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| import streamlit as st | |
| import pandas as pd | |
| from wordcloud import WordCloud | |
| import matplotlib.pyplot as plt | |
| from transformers import pipeline | |
| # Load Hugging Face models | |
| sentiment_analyzer = pipeline("sentiment-analysis") | |
| summarizer = pipeline("summarization") | |
| st.set_page_config(page_title="Sentiment Analysis App", layout="wide") | |
| st.title("📊 Sentiment Analysis of e-Consultation Comments") | |
| st.write("Analyze single or multiple comments: sentiment, summary, and word cloud.") | |
| # --- Sidebar mode selection --- | |
| mode = st.sidebar.radio("Choose mode:", ["Single Comment", "Upload File"]) | |
| # --- Mode 1: Single Comment Analysis --- | |
| if mode == "Single Comment": | |
| user_input = st.text_area("Enter a comment:", height=150) | |
| if st.button("Analyze Comment"): | |
| if user_input.strip(): | |
| # Sentiment | |
| sentiment = sentiment_analyzer(user_input)[0] | |
| st.subheader("🔹 Sentiment Analysis") | |
| st.write(f"**Label:** {sentiment['label']} | **Score:** {sentiment['score']:.2f}") | |
| # Summarization (if long enough) | |
| if len(user_input.split()) > 30: | |
| summary = summarizer(user_input, max_length=50, min_length=20, do_sample=False)[0]['summary_text'] | |
| st.subheader("🔹 Summary") | |
| st.write(summary) | |
| else: | |
| st.info("Not enough text for summarization (need > 30 words).") | |
| # Word Cloud | |
| st.subheader("🔹 Word Cloud") | |
| wordcloud = WordCloud(width=800, height=400, background_color="white").generate(user_input) | |
| fig, ax = plt.subplots(figsize=(10, 5)) | |
| ax.imshow(wordcloud, interpolation="bilinear") | |
| ax.axis("off") | |
| st.pyplot(fig) | |
| else: | |
| st.warning("⚠️ Please enter a comment before analyzing.") | |
| # --- Mode 2: Batch Analysis from File --- | |
| else: | |
| st.info("Upload a CSV or Excel file containing a column named **comment**.") | |
| uploaded_file = st.file_uploader("Upload file", type=["csv", "xlsx"]) | |
| if uploaded_file: | |
| # Read file | |
| if uploaded_file.name.endswith(".csv"): | |
| df = pd.read_csv(uploaded_file) | |
| else: | |
| df = pd.read_excel(uploaded_file) | |
| if "comment" not in df.columns: | |
| st.error("❌ File must contain a column named 'comment'.") | |
| else: | |
| st.write("### Uploaded Data", df.head()) | |
| if st.button("Analyze All Comments"): | |
| sentiments = [] | |
| all_text = " " | |
| for text in df["comment"].dropna(): | |
| result = sentiment_analyzer(str(text))[0] | |
| sentiments.append(result["label"]) | |
| all_text += " " + str(text) | |
| df["sentiment"] = sentiments | |
| st.subheader("🔹 Sentiment Results") | |
| st.write(df) | |
| # Overall Word Cloud | |
| st.subheader("🔹 Word Cloud (All Comments)") | |
| wordcloud = WordCloud(width=800, height=400, background_color="white").generate(all_text) | |
| fig, ax = plt.subplots(figsize=(10, 5)) | |
| ax.imshow(wordcloud, interpolation="bilinear") | |
| ax.axis("off") | |
| st.pyplot(fig) | |
| # Sentiment Distribution | |
| st.subheader("🔹 Sentiment Distribution") | |
| st.bar_chart(df["sentiment"].value_counts()) | |