Fix missing comma

app.py

@@ -1,8 +1,14 @@
 import gradio as gr
+import torch
+from transformers import DebertaV2Model, DebertaV2Config, AutoTokenizer, PreTrainedModel, ContextPooler
+from transformers import pipeline
+import torch.nn as nn
 
+# Define the model and tokenizer
 model_card = "microsoft/mdeberta-v3-base"
 finetuned_model = "MatteoFasulo/mdeberta-v3-base-subjectivity-sentiment-multilingual"
 
+# Custom model class for combining sentiment analysis with subjectivity detection
 class CustomModel(PreTrainedModel):
     config_class = DebertaV2Config
 
@@ -32,6 +38,7 @@ class CustomModel(PreTrainedModel):
         
         return {'logits': logits}
 
+# Load the pre-trained tokenizer
 def load_tokenizer(model_name: str):
     return AutoTokenizer.from_pretrained(model_name)
 
@@ -52,11 +59,13 @@ def load_model(model_card: str, finetuned_model: str):
 
     return model
 
+# Get sentiment values using a pre-trained sentiment analysis model
 def get_sentiment_values(text: str):
     pipe = pipeline("sentiment-analysis", model="cardiffnlp/twitter-xlm-roberta-base-sentiment", tokenizer="cardiffnlp/twitter-xlm-roberta-base-sentiment", top_k=None)
     sentiments = pipe(text)[0]
     return {k:v for k,v in [(list(sentiment.values())[0], list(sentiment.values())[1]) for sentiment in sentiments]}
 
+# Predict the subjectivity of a sentence
 def predict_subjectivity(text):
     sentiment_values = get_sentiment_values(text)
 
@@ -73,6 +82,7 @@ def predict_subjectivity(text):
 
     return predicted_class
 
+# Create a Gradio interface
 demo = gr.Interface(
     fn=predict_subjectivity, 
     inputs=gr.Textbox(
@@ -85,8 +95,9 @@ demo = gr.Interface(
         info="Whether the sentence is subjective or objective."
     ),
     title='Subjectivity Detection',
-    description='Detect if a sentence is subjective or objective using a pre-trained model.'
+    description='Detect if a sentence is subjective or objective using a pre-trained model.',
     theme='huggingface',
 )
 
+# Launch the interface
 demo.launch()