Update app.py

app.py

@@ -12,7 +12,6 @@ from transformers.utils import logging
 
 logging.set_verbosity("ERROR")
 
-
 # Load the provided dataset
 file_path = 'data.csv'
 df = pd.read_csv(file_path)
@@ -34,7 +33,6 @@ rf_model.fit(X_train, y_train)
 sentences = [
     "The announced restructuring will erase the company's indebtedness.",
     "UPM-Kymmene upgraded to `in-line' from `underperform' by Goldman Sachs.",
-   # "$AAPL shares are breaking above the recent resistance level.",
     "Profitability (in EBIT %) was not impressive due to expenses rising by 14.3%.",
     "The Finnish bank has issued a profit warning.",
     "TeliaSonera's underlying results however included 457 mln SKr in positive one-offs, hence the adjusted underlying EBITDA actually amounts to 7.309 bln SKr, clearly below expectations, analysts said."
@@ -49,19 +47,13 @@ def map_bert_label(label):
     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 = {
         "BERT": "nlptown/bert-base-multilingual-uncased-sentiment",
-      #  "RoBERTa": "cardiffnlp/twitter-roberta-base-sentiment"
     }
-
+    
     # Analyze sentiment using transformers models
     results = {}
     for model_name, model_path in model_paths.items():
@@ -69,50 +61,33 @@ def analyze_sentiment(sentence):
         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], 
+    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)
-sentiment_output = gr.JSON(label="Sentiment Scores")
-
-
-# Custom CSS to reduce font size
-custom_css = """
-body, .gradio-container {
-    font-size: 8px !important;
-}
-.output-markdown, .input-markdown {
-    font-size: 8px !important;
-}
-"""
-
-
-gr.Interface(
-    fn=analyze_sentiment,
-    inputs=[dropdown],
-    outputs=[text_output, sentiment_output],
-    title="Compare Sentiment Analysis Across Models",
-    description="Select a sentence to see sentiment analysis results from multiple models.",
-    css=custom_css  # Add this line to apply the custom CSS
-).launch()
-
-# gr.Interface(
-#     fn=analyze_sentiment,
-#     inputs=[dropdown],
-#     outputs=[text_output, sentiment_output],
-#     title="Compare Sentiment Analysis Across Models",
-#     description="Select a sentence to see sentiment analysis results from multiple models."
-# ).launch()
+# Create a custom theme with smaller font size
+custom_theme = gr.themes.Default(
+    text_size=gr.themes.sizes.text_sm,  # Set smaller text size
+    font=[gr.themes.GoogleFont("Source Sans Pro"), "Arial", "sans-serif"]  # Optional: change font
+)
+
+# Create Gradio interface with the custom theme
+with gr.Blocks(theme=custom_theme) as demo:
+    gr.Markdown("# Compare Sentiment Analysis Across Models")
+    gr.Markdown("Select a sentence to see sentiment analysis results from multiple models.")
+    
+    dropdown = gr.Dropdown(choices=sentences, label="Select Sentence")
+    text_output = gr.Textbox(label="Selected Sentence", lines=2)
+    sentiment_output = gr.JSON(label="Sentiment Scores")
+    
+    dropdown.change(analyze_sentiment, inputs=[dropdown], outputs=[text_output, sentiment_output])
+
+demo.launch()