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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
+xception.keras filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,41 @@
+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+ 
+model_path = "xception.keras"
+model = tf.keras.models.load_model(model_path)
+ 
+# Define the core prediction function
+def predict_tumor(image):
+    # Preprocess image
+    print(type(image))
+    image = Image.fromarray(image.astype('uint8'))  # Convert numpy array to PIL image
+    image = image.resize((150, 150)) #resize the image to 28x28 and converts it to gray scale
+    image = np.array(image)
+    image = np.expand_dims(image, axis=0) # same as image[None, ...]
+   
+    # Predict
+    prediction = model.predict(image)
+ 
+    # No need to apply sigmoid, as the output layer already uses softmax
+    # Convert the probabilities to rounded values
+    prediction = np.round(prediction, 2)
+ 
+    # Separate the probabilities for each class
+    p_non_tumor = prediction[0][0]  # Probability for class 'charmander'
+    tumor = prediction[0][1]   # Probability for class 'mewto'
+ 
+    return {'non_tumor': p_non_tumor, 'mewto': tumor}
+ 
+ 
+# Create the Gradio interface
+input_image = gr.Image()
+iface = gr.Interface(
+    fn=predict_tumor,
+    inputs=input_image,
+    outputs=gr.Label(),
+    examples=["images/1 no.jpeg", "images/3 no.jpg", "images/2 no.jpeg", "images/5 no.jpg", "images/4 no.jpg", "images/Y1.jpg", "images/Y2.jpg", "images/Y7.jpg", "images/Y4.jpg", "images/Y8.jpg"],  
+    description="TEST.")
+ 
+iface.launch()

xception.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7246cf7663baec611adec994e23e9622886524c660cc368806fc1fd500ec19bc
+size 250535559