import streamlit as st import pandas as pd import plotly.express as px import matplotlib.pyplot as plt import plotly.graph_objs as go from wordcloud import WordCloud from transformers import pipeline # Load the pre-trained sentiment analysis model sentiment_model = pipeline("sentiment-analysis", model="siebert/sentiment-roberta-large-english") # Define the Streamlit app's user interface # Set page title and favicon st.set_page_config(page_title="Review Analysis", page_icon=":smiley:") # Add image and heading st.image("Header.png", use_column_width=True) file = st.file_uploader(" ",label_visibility='collapsed',type=["csv"]) # Define the app's functionality if file is not None: # Read the CSV file into a Pandas DataFrame df = pd.read_csv(file) st.markdown(f"

Total reviews: {len(df)-1}

", unsafe_allow_html=True) # Write the total number of records st.markdown( f'

Distribution of Reviews

', unsafe_allow_html=True ) # Apply the sentiment analysis model to each review and store the results in a new column df["Sentiment"] = df["Review"].apply(lambda x: sentiment_model(x)[0]["label"]) # Generate pie chart # Define custom colors colors = ['#30C3C4', '#D1DDDE'] # Generate pie chart sentiment_counts = df["Sentiment"].value_counts() fig = px.pie(sentiment_counts, values=sentiment_counts.values, names=sentiment_counts.index, color_discrete_sequence=colors) st.plotly_chart(fig, use_container_width=True) # Create word clouds for positive and negative reviews positive_reviews = " ".join(df[df["Sentiment"] == "POSITIVE"]["Review"].tolist()) negative_reviews = " ".join(df[df["Sentiment"] == "NEGATIVE"]["Review"].tolist()) # Center-align the word clouds col1, col2 = st.columns(2) with col1: st.markdown( f'

Positive Reviews

', unsafe_allow_html=True ) wc_pos = WordCloud(width=800, height=600, background_color="white", colormap="winter").generate(positive_reviews) st.image(wc_pos.to_array(),use_column_width=True) with col2: st.markdown( f'

Negative Reviews

', unsafe_allow_html=True ) wc_neg = WordCloud(width=800, height=600, background_color="white", colormap="winter").generate(negative_reviews) st.image(wc_neg.to_array(),use_column_width=True) # Display the sentiment of each review as cards st.markdown( f'

Reviews in depth

', unsafe_allow_html=True ) # Add the selectbox to filter sentiments filter_sentiment = st.selectbox("", ["ALL", "POSITIVE", "NEGATIVE"]) # Filter the dataframe based on the selected sentiment if filter_sentiment != "ALL": df = df[df['Sentiment'] == filter_sentiment] # Set the max number of rows to display at a time max_rows = 15 # Create HTML table with no border and centered text table_html = (df.style .set_properties(**{'text-align': 'left','font-family': 'sans-serif'}) .set_table_styles([{'selector': 'th', 'props': [('border', '0px')]}, {'selector': 'td', 'props': [('border', '0px')]}]) .set_table_attributes('style="position: sticky; top: 0;"') .to_html(index=False, escape=False)) # Wrap the table inside a div with a fixed height and scrollable content st.write(f'

{table_html}

', unsafe_allow_html=True,header=True,sticky_header=True) def convert_df(df): # IMPORTANT: Cache the conversion to prevent computation on every rerun return df.to_csv().encode('utf-8') csv = convert_df(df) # Add some space between the download button and the table st.write("

", unsafe_allow_html=True) st.download_button( label="Download data as CSV", data=csv, file_name='Review Sentiments.csv' ) st.write("
", unsafe_allow_html=True) st.caption('

crafted with ❤️

', unsafe_allow_html=True)