created tabbed interfaces

app.py

@@ -1,57 +1,18 @@
-from transformers import TextClassificationPipeline, AutoTokenizer, AutoModelForSequenceClassification
 import gradio as gr
-import os
 
-models = [
-    "Overfit-GM/bert-base-turkish-cased-offensive",
-    "Overfit-GM/bert-base-turkish-uncased-offensive",
-    "Overfit-GM/bert-base-turkish-128k-cased-offensive",
-    "Overfit-GM/bert-base-turkish-128k-uncased-offensive",
-    "Overfit-GM/convbert-base-turkish-mc4-cased-offensive",
-    "Overfit-GM/convbert-base-turkish-mc4-uncased-offensive",
-    "Overfit-GM/convbert-base-turkish-cased-offensive",
-    "Overfit-GM/distilbert-base-turkish-cased-offensive",
-    "Overfit-GM/electra-base-turkish-cased-discriminator-offensive",
-    "Overfit-GM/electra-base-turkish-mc4-cased-discriminator-offensive",
-    "Overfit-GM/electra-base-turkish-mc4-uncased-discriminator-offensive",
-    "Overfit-GM/xlm-roberta-large-turkish-offensive",
-    "Overfit-GM/mdeberta-v3-base-offensive"
-]
+from interfaces.classifier_interface import classifier_interface
+from interfaces.masked_interface import masked_interface
+from interfaces.embed_interface import embed_interface
 
-model_box=[
-    gr.load(models[0], src='models', hf_token=os.environ['API_KEY']),
-    gr.load(models[1], src='models', hf_token=os.environ['API_KEY']),
-    gr.load(models[2], src='models', hf_token=os.environ['API_KEY']),
-    gr.load(models[3], src='models', hf_token=os.environ['API_KEY']),
-    gr.load(models[4], src='models', hf_token=os.environ['API_KEY']),
-    gr.load(models[5], src='models', hf_token=os.environ['API_KEY']),
-    gr.load(models[6], src='models', hf_token=os.environ['API_KEY']),
-    gr.load(models[7], src='models', hf_token=os.environ['API_KEY']),
-    gr.load(models[8], src='models', hf_token=os.environ['API_KEY']),
-    gr.load(models[9], src='models', hf_token=os.environ['API_KEY']),
-    gr.load(models[10], src='models', hf_token=os.environ['API_KEY']),
-    gr.load(models[11], src='models', hf_token=os.environ['API_KEY']),
-    gr.load(models[12], src='models', hf_token=os.environ['API_KEY'])
-]
-
-def sentiment_analysis(text, model_choice):
-    
-    model = model_box[model_choice]
-    output = model(text)
-    return output
-
-with gr.Blocks() as demo:
-    gr.HTML("""<h1 style="font-weight:600;font-size:50;margin-top:4px;margin-bottom:4px;text-align:center;">No Offense Classifier</h1></div>""")
-    with gr.Row():
-        with gr.Column():
-            model_choice = gr.Dropdown(label="Select Model", choices=[m for m in models], type="index", interactive=True)
-            input_text = gr.Textbox(label="Input", placeholder="senin ben amk")
-            the_button = gr.Button(label="Run")
-        with gr.Column():
-            output_window = gr.Label(num_top_classes=5)
-
-    the_button.click(sentiment_analysis, inputs=[input_text, model_choice], outputs=[output_window])
-    examples = gr.Examples(examples=["bu adamların ülkesine dönmesi lazım", "adam olsan oraya gitmezdin"],
-                           inputs=[input_text])
+demo = gr.TabbedInterface(
+    interface_list=[classifier_interface,
+                    masked_interface,
+                    embed_interface],
+    tab_names=[
+    'Multiclass Classification',
+    'Masked Language Modeling',
+    'Sentence Similarity'
+    ]
+)
 
 demo.launch()

