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| import os | |
| import streamlit as st | |
| import pandas as pd | |
| from transformers import pipeline | |
| import base64 | |
| # Set to use CPU only | |
| os.environ['CUDA_VISIBLE_DEVICES'] = '-1' | |
| # Load pipelines | |
| sentiment_pipe = pipeline("text-classification", model="ayameRushia/bert-base-indonesian-1.5G-sentiment-analysis-smsa") | |
| emotion_pipe = pipeline("text-classification", model="azizp128/prediksi-emosi-indobert") | |
| def direct_sentiment_analysis(text): | |
| texts = text.split('\n') # Memisahkan teks berdasarkan baris | |
| # Hasil analisis sentiment | |
| results = [] | |
| for text in texts: | |
| if text.strip(): | |
| result = sentiment_pipe(text)[0] # Melakukan analisis sentiment pada setiap teks | |
| results.append((text, result['label'].lower(), result['score'])) | |
| # Ubah ke DataFrame untuk tampilan tabel | |
| df = pd.DataFrame(results, columns=['Content', 'Sentiment', 'Score']) | |
| return df | |
| def direct_emotion_analysis(text): | |
| texts = text.split('\n') # Memisahkan teks berdasarkan baris | |
| # Hasil analisis sentiment | |
| results = [] | |
| for text in texts: | |
| if text.strip(): | |
| result = emotion_pipe(text)[0] # Melakukan analisis sentiment pada setiap teks | |
| results.append((text, result['label'].lower(), result['score'])) | |
| # Ubah ke DataFrame untuk tampilan tabel | |
| df = pd.DataFrame(results, columns=['Content', 'Emotion', 'Score']) | |
| return df | |
| def process_file_sentiment(file): | |
| if file.name.endswith('.xlsx'): | |
| df = pd.read_excel(file) # Baca file XLSX | |
| elif file.name.endswith('.csv'): | |
| df = pd.read_csv(file) # Baca file CSV | |
| else: | |
| st.error("Format file tidak didukung. Harap unggah file CSV atau XLSX.") | |
| return None | |
| # Analisis sentimen dan tambahkan hasil ke DataFrame | |
| results = [] | |
| for index, row in df.iterrows(): | |
| if pd.notna(row['content']) and isinstance(row['content'], str): | |
| sentiment, score = analyze_sentiment(row['content']) | |
| results.append((row['content'], sentiment, score)) | |
| else: | |
| results.append((row['content'], None, None)) # Menambahkan nilai None jika kosong | |
| df['Sentimen'] = [r[1] for r in results] | |
| df['Skor Sentimen'] = [r[2] for r in results] | |
| return df | |
| def process_file_emotion(file): | |
| if file.name.endswith('.xlsx'): | |
| df = pd.read_excel(file) # Baca file XLSX | |
| elif file.name.endswith('.csv'): | |
| df = pd.read_csv(file) # Baca file CSV | |
| else: | |
| st.error("Format file tidak didukung. Harap unggah file CSV atau XLSX.") | |
| return None | |
| # Prediksi emosi dan tambahkan hasil ke DataFrame | |
| results = [] | |
| for index, row in df.iterrows(): | |
| if pd.notna(row['content']) and isinstance(row['content'], str): | |
| emotion, score = emotion_prediction(row['content']) | |
| results.append((row['content'], emotion, score)) | |
| else: | |
| results.append((row['content'], None, None)) # Menambahkan nilai None jika kosong | |
| df['Emosi'] = [r[1] for r in results] | |
| df['Skor Emosi'] = [r[2] for r in results] | |
| return df | |
| def analyze_sentiment(text): | |
| result = sentiment_pipe(text)[0] | |
| return result['label'].lower(), result['score'] | |
| def emotion_prediction(text): | |
| result = emotion_pipe(text)[0] | |
| return result['label'].lower(), result['score'] | |
| def get_download_link_sentiment(df): | |
| # Generate a link to download the dataframe with Sentimen and Skor Sentimen as CSV | |
| csv = df.to_csv(index=False) | |
| b64 = base64.b64encode(csv.encode()).decode() # Encode CSV to base64 | |
| href = f'<a href="data:file/csv;base64,{b64}" download="analisis_sentimen.csv">Download CSV</a>' | |
| return href | |
| def get_download_link_emotion(df): | |
| # Generate a link to download the dataframe with Emosi and Skor Emosi as CSV | |
| csv = df.to_csv(index=False) | |
| b64 = base64.b64encode(csv.encode()).decode() # Encode CSV to base64 | |
| href = f'<a href="data:file/csv;base64,{b64}" download="prediksi_emosi.csv">Download CSV</a>' | |
| return href | |
| def main(): | |
| st.title("Aplikasi Analisis Sentimen dan Prediksi Emosi by Ramdhani") | |
| # Pilihan Program | |
| program = st.sidebar.selectbox("Pilih Program", ["Analisis Sentiment", "Prediksi Emosi"]) | |
| if program == "Analisis Sentiment": | |
| # Menu untuk analisis sentimen | |
| st.header("Analisis Sentiment") | |
| menu_sentiment = st.sidebar.selectbox("Pilih Metode", ["Analisis Langsung", "Import dari File"]) | |
| if menu_sentiment == "Analisis Langsung": | |
| # Masukan teks untuk analisis sentimen | |
| user_input = st.text_area("Masukkan teks yang ingin dianalisis (pisahkan dengan enter):") | |
| if st.button("Analisis Sentimen"): | |
| df = direct_sentiment_analysis(user_input) | |
| st.write("Hasil Analisis Sentimen:") | |
| st.write(df) | |
| # Tambahkan tombol download CSV | |
| st.markdown(get_download_link_sentiment(df), unsafe_allow_html=True) | |
| elif menu_sentiment == "Import dari File": | |
| st.subheader("Import dari File") | |
| uploaded_file = st.file_uploader("Upload file CSV atau XLSX", type=["csv", "xlsx"]) | |
| if uploaded_file is not None: | |
| df = process_file_sentiment(uploaded_file) | |
| # Tampilkan hasil analisis sentimen | |
| st.write("Hasil Analisis Sentimen:") | |
| st.write(df) | |
| # Tambahkan tombol download CSV | |
| st.markdown(get_download_link_sentiment(df), unsafe_allow_html=True) | |
| elif program == "Prediksi Emosi": | |
| # Menu untuk prediksi emosi | |
| st.header("Prediksi Emosi") | |
| menu_emot = st.sidebar.selectbox("Pilih Metode", ["Prediksi Langsung", "Import dari File"]) | |
| if menu_emot == "Prediksi Langsung": | |
| user_input = st.text_area("Masukkan teks yang ingin dianalisis (pisahkan dengan enter):") | |
| if st.button("Analisis Sentimen"): | |
| df = direct_emotion_analysis(user_input) | |
| st.write("Hasil Analisis Sentimen:") | |
| st.write(df) | |
| # Tambahkan tombol download CSV | |
| st.markdown(get_download_link_emotion(df), unsafe_allow_html=True) | |
| elif menu_emot == "Import dari File": | |
| st.subheader("Import dari File") | |
| uploaded_file = st.file_uploader("Upload file CSV atau XLSX", type=["csv", "xlsx"]) | |
| if uploaded_file is not None: | |
| df = process_file_emotion(uploaded_file) | |
| # Tampilkan hasil prediksi emosi | |
| st.write("Hasil Prediksi Emosi:") | |
| st.write(df) | |
| # Tambahkan tombol download CSV | |
| st.markdown(get_download_link_emotion(df), unsafe_allow_html=True) | |
| if __name__ == "__main__": | |
| main() | |