deploying sentiment analysis & emotion prediction apps

app.py

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+import streamlit as st
+import pandas as pd
+from transformers import pipeline
+import base64
+
+# 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()