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app.py

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+import gradio as gr
+import numpy as np
+from tensorflow.keras.models import load_model
+
+# Load the trained model
+model = load_model('skin_model.h5')
+
+# Define a function to make predictions
+def predict(image):
+    # Preprocess the image
+    image = image / 255.0
+    image = np.expand_dims(image, axis=0)
+    
+    # Make prediction using the model
+    prediction = model.predict(image)
+    
+    # Get the predicted class label
+    if prediction[0][0] < 0.5:
+        label = 'benign'
+    else:
+        label = 'malignant'
+    
+    return label
+
+examples=[["benign.jpg"], ["malignant.jpg"]]
+
+# Define a Gradio interface for user interaction
+image_input = gr.inputs.Image(shape=(150, 150))
+label_output = gr.outputs.Label()
+
+iface= gr.Interface(fn=predict, inputs=image_input, outputs=label_output, examples=examples,
+             title="Identifying Skin Cancer", description="Predicts whether an image of skin is cancerous or not")
+
+iface.launch()

requirements.txt

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+tensorflow

skin_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9a84edf274d1f6c849828d2f9d236df965a5c7bcd35c68049b6c92187e8bc1b8
+size 57212968