Create app.py

app.py

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+
+import gradio as gr
+import spaces
+from transformers import pipeline
+import torch
+
+binary_example = [["Yahudi terörüne karşı protestolar kararlılıkla devam ediyor."]]
+category_example = [["Ermeni zulmü sırasında hayatını kaybeden kadınlar anısına dikilen anıt ziyarete açıldı."]]
+target_example = [["Dün 5 bin suriyeli enik doğmuştur zaten Türkiyede aq 5 bin suriyelinin gitmesi çok çok az"]]
+
+DESCRIPTION = """"
+## Hate Speech Detection in Turkish News
+"""
+
+CITATION = """"
+"""
+
+def binary_classification(input):
+    model = pipeline(model='gokceuludogan/berturk_tr_hate_print_w0.1')
+    return model(input)[0]
+
+def category_classification():
+    model = pipeline(model='gokceuludogan/berturk/tr_hateprint_cat_w0.1_b128')
+    return model(input)[0]
+
+def target_detection():
+    model = pipeline(model='gokceuludogan/turna_generation_tr_hateprint_target')
+    return model(input)[0]
+
+with gr.Blocks(theme="abidlabs/Lime") as demo:
+
+    #gr.Markdown("# TURNA")
+    #gr.Image("images/turna-logo.png", width=100, show_label=False, show_download_button=False, show_share_button=False)
+
+    with gr.Tab("TRHatePrint"):
+        gr.Markdown(DESCRIPTION)
+    
+    with gr.Tab("Binary Classification"):
+        gr.Markdown("Enter text to analyse hatefulness and pick the model.")
+        with gr.Column():
+            with gr.Row():
+                with gr.Column():
+                    # sentiment_choice = gr.Radio(choices = ["turna_", "berturk"], label ="Model", value="turna_")
+                    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)
+        
+    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_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)
+        
+    
+    with gr.Tab("Target Detection"):
+        gr.Markdown("Enter text to detect 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(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)
+    
+    gr.Markdown(CITATION)
+
+demo.launch()