Update app.py

app.py

@@ -1,58 +1,60 @@
- import sqlite3
- import pandas as pd
- import streamlit as st
- from transformers import pipeline
- from sklearn.metrics import accuracy_score
- 
- # Load the data into a pandas dataframe
- df = pd.read_csv('https://raw.githubusercontent.com/SrinidhiRaghavan/AI-Sentiment-Analysis-on-IMDB-Dataset/master/test/imdb_te.csv', encoding= 'unicode_escape')
-
- # Create a connection to the database
- conn = sqlite3.connect('movie_reviews.db')
-
- # Add a column for the sentiment labels
- df['sentiment'] = ''
-
- # Load the data into a table
- df.to_sql('movie_reviews', conn, if_exists='replace', index=False)
-
- # Load the pre-trained sentiment analysis model
- classifier = pipeline('sentiment-analysis')
-
- # Extract sentiment labels for the movie reviews
- reviews = conn.execute('SELECT text FROM movie_reviews limit 10')
- for i, row in enumerate(reviews):
-     review = row[0]
-     sentiment = classifier(review[:512])[0]['label']
-     if sentiment == 'POSITIVE':
-         label = 1
-     else:
-         label = 0
-     conn.execute('UPDATE movie_reviews SET sentiment = ? WHERE rowid = ?', (label, i+1))
- conn.commit()
-
- def main():
-     # Load the data from the SQLite database
-     X = pd.read_sql_query('SELECT text FROM movie_reviews limit 10', conn)
-     y = pd.read_sql_query('SELECT sentiment FROM movie_reviews limit 10', conn)
-
-     # Train a logistic regression model on the sentiment labels
-     clf = pipeline('sentiment-analysis')
-     y_pred = [int(result['label'] == 'POSITIVE') for result in clf(X['text'].to_list(), truncation=True)]
-
-     # Evaluate the model on the testing set
-     accuracy = accuracy_score(y['sentiment'].astype(int).to_list(), y_pred)
-
-     # Create a Streamlit app
-     st.title('Sentiment Analysis on Movie Reviews')
-     st.subheader('Accuracy')
-     st.write(f'{accuracy:.2f}')
-
-     st.subheader('Movie Reviews')
-     st.write(X)
-
-     st.subheader('Sentiment Labels')
-     st.write(y)
-
- if __name__ == '__main__':
-     main()
+
+
+import sqlite3
+import pandas as pd
+import streamlit as st
+from transformers import pipeline
+from sklearn.metrics import accuracy_score
+
+# Load the data into a pandas dataframe
+df = pd.read_csv('https://raw.githubusercontent.com/SrinidhiRaghavan/AI-Sentiment-Analysis-on-IMDB-Dataset/master/test/imdb_te.csv', encoding= 'unicode_escape')
+
+# Create a connection to the database
+conn = sqlite3.connect('movie_reviews.db')
+
+# Add a column for the sentiment labels
+df['sentiment'] = ''
+
+# Load the data into a table
+df.to_sql('movie_reviews', conn, if_exists='replace', index=False)
+
+# Load the pre-trained sentiment analysis model
+classifier = pipeline('sentiment-analysis')
+
+# Extract sentiment labels for the movie reviews
+reviews = conn.execute('SELECT text FROM movie_reviews limit 10')
+for i, row in enumerate(reviews):
+review = row[0]
+sentiment = classifier(review[:512])[0]['label']
+if sentiment == 'POSITIVE':
+    label = 1
+else:
+    label = 0
+conn.execute('UPDATE movie_reviews SET sentiment = ? WHERE rowid = ?', (label, i+1))
+conn.commit()
+
+def main():
+    # Load the data from the SQLite database
+    X = pd.read_sql_query('SELECT text FROM movie_reviews limit 10', conn)
+    y = pd.read_sql_query('SELECT sentiment FROM movie_reviews limit 10', conn)
+    
+    # Train a logistic regression model on the sentiment labels
+    clf = pipeline('sentiment-analysis')
+    y_pred = [int(result['label'] == 'POSITIVE') for result in clf(X['text'].to_list(), truncation=True)]
+    
+    # Evaluate the model on the testing set
+    accuracy = accuracy_score(y['sentiment'].astype(int).to_list(), y_pred)
+    
+    # Create a Streamlit app
+    st.title('Sentiment Analysis on Movie Reviews')
+    st.subheader('Accuracy')
+    st.write(f'{accuracy:.2f}')
+    
+    st.subheader('Movie Reviews')
+    st.write(X)
+    
+    st.subheader('Sentiment Labels')
+    st.write(y)
+
+if __name__ == '__main__':
+    main()