code refactoring

app.py

@@ -1,13 +1,18 @@
 import gradio as gr
 
-from interfaces.classifier_interface import classifier_interface
-from interfaces.masked_interface import masked_interface
-from interfaces.embed_interface import embed_interface
+from interfaces.classifier_interface import ClassifierInterface
+from interfaces.masked_interface import MaskedInterface
+from interfaces.embed_interface import EmbedInterface
+
+
+class_interface = ClassifierInterface()
+masked_interface = MaskedInterface()
+embed_interface = EmbedInterface()
 
 demo = gr.TabbedInterface(
-    interface_list=[classifier_interface,
-                    masked_interface,
-                    embed_interface],
+    interface_list=[class_interface(),
+                    masked_interface(),
+                    embed_interface()],
     tab_names=[
     'Multiclass Classification',
     'Masked Language Modeling',

interfaces/classifier_interface.py

@@ -3,57 +3,59 @@ 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])
+class ClassifierInterface:
+    def __init__(self):
+        self.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"
+        ]
+        self.model_box=[
+            gr.load(self.models[0], src='models', hf_token=os.environ['API_KEY']),
+            gr.load(self.models[1], src='models', hf_token=os.environ['API_KEY']),
+            gr.load(self.models[2], src='models', hf_token=os.environ['API_KEY']),
+            gr.load(self.models[3], src='models', hf_token=os.environ['API_KEY']),
+            gr.load(self.models[4], src='models', hf_token=os.environ['API_KEY']),
+            gr.load(self.models[5], src='models', hf_token=os.environ['API_KEY']),
+            gr.load(self.models[6], src='models', hf_token=os.environ['API_KEY']),
+            gr.load(self.models[7], src='models', hf_token=os.environ['API_KEY']),
+            gr.load(self.models[8], src='models', hf_token=os.environ['API_KEY']),
+            gr.load(self.models[9], src='models', hf_token=os.environ['API_KEY']),
+            gr.load(self.models[10], src='models', hf_token=os.environ['API_KEY']),
+            gr.load(self.models[11], src='models', hf_token=os.environ['API_KEY']),
+            gr.load(self.models[12], src='models', hf_token=os.environ['API_KEY'])
+        ]
+
+
+
+    def sentiment_analysis(self, text, model_choice):
+        model = self.model_box[model_choice]
+        output = model(text)
+        return output
+
+
+    def __call__(self):
+        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 self.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(self.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])
+        return classifier_interface

interfaces/embed_interface.py

@@ -5,62 +5,62 @@ 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"
-]
+class EmbedInterface:
+    def __init__(self):
+        self.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"
+    ]
+    
+    
+    def clear_sentences(self):
+        return ""
 
-sentence_list = [] #global variable go brr
+    def display_list(self, written_text, text_to_add):
+        if written_text == "":
+            new_text = text_to_add
+        else:
+            new_text = written_text + "\n" + text_to_add
+        return new_text
 
-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")
+    def sentiment_analysis(self, text, model_choice, text_to_compare):
+        sentence_list = text_to_compare.split('\n')
+        model = SentenceEncoder(self.models[model_choice])
+        pred = model.find_most_similar(text, sentence_list)
+        return {p[0]:(float(p[1]) if p[1]>0 else 0) for p in pred}
+    
+    def __call__(self):
+        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():
-                    input_text2 = gr.Textbox(label ='Add Sentence', placeholder='asdas')
+                    model_choice = gr.Dropdown(label="Select Model", choices=[m for m in self.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='aptal aptal konuşma')
+                        with gr.Column():
+                            input_text3 = gr.Textbox(label ='Sentences List')
+                    with gr.Row():
+                        add_button = gr.Button('Add')       
+                        clear_button = gr.Button('Clear')
+                    the_button = gr.Button("Run")
                 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)
+                    output_window = gr.Label(num_top_classes=5, show_label=False)
+
+            clear_button.click(self.clear_sentences, outputs=[input_text3])
+            add_button.click(self.display_list, inputs=[input_text3,input_text2], outputs=[input_text3])
+            the_button.click(self.sentiment_analysis, inputs=[input_text, model_choice, input_text3], outputs=[output_window])
 
-    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])
+        return embed_interface

interfaces/masked_interface.py

@@ -2,55 +2,63 @@ from transformers import TextClassificationPipeline, AutoTokenizer, AutoModelFor
 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):
+
+class MaskedInterface:
+    def __init__(self):
+        self.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"
+    ]
+        self.model_box = [
+        gr.load(self.models[0], src='models', hf_token=os.environ['API_KEY']),
+        gr.load(self.models[1], src='models', hf_token=os.environ['API_KEY']),
+        gr.load(self.models[2], src='models', hf_token=os.environ['API_KEY']),
+        gr.load(self.models[3], src='models', hf_token=os.environ['API_KEY']),
+        gr.load(self.models[4], src='models', hf_token=os.environ['API_KEY']),
+        gr.load(self.models[5], src='models', hf_token=os.environ['API_KEY']),
+        gr.load(self.models[6], src='models', hf_token=os.environ['API_KEY']),
+        gr.load(self.models[7], src='models', hf_token=os.environ['API_KEY']),
+        gr.load(self.models[8], src='models', hf_token=os.environ['API_KEY']),
+        gr.load(self.models[9], src='models', hf_token=os.environ['API_KEY']),
+        gr.load(self.models[10], src='models', hf_token=os.environ['API_KEY']),
+        gr.load(self.models[11], src='models', hf_token=os.environ['API_KEY']),
+        gr.load(self.models[12], src='models', hf_token=os.environ['API_KEY'])
+        ]
+
+    def sentiment_analysis(self, text, model_choice):
+        model = self.model_box[model_choice]
+        output = model(text)
+        return output
     
-    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])
+    def __call__(self):
+        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 self.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(self.sentiment_analysis, inputs=[input_text, model_choice], outputs=[output_window])
+            examples = gr.Examples(examples=["sen tam bir [MASK]", "erkekler [MASK] üstündür"],
+                                inputs=[input_text])
+            
+        return masked_interface
+
+
+
+
+