Update app.py

app.py

@@ -37,7 +37,7 @@ sentences = [
 ]
 
 # Function to map BERT labels
-#def map_bert_label(label):
+def map_bert_label(label):
     if label in ["1 star", "2 stars"]:
         return "negative"
     elif label == "3 stars":
@@ -45,18 +45,15 @@ sentences = [
     elif label in ["4 stars", "5 stars"]:
         return "positive"
 
-
 # Function to map RoBERTa labels
 def map_roberta_label(label):
     label_mapping = {"LABEL_0": "negative", "LABEL_1": "neutral", "LABEL_2": "positive"}
     return label_mapping[label]
 
-
 # Function to analyze sentiment
 def analyze_sentiment(sentence):
     # Define model paths
     model_paths = {
- #       "FinBert": "ProsusAI/finbert",
         "BERT": "nlptown/bert-base-multilingual-uncased-sentiment",
         "RoBERTa": "cardiffnlp/twitter-roberta-base-sentiment"
     }
@@ -66,24 +63,22 @@ def analyze_sentiment(sentence):
     for model_name, model_path in model_paths.items():
         sentiment_analyzer = pipeline("sentiment-analysis", model=model_path)
         result = sentiment_analyzer(sentence[:512])[0]  # Analyze first 512 characters for brevity
-
         if model_name == "BERT":
             result['label'] = map_bert_label(result['label'])
         elif model_name == "RoBERTa":
             result['label'] = map_roberta_label(result['label'])
-
         results[model_name] = result
 
     # Analyze sentiment using sklearn models
     results["Naive Bayes"] = {"label": nb_model.predict([sentence])[0],
                               "score": nb_model.predict_proba([sentence]).max()}
-    results["SVM"] = {"label": svm_model.predict([sentence])[0], "score": svm_model.predict_proba([sentence]).max()}
+    results["SVM"] = {"label": svm_model.predict([sentence])[0], 
+                      "score": svm_model.predict_proba([sentence]).max()}
     results["Random Forest"] = {"label": rf_model.predict([sentence])[0],
                                 "score": rf_model.predict_proba([sentence]).max()}
 
     return sentence, results
 
-
 # Create Gradio interface
 dropdown = gr.Dropdown(choices=sentences, label="Select Sentence")
 text_output = gr.Textbox(label="Selected Sentence", lines=2)
@@ -95,4 +90,4 @@ gr.Interface(
     outputs=[text_output, sentiment_output],
     title="Compare Sentiment Analysis Across Models",
     description="Select a sentence to see sentiment analysis results from multiple models."
-).launch(share=True)
+).launch(share=True)