Delete ascr.py

ascr.py

@@ -1,68 +0,0 @@
-
-# %%
-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()