Add new features

app.py

@@ -41,15 +41,46 @@ df_example_instance_loaded = pd.read_csv('./data/example_instance.csv', index_co
 O = df_example_instance_loaded.values[0]
 v_att1 = tf.convert_to_tensor(df_atts_loaded.values[0])
 v_att2 = tf.convert_to_tensor(df_atts_loaded.values[1])
+v_att3 = tf.convert_to_tensor(df_atts_loaded.values[2])
+v_att4 = tf.convert_to_tensor(df_atts_loaded.values[3])
+v_att5 = tf.convert_to_tensor(df_atts_loaded.values[4])
+v_att6 = tf.convert_to_tensor(df_atts_loaded.values[5])
+v_att7 = tf.convert_to_tensor(df_atts_loaded.values[6])
+
+# maximum_ = 25
+# delta = 3.0 / maximum_
+# def image_classifier(value_1, value_2):
+#     return np.clip(((variational_decoder(tf.reshape((O + delta * value_1 * v_att1 + delta * value_2 * v_att2), (1, 64)))[0]) * 255), 0, 255).astype(int)[:, :, :]
+
+# input_value_d_1 = gr.Slider(minimum=0, maximum=25, step=1)
+# input_value_d_2 = gr.Slider(minimum=0, maximum=25, step=1)
+# demo = gr.Interface(fn=image_classifier, inputs=[input_value_d_1, input_value_d_2], outputs="image", live=True)
 
 maximum_ = 25
-delta = 3.0 / maximum_
-def image_classifier(value_1, value_2):
-    return np.clip(((variational_decoder(tf.reshape((O + delta * value_1 * v_att1 + delta * value_2 * v_att2), (1, 64)))[0]) * 255), 0, 255).astype(int)[:, :, :]
+delta = 1.0 / maximum_
+def image_classifier(value_1, value_2, value_3, value_4, value_5, value_6, value_7):
+    return np.clip(((variational_decoder(tf.reshape((O + delta * value_1 * v_att1 + \
+                                                     delta * 1.5 * value_2 * v_att2 + \
+                                                     delta * 3.5 * value_3 * v_att3 + \
+                                                     3.2 * delta * value_4 * v_att4 + \
+                                                     4.0 * delta * value_5 * v_att5 + \
+                                                     delta * value_6 * v_att6 + \
+                                                     3.0 * delta * value_7 * v_att7), (1, 64)))[0]) * 255), 0, 255).astype(int)[:, :, :]
 
 input_value_d_1 = gr.Slider(minimum=0, maximum=25, step=1)
 input_value_d_2 = gr.Slider(minimum=0, maximum=25, step=1)
-demo = gr.Interface(fn=image_classifier, inputs=[input_value_d_1, input_value_d_2], outputs="image", live=True)
+input_value_d_3 = gr.Slider(minimum=0, maximum=25, step=1)
+input_value_d_4 = gr.Slider(minimum=0, maximum=25, step=1)
+input_value_d_5 = gr.Slider(minimum=0, maximum=25, step=1)
+input_value_d_6 = gr.Slider(minimum=0, maximum=25, step=1)
+input_value_d_7 = gr.Slider(minimum=0, maximum=25, step=1)
+demo = gr.Interface(fn=image_classifier, inputs=[input_value_d_1, 
+                                                 input_value_d_2, 
+                                                 input_value_d_3, 
+                                                 input_value_d_4, 
+                                                 input_value_d_5,
+                                                 input_value_d_6,
+                                                 input_value_d_7], outputs="image", live=True)
 
 if __name__ == "__main__":
     demo.launch()

data/attributes_encodings.csv

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:b056f7f6d074e67958c270f84ba6ba9cfbca1285efd5e7f5fadd42c41abaad70
-size 1716
+oid sha256:a618b8bb435d3ed8872c96c12dfa10c4c3b622ffd9933896c846d0f53ec644b2
+size 5587