interfaces/classifier_interface.py

@@ -0,0 +1,59 @@
+from transformers import TextClassificationPipeline, AutoTokenizer, AutoModelForSequenceClassification
+import gradio as gr
+import os
+
+
+models = [
+    "Overfit-GM/bert-base-turkish-cased-offensive",
+    "Overfit-GM/bert-base-turkish-uncased-offensive",
+    "Overfit-GM/bert-base-turkish-128k-cased-offensive",
+    "Overfit-GM/bert-base-turkish-128k-uncased-offensive",
+    "Overfit-GM/convbert-base-turkish-mc4-cased-offensive",
+    "Overfit-GM/convbert-base-turkish-mc4-uncased-offensive",
+    "Overfit-GM/convbert-base-turkish-cased-offensive",
+    "Overfit-GM/distilbert-base-turkish-cased-offensive",
+    "Overfit-GM/electra-base-turkish-cased-discriminator-offensive",
+    "Overfit-GM/electra-base-turkish-mc4-cased-discriminator-offensive",
+    "Overfit-GM/electra-base-turkish-mc4-uncased-discriminator-offensive",
+    "Overfit-GM/xlm-roberta-large-turkish-offensive",
+    "Overfit-GM/mdeberta-v3-base-offensive"
+]
+
+
+model_box=[
+    gr.load(models[0], src='models', hf_token=os.environ['API_KEY']),
+    gr.load(models[1], src='models', hf_token=os.environ['API_KEY']),
+    gr.load(models[2], src='models', hf_token=os.environ['API_KEY']),
+    gr.load(models[3], src='models', hf_token=os.environ['API_KEY']),
+    gr.load(models[4], src='models', hf_token=os.environ['API_KEY']),
+    gr.load(models[5], src='models', hf_token=os.environ['API_KEY']),
+    gr.load(models[6], src='models', hf_token=os.environ['API_KEY']),
+    gr.load(models[7], src='models', hf_token=os.environ['API_KEY']),
+    gr.load(models[8], src='models', hf_token=os.environ['API_KEY']),
+    gr.load(models[9], src='models', hf_token=os.environ['API_KEY']),
+    gr.load(models[10], src='models', hf_token=os.environ['API_KEY']),
+    gr.load(models[11], src='models', hf_token=os.environ['API_KEY']),
+    gr.load(models[12], src='models', hf_token=os.environ['API_KEY'])
+]
+
+
+
+def sentiment_analysis(text, model_choice):
+    
+    model = model_box[model_choice]
+    output = model(text)
+    return output
+
+with gr.Blocks() as classifier_interface:
+    gr.HTML("""<h1 style="font-weight:600;font-size:50;margin-top:4px;margin-bottom:4px;text-align:center;">No Offense Classifier</h1></div>""")
+    with gr.Row():
+        with gr.Column():
+            model_choice = gr.Dropdown(label="Select Model", choices=[m for m in models], type="index", interactive=True)
+            input_text = gr.Textbox(label="Input", placeholder="senin ben amk")
+            the_button = gr.Button(label="Run")
+        with gr.Column():
+            output_window = gr.Label(num_top_classes=5)
+
+    the_button.click(sentiment_analysis, inputs=[input_text, model_choice], outputs=[output_window])
+    examples = gr.Examples(examples=["bu adamların ülkesine dönmesi lazım", "adam olsan oraya gitmezdin"],
+                           inputs=[input_text])

interfaces/embed_interface.py

@@ -0,0 +1,66 @@
+from transformers import TextClassificationPipeline, AutoTokenizer, AutoModelForSequenceClassification
+from nooffense.sentence_encoder import SentenceEncoder
+import numpy as np
+import gradio as gr
+import os
+
+
+models = [
+    "Overfit-GM/bert-base-turkish-cased-offensive",
+    "Overfit-GM/bert-base-turkish-uncased-offensive",
+    "Overfit-GM/bert-base-turkish-128k-cased-offensive",
+    "Overfit-GM/bert-base-turkish-128k-uncased-offensive",
+    "Overfit-GM/convbert-base-turkish-mc4-cased-offensive",
+    "Overfit-GM/convbert-base-turkish-mc4-uncased-offensive",
+    "Overfit-GM/convbert-base-turkish-cased-offensive",
+    "Overfit-GM/distilbert-base-turkish-cased-offensive",
+    "Overfit-GM/electra-base-turkish-cased-discriminator-offensive",
+    "Overfit-GM/electra-base-turkish-mc4-cased-discriminator-offensive",
+    "Overfit-GM/electra-base-turkish-mc4-uncased-discriminator-offensive",
+    "Overfit-GM/xlm-roberta-large-turkish-offensive",
+    "Overfit-GM/mdeberta-v3-base-offensive"
+]
+
+sentence_list = [] #global variable go brr
+
+def normalize_outputs(pred):
+    values = np.asarray([p[1] for p in pred])
+    normalized = (values-min(values))/(max(values)-min(values))
+    new_preds = {p[0]:float(v) for p,v in zip(pred, normalized)}
+    return new_preds
+
+def clear_sentences():
+    sentence_list.clear()
+    return None
+
+def display_list(text):
+    sentence_list.append(text)
+    new_text = '\n'.join(sentence_list)
+    return new_text
+
+def sentiment_analysis(text, model_choice):
+    model = SentenceEncoder(models[model_choice])
+    pred = model.find_most_similar(text, sentence_list)
+    return normalize_outputs(pred)
+
+with gr.Blocks() as embed_interface:
+    gr.HTML("""<h1 style="font-weight:600;font-size:50;margin-top:4px;margin-bottom:4px;text-align:center;">No Offense Sentence Similarity</h1></div>""")
+    with gr.Row():
+        with gr.Column():
+            model_choice = gr.Dropdown(label="Select Model", choices=[m for m in models], type="index", interactive=True)
+            input_text = gr.Textbox(label="Input", placeholder="senin ben amk")
+            with gr.Row():
+                with gr.Column():
+                    input_text2 = gr.Textbox(label ='Add Sentence', placeholder='asdas')
+                with gr.Column():
+                    input_text3 = gr.Textbox(label ='Sentences List', placeholder='asdasd')
+            with gr.Row():
+                add_button = gr.Button('Add')       
+                clear_button = gr.Button('Clear')
+            the_button = gr.Button("Run")
+        with gr.Column():
+            output_window = gr.Label(num_top_classes=5, show_label=False)
+
+    clear_button.click(clear_sentences, outputs=[input_text3])
+    add_button.click(display_list, inputs=[input_text2], outputs=[input_text3])
+    the_button.click(sentiment_analysis, inputs=[input_text, model_choice], outputs=[output_window])

