loading model with pipeline

Loading dsfsi model with pipeline

app.py

@@ -160,7 +160,7 @@ model_2 = load_model(constants.MODEL_NAME, "model_v2.bin")
 model_3 = load_model(constants.MODEL_NAME, "model_v3.bin")
 openlid = load_model('laurievb/OpenLID', "model.bin")
 nllb = load_model('facebook/fasttext-language-identification', "model.bin")
-za_lid = load_model('dsfsi/za-lid-bert', "model.bin")
+za_lid = load_model_pipeline('dsfsi/za-lid-bert', "model.bin")
 
 
 # @st.cache_resource
@@ -220,20 +220,22 @@ def compute(sentences, version = 'v3'):
     for index, sent in enumerate(sentences):
 
         output = model_choice.predict(sent)
-        
-        output_label  = output[0][0].split('__')[-1].replace('_Hans', '_Hani').replace('_Hant', '_Hani')
-        output_prob = max(min(output[1][0], 1), 0) 
-        output_label_language = output_label.split('_')[0]
-
-        # script control
-        if version in ['v3', 'v2', 'openlid-201', 'nllb-218'] and output_label_language!= 'zxx':
-            main_script, all_scripts = get_script(sent)
-            output_label_script = output_label.split('_')[1]
-
-            if output_label_script not in all_scripts:
-                output_label_script = main_script
-                output_label = f"und_{output_label_script}"
-                output_prob = 0
+        output_label = output['label']
+        output_prob =  output['score']
+        output_label_language = output['label']
+        # output_label  = output[0][0].split('__')[-1].replace('_Hans', '_Hani').replace('_Hant', '_Hani')
+        # output_prob = max(min(output[1][0], 1), 0) 
+        # output_label_language = output_label.split('_')[0]
+
+        # # script control
+        # if version in ['v3', 'v2', 'openlid-201', 'nllb-218'] and output_label_language!= 'zxx':
+        #     main_script, all_scripts = get_script(sent)
+        #     output_label_script = output_label.split('_')[1]
+
+        #     if output_label_script not in all_scripts:
+        #         output_label_script = main_script
+        #         output_label = f"und_{output_label_script}"
+        #         output_prob = 0
 
     
         labels = labels + [output_label]