Upload app.py

app.py

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+import gradio as gr
+from tensorflow.keras.models import load_model
+import numpy as np
+import tensorflow as tf
+
+model = load_model("skin_cancer_model.h5")
+
+def predict_image(image):
+    img = tf.image.resize(image, (224, 224))
+    img = np.expand_dims(img, axis=0) / 255.0
+
+    prediction = model.predict(img)
+    predicted_class = np.argmax(prediction, axis=1)[0]
+
+    class_names = ['akiec', 'bcc', 'bkl', 'df', 'nv', 'vasc', 'mel']
+    disease_info = {
+        'akiec': "Actinic Keratoses and Intraepithelial Carcinoma (pre-cancerous lesion)",
+        'bcc': "Basal Cell Carcinoma (a common type of skin cancer)",
+        'bkl': "Benign Keratosis (non-cancerous lesion)",
+        'df': "Dermatofibroma (benign skin lesion)",
+        'nv': "Melanocytic Nevus (a common mole)",
+        'vasc': "Vascular Lesions (benign lesion of blood vessels)",
+        'mel': "Melanoma (most dangerous type of skin cancer)"
+    }
+
+    return f"{class_names[predicted_class]}: {disease_info[class_names[predicted_class]]}"
+
+iface = gr.Interface(fn=predict_image, inputs="image", outputs="text")
+iface.launch()