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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+best_model_InceptionV2.keras filter=lfs diff=lfs merge=lfs -text

app.py

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+import gradio as gr
+import tensorflow as tf
+from tensorflow.keras.applications.inception_resnet_v2 import preprocess_input
+from tensorflow.keras.preprocessing import image
+import numpy as np
+from huggingface_hub import hf_hub_download
+
+# Download model from Hugging Face Space
+model_path = hf_hub_download(repo_id="Suphawan/Melanoma", filename="best_model_InceptionV2.keras")
+
+# Load the trained InceptionV2 model
+try:
+    model = tf.keras.models.load_model(model_path)
+    print("Model loaded successfully.")
+except OSError as e:
+    print(f"Error loading model: {e}")
+    model = None  # Handle this case appropriately in your code
+
+# Function for prediction
+def predict(img):
+    if model is None:
+        return "Model could not be loaded."
+    
+    try:
+        img_resized = img.resize((224, 224))  # Resize image to the target size
+        img_array = image.img_to_array(img_resized)  # Convert image to array
+        img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
+        img_array = preprocess_input(img_array)  # Preprocess image according to model requirements
+
+        predictions = model.predict(img_array)
+        class_idx = np.argmax(predictions, axis=1)[0]
+        class_labels = ['Benign', 'Malignant']  # Update according to your class labels
+        class_label = class_labels[class_idx]
+        confidence = float(predictions[0][class_idx])
+
+        return f"Class: {class_label}, Confidence: {confidence:.2f}"
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+# Define the Gradio app
+with gr.Blocks() as demo:
+    gr.Markdown("Image Classification with InceptionV2")
+    
+    with gr.Row():
+        with gr.Column():
+            classify_input = gr.Image(type="pil", label="Upload an Image")
+            classify_button = gr.Button("Classify!")
+        with gr.Column():
+            classify_output = gr.Textbox(label="Classification Result")
+
+    classify_button.click(
+        predict,
+        inputs=[classify_input],
+        outputs=[classify_output]
+    )
+
+demo.launch(debug=True)

best_model_InceptionV2.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dbf8d3154db9acd877f922cfa6f0f425c552972c1a5c62f93b708ffa7db114ef
+size 656891655

requirements.txt

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+tensorflow==2.11.0
+numpy==1.21.6
+Pillow==9.2.0
+gradio==3.6.0
+httpx==0.21.1
+seaborn==0.11.2
+matplotlib==3.5.1
+scikit-learn==1.1.3
+Cython==0.29.32