(image):
+    # Preprocess the image
+    image = np.array(image)
+    image = tf.image.resize(image, (75, 100))  # Resize to match model input shape
+    image = np.expand_dims(image, axis=0)
+
+    predictions = model.predict(image)
+    class_index = np.argmax(predictions)
+    class_name = class_labels[class_index]
+
+    confidence = np.max(predictions)
+
+    return f"Predicted Class: {class_name}\nConfidence: {confidence:.2f}"
+
+iface = gr.Interface(
+    fn=classify_skin_cancer,
+    inputs="image",
+    outputs="text",
+    live=True,
+    title="Skin Cancer Classification",
+    description="Upload an image of a skin lesion to classify its type."
+)
+
+iface.launch()