apply black formatting and add doc strings

.gitignore

@@ -0,0 +1 @@
+/venv

app.py

@@ -1,10 +1,10 @@
-#import csv
+# import csv
 import gradio as gr
 import pandas as pd
 from transformers import pipeline
 from transformers import AutoTokenizer, AutoModelForSequenceClassification, AutoConfig
-#from datasets import load_dataset
 
+# from datasets import load_dataset
 
 
 # Load the model and define the sentiment classifier
@@ -16,47 +16,53 @@ pipe = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer, config=c
 
 
 def classify_sentiment(sentences):
-  # Classify the sentiment of each sentence
-  predictions = pipe(sentences)
-  
-  # Extract the predicted labels and confidence scores from the predictions
-  labels = [prediction['label'] for prediction in predictions]
-  confidences = [prediction['score'] for prediction in predictions]
-  
-  return labels, confidences
+    """
+    Classify the sentiment of each sentence
+    """
+    predictions = pipe(sentences)
+
+    # Extract the predicted labels and confidence scores from the predictions
+    labels = [prediction["label"] for prediction in predictions]
+    confidences = [prediction["score"] for prediction in predictions]
+
+    return labels, confidences
+
 
 def classify_sentiment_from_csv(csv_file):
-  # Read the CSV file and extract the list of sentences
-  df = pd.read_csv(csv_file.name, delimiter=",")
-  sentences = df['sentence'].tolist()
-  
-  # Classify the sentiment of the sentences
-  labels, confidences = classify_sentiment(sentences)
-  df['confidences'] = confidences
-  df['labels'] = labels
-  return df
-
-# Define the gradio app
+    """
+    Read the CSV file and extract the list of sentences
+    """
+    df = pd.read_csv(csv_file.name, delimiter=",")
+    sentences = df["sentence"].tolist()
+
+    # Classify the sentiment of the sentences
+    labels, confidences = classify_sentiment(sentences)
+    df["confidences"] = confidences
+    df["labels"] = labels
+    return df
+
+
 def main():
-  iface = gr.Interface(fn=classify_sentiment_from_csv,
-                       inputs=gr.File(),
-                       outputs=gr.Dataframe(),
-                       live=True,
-                       #capture_session=True,
-                       allow_flagging='never')
-  
-  iface.launch(enable_queue=False)
+    """
+    Define the gradio app
+    """
+    iface = gr.Interface(
+        fn=classify_sentiment_from_csv,
+        inputs=gr.File(),
+        outputs=gr.Dataframe(),
+        live=True,
+        # capture_session=True,
+        allow_flagging="never",
+    )
 
+    iface.launch(enable_queue=False)
 
 
-#debug:
+# debug:
 # labels, confidence = classify_sentiment_from_csv("./reviews.csv")
 # print(labels)
 
 
-
 # Run the gradio app
 if __name__ == "__main__":
-  main()
-
-
+    main()