interfaces/masked_interface.py

@@ -0,0 +1,56 @@
+from transformers import TextClassificationPipeline, AutoTokenizer, AutoModelForSequenceClassification
+import gradio as gr
+import os
+
+models = [
+    "Overfit-GM/bert-base-turkish-cased-offensive-mlm",
+    "Overfit-GM/bert-base-turkish-uncased-offensive-mlm",
+    "Overfit-GM/bert-base-turkish-128k-cased-offensive-mlm",
+    "Overfit-GM/bert-base-turkish-128k-uncased-offensive-mlm",
+    "Overfit-GM/convbert-base-turkish-mc4-cased-offensive-mlm",
+    "Overfit-GM/convbert-base-turkish-mc4-uncased-offensive-mlm",
+    "Overfit-GM/convbert-base-turkish-cased-offensive-mlm",
+    "Overfit-GM/distilbert-base-turkish-cased-offensive-mlm",
+    "Overfit-GM/electra-base-turkish-cased-discriminator-offensive-mlm",
+    "Overfit-GM/electra-base-turkish-mc4-cased-discriminator-offensive-mlm",
+    "Overfit-GM/electra-base-turkish-mc4-uncased-discriminator-offensive-mlm",
+    "Overfit-GM/xlm-roberta-large-offensive-mlm",
+    "Overfit-GM/mdeberta-v3-base-offensive-mlm"
+]
+
+
+model_box=[
+    gr.load(models[0], src='models', hf_token=os.environ['API_KEY']),
+    gr.load(models[1], src='models', hf_token=os.environ['API_KEY']),
+    gr.load(models[2], src='models', hf_token=os.environ['API_KEY']),
+    gr.load(models[3], src='models', hf_token=os.environ['API_KEY']),
+    gr.load(models[4], src='models', hf_token=os.environ['API_KEY']),
+    gr.load(models[5], src='models', hf_token=os.environ['API_KEY']),
+    gr.load(models[6], src='models', hf_token=os.environ['API_KEY']),
+    gr.load(models[7], src='models', hf_token=os.environ['API_KEY']),
+    gr.load(models[8], src='models', hf_token=os.environ['API_KEY']),
+    gr.load(models[9], src='models', hf_token=os.environ['API_KEY']),
+    gr.load(models[10], src='models', hf_token=os.environ['API_KEY']),
+    gr.load(models[11], src='models', hf_token=os.environ['API_KEY']),
+    gr.load(models[12], src='models', hf_token=os.environ['API_KEY'])
+]
+
+def sentiment_analysis(text, model_choice):
+    
+    model = model_box[model_choice]
+    output = model(text)
+    return output
+
+with gr.Blocks() as masked_interface:
+    gr.HTML("""<h1 style="font-weight:600;font-size:50;margin-top:4px;margin-bottom:4px;text-align:center;">No Offense Fill Masks</h1></div>""")
+    with gr.Row():
+        with gr.Column():
+            model_choice = gr.Dropdown(label="Select Model", choices=[m for m in models], type="index", interactive=True)
+            input_text = gr.Textbox(label="Input", placeholder="senin ben [MASK]")
+            the_button = gr.Button(label="Run")
+        with gr.Column():
+            output_window = gr.Label(num_top_classes=5)
+
+    the_button.click(sentiment_analysis, inputs=[input_text, model_choice], outputs=[output_window])
+    examples = gr.Examples(examples=["sen tam bir [MASK]", "erkekler [MASK] üstündür"],
+                           inputs=[input_text])

requirements.txt

@@ -1,4 +1,5 @@
 torch
 transformers
 numpy
-gradio
+gradio
+git+https://github.com/ertugrul-dmr/NoOffense.git