Create app.py

app.py

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+import tensorflow as tf 
+import gradio as gr
+import numpy as np
+from PIL import Image
+from tensorflow.keras.models import load_model
+# Load your trained model and label map 
+path="skinDiseaseDetection (1).h5"
+model = load_model(path)  
+label_map={0: 'pigmented benign keratosis',
+ 1: 'melanoma',
+ 2: 'vascular lesion',
+ 3: 'actinic keratosis',
+ 4: 'squamous cell carcinoma',
+ 5: 'basal cell carcinoma',
+ 6: 'seborrheic keratosis',
+ 7: 'dermatofibroma',
+ 8: 'nevus'}
+
+def predict_image(image):
+    """
+    Predict the class of an uploaded image of a skin lesion.
+
+    Parameters:
+    - image: The uploaded image in PIL format.
+
+    Returns:
+    - predicted_class: The name of the predicted class or an error message.
+    - confidence: The confidence score of the prediction (0 to 1) or None.
+    - prediction_type: The type of prediction or an error message.
+    """
+    if image is None:
+        return "Please upload an image.", None, "No Diagnosis"
+
+    # Preprocess the image
+    image = image.resize((100, 75))  # Resize to model's expected input size
+    image_array = np.asarray(image)
+    image_array = (image_array - np.mean(image_array)) / np.std(image_array)  # Normalize
+    image_array = np.expand_dims(image_array, axis=0)  # Add batch dimension
+
+    # Make prediction using the loaded model
+    predictions = model.predict(image_array)
+    predicted_index = np.argmax(predictions, axis=1)[0]
+    confidence = np.max(predictions)
+    predicted_class = label_map[predicted_index]
+
+    # Define classes for benign and malignant lesions
+    benign_classes = [
+        'pigmented benign keratosis', 
+        'vascular lesion', 
+        'actinic keratosis',
+        'seborrheic keratosis', 
+        'dermatofibroma', 
+        'nevus'
+    ]
+    malignant_classes = [
+        'melanoma', 
+        'squamous cell carcinoma', 
+        'basal cell carcinoma'
+    ]
+
+    # Determine the type of prediction
+    if predicted_class in benign_classes:
+        prediction_type = 'Benign Neoplasm'
+    elif predicted_class in malignant_classes:
+        prediction_type = 'Malignant Neoplasm'
+    else:
+        prediction_type = 'Unknown Neoplasm'
+
+    return predicted_class, float(f"{confidence:.2f}"), prediction_type  # Format confidence here
+
+# Example images and their descriptions
+examples = [
+    ["/content/sample_data/bcc.jpg"],
+    ["/content/sample_data/d.jpg"],
+    ["/content/sample_data/m.PNG"],
+    ["/content/sample_data/sck.jpg"]
+]
+
+# Example images and their descriptions
+examples = [
+    ["/content/sample_data/bcc.jpg"],
+    ["/content/sample_data/d.jpg"],
+    ["/content/sample_data/m.PNG"],
+    ["/content/sample_data/sck.jpg"]
+]
+
+# Set up the Gradio interface
+iface = gr.Interface(
+    fn=predict_image,
+    inputs=gr.Image(type="pil", label="Upload Skin Lesion Image"),
+    outputs=[
+        gr.Label(label="Predicted Class"),
+        gr.Number(label="Confidence Score", precision=2),
+        gr.Textbox(label="Diagnosis Type", interactive=False)
+    ],
+    title="Skin Cancer Image Classification",
+    description="Upload an image of a skin lesion to predict its type and determine if the diagnosis is Benign or Malignant.",
+    theme="default",  # Use a valid theme like "default" or "compact"
+    allow_flagging="never",  # Optional: Disable flagging for a cleaner UI
+    examples=examples ,
+    css="""
+    .gradio-container {
+        font-family: 'Arial', sans-serif;
+        background-color: #ddebf7;
+        border-radius: 10px;
+        padding: 20px;
+    }
+    .gr-button {
+        background-color: #4CAF50;
+        color: white;
+    }
+    .gr-image {
+        border: 2px solid #ddd;
+        border-radius: 10px;
+        box-shadow: 0 4px 8px rgba(0, 0, 0, 0.2);
+    }
+    .output-textbox {
+        font-size: 1.2em;
+        text-align: center;
+        color: #333;
+    }
+    """,
+)
+
+# Launch the Gradio interface
+iface.launch()