Update app.py

app.py

@@ -23,6 +23,20 @@ APP_CITATION = """
 For citation, please refer to the tool's documentation.
 """
 
+binary_mapping = {
+    'LABEL_0': 'non-hateful', 
+    'LABEL_1': 'hateful', 
+}
+
+category_mapping = {
+    'LABEL_0': 'non-hateful', 
+    'LABEL_1': 'symbolization', 
+    'LABEL_2': 'exaggeration/generalization/attribution/distortion', 
+    'LABEL_3': 'swearing/insult/defamation/dehumanization',      
+    'LABEL_4': 'threat of enmity/war/attack/murder/harm',     
+}
+
+
 def inference_t5(input_text, selected_model):
     model = T5ForClassification.from_pretrained("gokceuludogan/turna_tr_hateprint_w0.1_new_") #_b128")
     tokenizer = AutoTokenizer.from_pretrained("gokceuludogan/turna_tr_hateprint_w0.1_new_") #_b128")
@@ -35,11 +49,11 @@ def perform_binary_classification(input_text, selected_model):
         return inference_t5(input_text, selected_model)
         
     model = pipeline(model=f'gokceuludogan/{selected_model}')
-    return model(input_text)[0]
+    return binary_mapping.get(model(input_text)[0]['label'], 'error')
 
-def perform_categorization(input_text, selected_model):
-    model = pipeline(model=f'gokceuludogan/{selected_model}')
-    return model(input_text)[0]
+def perform_categorization(input_text):
+    model = pipeline(model=f'gokceuludogan/berturk_tr_hateprint_cat_w0.1_b128')
+    return category_mapping.get(model(input_text)[0]['label'], 'error')
 
 def perform_target_detection(input_text):
     model = pipeline(model='gokceuludogan/turna_generation_tr_hateprint_target')
@@ -76,33 +90,21 @@ with gr.Blocks(theme="abidlabs/Lime") as hate_speech_demo:
                 inputs=[text_input_binary, model_choice_binary], 
                 outputs=classification_output
             )
-            gr.Examples(
-                examples=binary_classification_examples,
-                inputs=[text_input_binary, model_choice_binary],
-                outputs=classification_output,
-                fn=perform_binary_classification
-            )
 
     # Hate Speech Categorization Tab
     with gr.Tab("Hate Speech Categorization"):
         gr.Markdown("Categorize the hate speech type in the provided text.")
         with gr.Column():
-            model_choice_category = "berturk_tr_hateprint_cat_w0.1_b128"
         
             text_input_category = gr.Textbox(label="Input Text")
             categorize_button = gr.Button("Categorize")
             categorization_output = gr.Textbox(label="Categorization Result")
             categorize_button.click(
                 perform_categorization, 
-                inputs=[text_input_category, model_choice_category], 
+                inputs=[text_input_category], 
                 outputs=categorization_output
             )
-            gr.Examples(
-                examples=categorization_examples,
-                inputs=[text_input_category, model_choice_category],
-                outputs=categorization_output,
-                fn=perform_categorization
-            )
+
 
     # Target Detection Tab
     with gr.Tab("Target Detection"):
@@ -116,12 +118,7 @@ with gr.Blocks(theme="abidlabs/Lime") as hate_speech_demo:
                 inputs=[text_input_target], 
                 outputs=target_output
             )
-            gr.Examples(
-                examples=target_detection_examples,
-                inputs=[text_input_target],
-                outputs=target_output,
-                fn=perform_target_detection
-            )
+
 
     # Multi Detection Tab
     with gr.Tab("Multi Detection"):
@@ -135,12 +132,7 @@ with gr.Blocks(theme="abidlabs/Lime") as hate_speech_demo:
                 inputs=[text_input_multi], 
                 outputs=multi_output
             )
-            gr.Examples(
-                examples=target_detection_examples,
-                inputs=[text_input_multi],
-                outputs=multi_output,
-                fn=perform_multi_detection
-            )
+
 
     # Citation Section
     gr.Markdown(APP_CITATION)