Update app.py

app.py

@@ -1,11 +1,10 @@
-import os
-import cv2
-import numpy as np
-import gradio as gr
-from PIL import Image
-import tensorflow as tf
+# import os
+# import numpy as np
+# import gradio as gr
+# import tensorflow as tf
 
 # 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(),
@@ -23,33 +22,46 @@ import tensorflow as tf
 #              loss=tf.keras.losses.CategoricalCrossentropy(),
 #              metrics=[tf.keras.metrics.MeanSquaredError(), tf.keras.metrics.AUC(), tf.keras.metrics.CategoricalAccuracy()])
 
-# model.load_model("my_model.keras")
+# model = tf.keras.models.load_model('my_model.h5', compile=False)
 
-
-# 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
+# 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.image.resize(image_tensor, (28, 28)),
+#     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)"
+
+# example_list = [["examples/" + example] for example in os.listdir("examples")]
+
+# interface = gr.Interface(fn=classify_image,
+#                           inputs=gr.Image(type="pil"),
+#                           # inputs='sketchpad',
+#                           outputs=gr.Label(num_top_classes=3, label="Predictions"),
+#                           examples=example_list, 
+#                           title=title,
+#                           description=description,
+#                           article=article)
+                        
+# interface.launch()
+
+
+
+
+
+
+
 
-# demo = gr.Interface(
-#   fn=image_mod, 
-#   inputs='sketchpad',
-#   outputs='text',
-#   title=title,
-#   description=description,
-#   live=True)
 
-# demo.launch(share=False)
-# demo.launch(debug=True)
 
 
 
@@ -73,28 +85,26 @@ import tensorflow as tf
 
 
 
-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', compile=False)
+
+
+
+
+
+
+
+
+
+import os
+import numpy as np
+import gradio as gr
+import tensorflow as tf
 
 def classify_image(image):
+    print(type(image))
+    print(image)
+    print(np.array(image).shape)
     if len(np.array(image).shape) == 3:
         image = tf.image.rgb_to_grayscale(image)
     image_tensor = tf.convert_to_tensor(image)
@@ -114,8 +124,8 @@ article = "for source code you can visit [my github](https://github.com/mralamda
 example_list = [["examples/" + example] for example in os.listdir("examples")]
 
 interface = gr.Interface(fn=classify_image,
-                          inputs=gr.Image(type="pil"),
-                          # inputs='sketchpad',
+                          # inputs=gr.Image(type="pil"),
+                          inputs='sketchpad',
                           outputs=gr.Label(num_top_classes=3, label="Predictions"),
                           examples=example_list, 
                           title=title,