| import streamlit as st | |
| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| from textblob import TextBlob | |
| def load_data(uploaded_file): | |
| df = pd.read_excel(uploaded_file) | |
| return df | |
| def analyze_sentiment(text): | |
| polarity = TextBlob(str(text)).sentiment.polarity | |
| if polarity >= 0.6: | |
| return "Very Positive" | |
| elif polarity >= 0.2: | |
| return "Positive" | |
| elif polarity > -0.2: | |
| return "Neutral" | |
| elif polarity > -0.6: | |
| return "Negative" | |
| else: | |
| return "Very Negative" | |
| st.title("Sentiment Analysis with Pie Chart") | |
| uploaded_file = st.file_uploader("Upload an Excel file with text data", type=["xlsx"]) | |
| if uploaded_file is not None: | |
| df = load_data(uploaded_file) | |
| if "text" not in df.columns: | |
| st.error("Error: The file must contain a 'text' column.") | |
| else: | |
| df["Sentiment"] = df["text"].apply(analyze_sentiment) | |
| st.write("Here is a preview of the data:") | |
| st.write(df.head()) | |
| sentiment_counts = df["Sentiment"].value_counts() | |
| fig, ax = plt.subplots() | |
| ax.pie(sentiment_counts, labels=sentiment_counts.index, autopct="%1.1f%%", colors=["green", "lightgreen", "gray", "orange", "red"]) | |
| ax.set_title("Sentiment Distribution") | |
| st.pyplot(fig) | |
| csv = df.to_csv(index=False) | |
| st.download_button("Download Sentiment Data", csv, "sentiment_results.csv", "text/csv") | |
| else: | |
| st.write("Please upload an Excel file to get started.") | |