Update app.py

app.py

@@ -1,36 +1,35 @@
-import gradio as gr
+import tensorflow as tf
+from huggingface_hub import snapshot_download
+from keras.preprocessing import image
 import numpy as np
+import gradio as gr
 from keras.layers import TFSMLayer
-from tensorflow.keras.preprocessing import image
-from PIL import Image
 
-# Load the model from Hugging Face using TFSMLayer
-model = TFSMLayer("https://huggingface.co/syaha/skin_cancer_detection_model/resolve/main", call_endpoint="serving_default")
+# Download the model from Hugging Face Space to a local path
+local_model_path = snapshot_download("syaha/skin_cancer_detection_model")
+
+# Load the model using TFSMLayer
+model = TFSMLayer(local_model_path, call_endpoint="serving_default")
 
-# Class names used in the model
+# Class names for skin cancer classification (assuming you already know these)
 class_names = ['akiec', 'bcc', 'bkl', 'df', 'nv', 'vasc', 'mel']
 
-def predict(img: Image.Image):
-    img = img.resize((224, 224))
+# Define a prediction function
+def predict_skin_cancer(image_path):
+    # Preprocess the image for prediction
+    img = image.load_img(image_path, target_size=(224, 224))  # Resize to 224x224 (or use the correct input size)
     img_array = image.img_to_array(img)
-    img_array = np.expand_dims(img_array, axis=0) / 255.0
-
-    predictions = model(img_array, training=False).numpy()
-    predicted_class = np.argmax(predictions, axis=1)[0]
-    label = class_names[predicted_class]
-    confidence = float(predictions[0][predicted_class])
-
-    return {
-        "label": label,
-        "confidence": round(confidence * 100, 2)
-    }
-
-iface = gr.Interface(
-    fn=predict,
-    inputs=gr.Image(type="pil"),
-    outputs="json",
-    title="Skin Cancer Classifier",
-    description="Upload a skin lesion image to get a prediction"
-)
+    img_array = np.expand_dims(img_array, axis=0) / 255.0  # Normalize the image
+
+    # Make prediction
+    predictions = model.predict(img_array)
+    predicted_class = np.argmax(predictions, axis=1)[0]  # Get the class index
+    predicted_label = class_names[predicted_class]  # Map the index to the class name
+
+    return f"Predicted class: {predicted_label}"
+
+# Set up Gradio interface to interact with the model
+iface = gr.Interface(fn=predict_skin_cancer, inputs=gr.Image(type="filepath"), outputs="text", live=True)
 
+# Launch the Gradio interface
 iface.launch()