Create app.py

app.py

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+import gradio as gr
+import numpy as np
+from tensorflow.keras.models import load_model
+from PIL import Image
+
+# Load the trained model
+model = load_model('skin_model.h5')
+
+# Define a function to make predictions
+def predict(name, age, image):
+    # Preprocess the image
+    image = Image.fromarray(image)
+    image = image.resize((150, 150))
+    image = np.array(image) / 255.0
+    image = np.expand_dims(image, axis=0)
+    
+    # Make a 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 f"Patient: {name}, Age: {age}, Prediction: {label}"
+
+# Define input and output components
+name_input = gr.inputs.Text(label="Patient's Name")
+age_input = gr.inputs.Text(label="Patient's Age")
+image_input = gr.inputs.Image(shape=(150, 150))
+label_output = gr.outputs.Label()
+
+# Define a Gradio interface for user interaction
+iface = gr.Interface(
+    fn=predict,
+    inputs=[name_input, age_input, image_input],
+    outputs=label_output,
+    title="Skin Cancer Classification Chatbot",
+    description="Predicts whether a skin image is cancerous or not based on patient's name, age, and lesion image.",
+    theme="default",  # Choose a theme: "default", "compact", "huggingface"
+    layout="vertical",  # Choose a layout: "vertical", "horizontal", "double"
+    live=False  # Set to True for live updates without clicking "Submit"
+)
+
+iface.launch()