Update app.py

app.py

@@ -214,59 +214,6 @@ def load_reviews_from_file(file_path):
         st.error(f"File not found: {file_path}")
     return reviews
 
-# Function to load reviews from a CSV file
-def load_reviews_from_csv(file_path):
-    try:
-        df = pd.read_csv(file_path)
-        return df
-    except FileNotFoundError:
-        st.error(f"File not found: {file_path}")
-        return None
-
-# Function to process each review in the CSV and get the model's predictions
-def process_csv_reviews(df):
-    true_reviews = []
-    for _, row in df.iterrows():
-        review_text = row['Review']
-        label = row['Label']
-        
-        # Get BERT embedding for the review text
-        bert_embedding = get_bert_embedding(review_text.lower())
-        
-        # Get SpaBERT embedding for geo-entities
-        spaBert_embedding, _ = processSpatialEntities(review_text, nlp)
-        
-        # Concatenate BERT and SpaBERT embeddings
-        combined_embedding = torch.cat((bert_embedding, spaBert_embedding), dim=-1)
-        
-        # Get model prediction
-        prediction = get_prediction(combined_embedding)
-        
-        # If prediction is "Not Spam" (0), store the review
-        if prediction == 0:
-            true_reviews.append((review_text, label))
-    
-    # Convert to a DataFrame for easy display
-    return pd.DataFrame(true_reviews, columns=['Review', 'Label'])
-
-st.write("### Process Filtered Reviews CSV")
-csv_file_path = "models/spabert/datasets/filtered_reviews.csv"
-
-if st.button("Process CSV and Find True Reviews"):
-    # Load the CSV file
-    df = load_reviews_from_csv(csv_file_path)
-    
-    if df is not None:
-        # Filter reviews predicted to be "Not Spam"
-        true_reviews_df = process_csv_reviews(df)
-        
-        if not true_reviews_df.empty:
-            st.write("### Reviews Predicted to be Not Spam:")
-            st.dataframe(true_reviews_df)
-        else:
-            st.write("No reviews were predicted to be Not Spam.")
-    else:
-        st.error("Could not load CSV file.")
 
 
 #Demo Section