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app.py

@@ -3,43 +3,45 @@ 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 PIL import Image
 
-model = tf.keras.models.load_model('best_model_InceptionV3.keras')
+# กำหนดเลเยอร์ที่กำหนดเอง (CustomScaleLayer)
+class CustomScaleLayer(tf.keras.layers.Layer):
+    def __init__(self, scale=1.0, **kwargs):
+        super(CustomScaleLayer, self).__init__(**kwargs)
+        self.scale = scale
+
+    def call(self, inputs, *args, **kwargs):
+        if isinstance(inputs, list):
+            return [input_tensor * self.scale for input_tensor in inputs]
+        else:
+            return inputs * self.scale
+
+# ใช้ custom_object_scope เพื่อทำให้เลเยอร์ที่กำหนดเองสามารถใช้งานได้
+with tf.keras.utils.custom_object_scope({'CustomScaleLayer': CustomScaleLayer}):
+    model = tf.keras.models.load_model("jo33_model_v222.h5")
 
 # Function for prediction
 def predict(img):
-    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 in prediction: {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()
+    img = img.resize((224, 224))  # Resize image to the target size
+    img_array = image.img_to_array(img)  # 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_label = list(train_generator.class_indices.keys())[class_idx]
+    confidence = predictions[0][class_idx]
+
+    return {class_label: confidence}
+
+# Create Gradio Interface
+interface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil", label="Upload an Image"),
+    outputs=gr.Label(num_top_classes=2, label="Predicted Class"),
+    title="Image Classification with InceptionResNetV2",
+    description="Upload an image to classify it into one of the classes."
+)
+
+# Launch the interface
+interface.launch()

jo33_model_v222.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0b0cac9d667bdc51caba249e3ac5325ae2acd0cd91e57a058770fafc5bbbab30
+size 656724656