adding examples

.gitattributes

@@ -26,3 +26,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+weights.h5 filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,13 +1,5 @@
----
-title: ACSR
-emoji: 📊
-colorFrom: red
-colorTo: indigo
-sdk: gradio
-sdk_version: 2.9.1
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
+The weights of the model aren't here, download them first and put them in the same directory as `acsr.py`
+
+```bash
+$ wget 'https://raw.githubusercontent.com/mhmoodlan/arabic-font-classification/master/codebase/code/font_classifier/weights/FontModel_RuFaDataset_cnn_weights(4).h5' -O weights.h5
+```

app.py

@@ -1,68 +1,77 @@
-
-# %%
-import gradio as gr
-import numpy as np
-# import random as rn
-# import os
-import tensorflow as tf
-import cv2
-
-# tf.config.experimental.set_visible_devices([], 'GPU')
-
-
-#%%
-def parse_image(image):
-    image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
-    image = cv2.resize(image, (100, 100))
-    image = image.astype(np.float32)
-    image = image / 255.0
-    image = np.expand_dims(image, axis=0)
-    image = np.expand_dims(image, axis=-1)
-    return image
-
-#%%
-
-def cnn(input_shape, output_shape):
-    num_classes = output_shape[0]
-    dropout_seed = 708090
-    kernel_seed = 42
-  
-    model = tf.keras.models.Sequential([
-      tf.keras.layers.Conv2D(16, 3, activation='relu', input_shape=input_shape, kernel_initializer=tf.keras.initializers.GlorotUniform(seed=kernel_seed)),
-      tf.keras.layers.MaxPooling2D(),
-      tf.keras.layers.Dropout(0.1, seed=dropout_seed),
-      tf.keras.layers.Conv2D(32, 5, activation='relu', kernel_initializer=tf.keras.initializers.GlorotUniform(seed=kernel_seed)),
-      tf.keras.layers.MaxPooling2D(),
-      tf.keras.layers.Dropout(0.1, seed=dropout_seed),
-      tf.keras.layers.Conv2D(64, 10, activation='relu', kernel_initializer=tf.keras.initializers.GlorotUniform(seed=kernel_seed)),
-      tf.keras.layers.MaxPooling2D(),
-      tf.keras.layers.Dropout(0.1, seed=dropout_seed),
-      tf.keras.layers.Flatten(),
-      tf.keras.layers.Dense(128, activation='relu', kernel_regularizer='l2', kernel_initializer=tf.keras.initializers.GlorotUniform(seed=kernel_seed)),
-      tf.keras.layers.Dropout(0.2, seed=dropout_seed),
-      tf.keras.layers.Dense(16, activation='relu', kernel_regularizer='l2', kernel_initializer=tf.keras.initializers.GlorotUniform(seed=kernel_seed)),
-      tf.keras.layers.Dropout(0.2, seed=dropout_seed),
-      tf.keras.layers.Dense(num_classes, activation='sigmoid', kernel_initializer=tf.keras.initializers.GlorotUniform(seed=kernel_seed))
-    ])
-
-    return model
-
-#%%
-model = cnn((100, 100, 1), (1,))
-model.compile(loss=tf.keras.losses.BinaryCrossentropy(from_logits=False), optimizer='Adam', metrics='accuracy')
-
-model.load_weights('weights.h5')
-
-#%%
-def segment(image):
-    image = parse_image(image)
-    # print(image.shape)
-    output = model.predict(image)
-    # print(output)
-    labels = {
-        "farsi" : 1-float(output),
-        "ruqaa" : float(output)
-    }
-    return labels
-
-iface = gr.Interface(fn=segment, inputs="image", outputs="label").launch()
+
+# %%
+import gradio as gr
+import numpy as np
+# import random as rn
+# import os
+import tensorflow as tf
+import cv2
+
+tf.config.experimental.set_visible_devices([], 'GPU')
+
+
+#%%
+def parse_image(image):
+    image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
+    image = cv2.resize(image, (100, 100))
+    image = image.astype(np.float32)
+    image = image / 255.0
+    image = np.expand_dims(image, axis=0)
+    image = np.expand_dims(image, axis=-1)
+    return image
+
+#%%
+
+def cnn(input_shape, output_shape):
+    num_classes = output_shape[0]
+    dropout_seed = 708090
+    kernel_seed = 42
+  
+    model = tf.keras.models.Sequential([
+      tf.keras.layers.Conv2D(16, 3, activation='relu', input_shape=input_shape, kernel_initializer=tf.keras.initializers.GlorotUniform(seed=kernel_seed)),
+      tf.keras.layers.MaxPooling2D(),
+      tf.keras.layers.Dropout(0.1, seed=dropout_seed),
+      tf.keras.layers.Conv2D(32, 5, activation='relu', kernel_initializer=tf.keras.initializers.GlorotUniform(seed=kernel_seed)),
+      tf.keras.layers.MaxPooling2D(),
+      tf.keras.layers.Dropout(0.1, seed=dropout_seed),
+      tf.keras.layers.Conv2D(64, 10, activation='relu', kernel_initializer=tf.keras.initializers.GlorotUniform(seed=kernel_seed)),
+      tf.keras.layers.MaxPooling2D(),
+      tf.keras.layers.Dropout(0.1, seed=dropout_seed),
+      tf.keras.layers.Flatten(),
+      tf.keras.layers.Dense(128, activation='relu', kernel_regularizer='l2', kernel_initializer=tf.keras.initializers.GlorotUniform(seed=kernel_seed)),
+      tf.keras.layers.Dropout(0.2, seed=dropout_seed),
+      tf.keras.layers.Dense(16, activation='relu', kernel_regularizer='l2', kernel_initializer=tf.keras.initializers.GlorotUniform(seed=kernel_seed)),
+      tf.keras.layers.Dropout(0.2, seed=dropout_seed),
+      tf.keras.layers.Dense(num_classes, activation='sigmoid', kernel_initializer=tf.keras.initializers.GlorotUniform(seed=kernel_seed))
+    ])
+
+    return model
+
+#%%
+model = cnn((100, 100, 1), (1,))
+model.compile(loss=tf.keras.losses.BinaryCrossentropy(from_logits=False), optimizer='Adam', metrics='accuracy')
+
+model.load_weights('weights.h5')
+
+#%%
+def segment(image):
+    image = parse_image(image)
+    # print(image.shape)
+    output = model.predict(image)
+    # print(output)
+    labels = {
+        "farsi" : 1-float(output),
+        "ruqaa" : float(output)
+    }
+    return labels
+
+iface = gr.Interface(fn=segment, 
+                    inputs="image", 
+                    outputs="label",
+                    examples=[["images/Farsi_1.jpg"], 
+                              ["images/Farsi_2.jpg"],
+                              ["images/Ruqaa_1.jpg"],
+                              ["images/Ruqaa_2.jpg"],
+                              ["images/Ruqaa_3.jpg"],
+                    ]).launch()
+# %%