Update app.py

app.py

@@ -19,17 +19,21 @@ DESCRIPTION = """"
 CITATION = """"
 """
 
-def binary_classification(input):
-    model = pipeline(model='gokceuludogan/berturk_tr_hate_print_w0.1', token=token)
+def binary_classification(input, choice):
+    model = pipeline(model=f'gokceuludogan/{choice}')
     return model(input)[0]
 
-def category_classification(input):
-    model = pipeline(model='gokceuludogan/berturk_tr_hateprint_cat_w0.1_b128', token=token)
+def category_classification(input, choice):
+    model = pipeline(model=f'gokceuludogan/{choice}')
     return model(input)[0]
 
 def target_detection(input):
-    model = pipeline(model='gokceuludogan/turna_generation_tr_hateprint_target', token=token)
-    return model(input)[0]
+    model = pipeline(model='gokceuludogan/turna_generation_tr_hateprint_target')
+    return model(input)[0]['generated_text']
+
+def multi_detection(input):
+    model = pipeline(model='gokceuludogan/turna_generation_tr_hateprint_multi')
+    return model(input)[0]['generated_text']
 
 with gr.Blocks(theme="abidlabs/Lime") as demo:
 
@@ -44,25 +48,26 @@ with gr.Blocks(theme="abidlabs/Lime") as demo:
         with gr.Column():
             with gr.Row():
                 with gr.Column():
-                    # sentiment_choice = gr.Radio(choices = ["turna_", "berturk"], label ="Model", value="turna_")
+                    sentiment_choice = gr.Radio(choices = ["turna_tr_hateprint", "turna_tr_hateprint_5e6_w0.1_", "berturk_tr_hateprint_w0.1", "berturk_tr_hateprint_w0.1_b128"], label ="Model", value="turna_tr_hateprint")
                     sentiment_input = gr.Textbox(label="Input")
                 
                     sentiment_submit = gr.Button()
                 sentiment_output = gr.Textbox(label="Output")
-                sentiment_submit.click(binary_classification, inputs=[sentiment_input], outputs=sentiment_output)
-            sentiment_examples = gr.Examples(examples = binary_example, inputs = [sentiment_input], outputs=sentiment_output, fn=binary_classification)
+                sentiment_submit.click(binary_classification, inputs=[sentiment_input, sentiment_choice], outputs=sentiment_output)
+            sentiment_examples = gr.Examples(examples = binary_example, inputs = [sentiment_input, sentiment_choice], outputs=sentiment_output, fn=binary_classification)
         
     with gr.Tab("Hate Speech Categorization"):
         gr.Markdown("Enter a hateful text to categorize or try the example.")
         with gr.Column():
             with gr.Row():
                 with gr.Column():
+                    text_choice = gr.Radio(choices= ["berturk_tr_hateprint_cat_w0.1_b128", "berturk_tr_hateprint_cat_w0.1"])
                     text_input = gr.Textbox(label="Input")
                 
                     text_submit = gr.Button()
                 text_output = gr.Textbox(label="Output")
-                text_submit.click(category_classification, inputs=[text_input], outputs=text_output)
-            text_examples = gr.Examples(examples = category_example,inputs=[text_input], outputs=text_output, fn=category_classification)
+                text_submit.click(category_classification, inputs=[text_input, text_choice], outputs=text_output)
+            text_examples = gr.Examples(examples = category_example,inputs=[text_input, text_choice], outputs=text_output, fn=category_classification)
         
     
     with gr.Tab("Target Detection"):
@@ -74,8 +79,20 @@ with gr.Blocks(theme="abidlabs/Lime") as demo:
                     nli_submit = gr.Button()
                 nli_output = gr.Textbox(label="Output")
                 nli_submit.click(target_detection, inputs=[nli_first_input], outputs=nli_output)
-            nli_examples = gr.Examples(examples = target_example, inputs = [nli_first_input], outputs=nli_output, fn=target_example)
+            nli_examples = gr.Examples(examples = target_example, inputs = [nli_first_input], outputs=nli_output, fn=target_detection)
+      
     
+    with gr.Tab("Multi Detection"):
+        gr.Markdown("Enter text to detect hate, category, and targets ")
+        with gr.Column():
+            with gr.Row():
+                with gr.Column():
+                    nli_first_input = gr.Textbox(label="Input")
+                    nli_submit = gr.Button()
+                nli_output = gr.Textbox(label="Output")
+                nli_submit.click(multi_detection, inputs=[nli_first_input], outputs=nli_output)
+            nli_examples = gr.Examples(examples = target_example, inputs = [nli_first_input], outputs=nli_output, fn=multi_detection)
+            
     gr.Markdown(CITATION)
 
 demo.launch()