Update app.py

app.py

@@ -26,27 +26,98 @@ import tensorflow as tf
 # model.load_model("my_model.keras")
 
 
-def image_mod(image):
+# def image_mod(image):
     
-    # img = Image.fromarray(image['composite'])
-    model = tf.keras.models.load_model('weights_1.h5')
-    test_img = np.array(image['composite']).reshape(1, 28, 28, 1)
-    # test_img = cv2.resize(np.array(image['composite']), (28, 28, 1))
-    prediction = model.predict(test_img)
-    pred   = np.argmax(prediction, axis=1)[0]
-    return pred
+#     # img = Image.fromarray(image['composite'])
+#     model = tf.keras.models.load_model('weights_1.h5')
+#     test_img = np.array(image['composite']).reshape(1, 28, 28, 1)
+#     # test_img = cv2.resize(np.array(image['composite']), (28, 28, 1))
+#     prediction = model.predict(test_img)
+#     pred   = np.argmax(prediction, axis=1)[0]
+#     return pred
+
+
+# title = "Draw to Search"
+# description = "Using the power of AI to detect the number you draw!"
+
+# demo = gr.Interface(
+#   fn=image_mod, 
+#   inputs='sketchpad',
+#   outputs='text',
+#   title=title,
+#   description=description,
+#   live=True)
+
+# demo.launch(share=False)
+# demo.launch(debug=True)
+
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+model = tf.keras.models.Sequential([
+    tf.keras.layers.Input(shape=(28, 28, 1)),
+    tf.keras.layers.Conv2D(filters=16, kernel_size=3, strides=1, padding='same', activation='relu', input_shape=(28, 28, 1)),
+    tf.keras.layers.Conv2D(filters=16, kernel_size=3, strides=1, padding='same', activation='relu'),
+    tf.keras.layers.BatchNormalization(),
+    tf.keras.layers.Conv2D(filters=32, kernel_size=3, strides=1, padding='same', activation='relu'),
+    tf.keras.layers.Conv2D(filters=32, kernel_size=3, strides=1, padding='same', activation='relu'),
+    tf.keras.layers.BatchNormalization(),
+    tf.keras.layers.Conv2D(filters=64, kernel_size=3, strides=2, padding='same', activation='relu'),
+    tf.keras.layers.Conv2D(filters=64, kernel_size=3, strides=2, padding='same', activation='relu'),
+    tf.keras.layers.BatchNormalization(),
+    tf.keras.layers.GlobalAveragePooling2D(),
+    tf.keras.layers.Dense(10, activation='softmax')
+])
+
+model.compile(optimizer=tf.keras.optimizers.Adam(),
+             loss=tf.keras.losses.CategoricalCrossentropy(),
+             metrics=[tf.keras.metrics.MeanSquaredError(), tf.keras.metrics.AUC(), tf.keras.metrics.CategoricalAccuracy()])
+
+model = tf.keras.models.load_model('my_model.h5')
+
+def classify_image(image):
+    if len(np.array(image).shape) == 3:
+        image = tf.image.rgb_to_grayscale(image)
+    image_tensor = tf.convert_to_tensor(image)
+    image_tensor = tf.cast(image_tensor, tf.float32)
+    image_tensor = tf.expand_dims(image_tensor, 0)
+    image_tensor = image_tensor / 255.0
+    prediction = model.predict(image_tensor)
+    prediction_label = str(prediction.argmax())
+    return prediction_label
 
 
 title = "Draw to Search"
 description = "Using the power of AI to detect the number you draw!"
+article = "for source code you can visit [my github](https://github.com/mralamdari)"
 
-demo = gr.Interface(
-  fn=image_mod, 
-  inputs='sketchpad',
-  outputs='text',
-  title=title,
-  description=description,
-  live=True)
+example_list = [["examples/" + example] for example in os.listdir("examples")]
 
-demo.launch(share=False)
-# demo.launch(debug=True)
+interface = gr.Interface(fn=classify_image,
+                          inputs=gr.Image(type="pil"),
+                          outputs=gr.Label(num_top_classes=3, label="Predictions"),
+                          examples=example_list, 
+                          title=title,
+                          description=description,
+                          article=article)
+                        
+interface.launch()