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app.py

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+import streamlit as st
+import pandas as pd
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch.nn.functional as F
+import torch
+import io
+import base64
+from stqdm import stqdm
+
+# Define the model and tokenizer
+model_name = 'nlptown/bert-base-multilingual-uncased-sentiment'
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+st.set_page_config(layout="wide")
+
+#defs
+def classify_reviews(reviews):
+    inputs = tokenizer(reviews, return_tensors='pt', truncation=True, padding=True, max_length=512)
+    outputs = model(**inputs)
+    probabilities = F.softmax(outputs.logits, dim=1).tolist()  
+    return probabilities
+
+def top_rating(scores):
+    return scores.index(max(scores)) + 1  
+
+def top_prob(scores):
+    return max(scores)
+
+def get_table_download_link(df):
+    csv = df.to_csv(index=False)
+    b64 = base64.b64encode(csv.encode()).decode()
+    return f'<a href="data:file/csv;base64,{b64}" download="data.csv">Download csv file</a>'
+
+def main():
+    st.title('Sentiment Analysis')
+    st.markdown('Upload an Excel file and select a column to get sentiment analysis.')
+
+    file = st.file_uploader("Upload an excel file", type=['xlsx'])
+    review_column = None
+    df = None
+
+    if file is not None:
+        try:
+            df = pd.read_excel(file)
+            review_column = st.selectbox('Select the reviews column', df.columns)
+            df[review_column] = df[review_column].astype(str)
+        except Exception as e:
+            st.write("An error occurred while reading the uploaded file. Please make sure it's a valid Excel file.")
+            return
+
+    start_button = st.button('Start Analysis')
+
+    if start_button and df is not None:
+        if review_column in df.columns:
+            with st.spinner('Performing sentiment analysis...'):
+                df, df_display = process_reviews(df, review_column)
+
+            display_ratings(df)
+            display_dataframe(df, df_display)
+        else:
+            st.write(f'No column named "{review_column}" found in the uploaded file.')
+            
+
+def process_reviews(df, review_column):
+    with st.spinner('Classifying reviews...'):
+        progress_bar = st.progress(0)
+        total_reviews = len(df[review_column].tolist())
+        review_counter = 0
+
+        batch_size = 50
+        raw_scores = []
+        reviews = df[review_column].tolist()
+        for i in range(0, len(reviews), batch_size):
+            batch_reviews = reviews[i:i+batch_size]
+            batch_scores = classify_reviews(batch_reviews)
+            raw_scores.extend(batch_scores)
+            review_counter += len(batch_reviews)
+            progress_bar.progress(review_counter / total_reviews)
+
+    df_new = df.copy()
+    df_new['raw_scores'] = raw_scores
+    scores_to_df(df_new)
+    df_display = scores_to_percent(df_new.copy())
+
+    # Get all columns excluding the created ones and the review_column
+    remaining_columns = [col for col in df.columns if col not in [review_column, 'raw_scores', 'Weighted Rating', 'Rating', 'Probability', '1 Star', '2 Star', '3 Star', '4 Star', '5 Star']]
+
+    # Reorder the dataframe with selected columns first, created columns next, then the remaining columns
+    df_new = df_new[[review_column, 'Weighted Rating', 'Rating', 'Probability', '1 Star', '2 Star', '3 Star', '4 Star', '5 Star'] + remaining_columns]
+
+    # Reorder df_display as well
+    df_display = df_display[[review_column, 'Weighted Rating', 'Rating', 'Probability', '1 Star', '2 Star', '3 Star', '4 Star', '5 Star'] + remaining_columns]
+
+    return df_new, df_display
+
+
+def scores_to_df(df):
+    for i in range(1, 6):
+        df[f'{i} Star'] = df['raw_scores'].apply(lambda scores: scores[i-1]).round(2)
+
+    df['Rating'] = df['raw_scores'].apply(top_rating)
+    df['Probability'] = df['raw_scores'].apply(top_prob).round(2)
+    # Compute the Weighted Rating
+    df['Weighted Rating'] = sum(df[f'{i} Star']*i for i in range(1, 6))
+    
+    df.drop(columns=['raw_scores'], inplace=True)
+
+def scores_to_percent(df):
+    for i in range(1, 6):
+        df[f'{i} Star'] = df[f'{i} Star'].apply(lambda x: f'{x*100:.0f}%')
+
+    df['Probability'] = df['Probability'].apply(lambda x: f'{x*100:.0f}%')
+
+    return df
+
+def convert_df_to_csv(df):
+   return df.to_csv(index=False).encode('utf-8')
+
+def display_dataframe(df, df_display):
+    csv = convert_df_to_csv(df)
+
+    col1, col2, col3, col4, col5, col6, col7, col8, col9 = st.columns(9)
+
+    with col1:
+        st.download_button(
+            "Download CSV",
+            csv,
+            "data.csv",
+            "text/csv",
+            key='download-csv'
+        )
+
+    st.dataframe(df_display)
+
+def display_ratings(df):
+    cols = st.columns(5)
+
+    for i in range(1, 6):
+        rating_counts = df[df['Rating'] == i].shape[0]
+        cols[i-1].markdown(f"### {rating_counts}")
+        cols[i-1].markdown(f"{'⭐' * i}")
+
+
+
+if __name__ == "__main__":
+    main()

requirements.txt

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+streamlit
+pandas
+transformers
+torch
+stqdm
+openpyxl