app.py

import streamlit as st
import pandas as pd
import time
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
from transformers import pipeline, MarianMTModel, MarianTokenizer
import matplotlib.pyplot as plt
from pymystem3 import Mystem
import io
from rapidfuzz import fuzz

# Initialize components (VADER, FinBERT, RoBERTa, FinBERT-Tone, Mystem, translation model)

# (Copy the initialization code from your original script)

# Define helper functions (lemmatize_text, translate, get_vader_sentiment...)
# (Copy these functions from your original script)

def process_file(uploaded_file):
    df = pd.read_excel(uploaded_file, sheet_name='Публикации')
    
    # Apply fuzzy deduplication
    df = df.groupby('Объект').apply(lambda x: fuzzy_deduplicate(x, 'Выдержки из текста', 65)).reset_index(drop=True)
    
    # Translate texts
    translated_texts = []
    progress_bar = st.progress(0)
    for i, text in enumerate(df['Выдержки из текста']):
        translated_text = translate(str(text))
        translated_texts.append(translated_text)
        progress_bar.progress((i + 1) / len(df))
    
    # Perform sentiment analysis
    vader_results = [get_vader_sentiment(text) for text in translated_texts]
    finbert_results = [get_finbert_sentiment(text) for text in translated_texts]
    roberta_results = [get_roberta_sentiment(text) for text in translated_texts]
    finbert_tone_results = [get_finbert_tone_sentiment(text) for text in translated_texts]
    
    # Add results to DataFrame
    df['VADER'] = vader_results
    df['FinBERT'] = finbert_results
    df['RoBERTa'] = roberta_results
    df['FinBERT-Tone'] = finbert_tone_results
    
    # Reorder columns
    columns_order = ['Объект', 'VADER', 'FinBERT', 'RoBERTa', 'FinBERT-Tone', 'Выдержки из текста']
    df = df[columns_order]
    
    return df

def main():
    st.title("Sentiment Analysis App")
    
    uploaded_file = st.file_uploader("Choose an Excel file", type="xlsx")
    
    if uploaded_file is not None:
        df = process_file(uploaded_file)
        
        st.subheader("Data Preview")
        st.write(df.head())
        
        st.subheader("Sentiment Distribution")
        fig, axs = plt.subplots(2, 2, figsize=(12, 8))
        fig.suptitle("Sentiment Distribution for Each Model")
        
        models = ['VADER', 'FinBERT', 'RoBERTa', 'FinBERT-Tone']
        for i, model in enumerate(models):
            ax = axs[i // 2, i % 2]
            sentiment_counts = df[model].value_counts()
            sentiment_counts.plot(kind='bar', ax=ax)
            ax.set_title(f"{model} Sentiment")
            ax.set_xlabel("Sentiment")
            ax.set_ylabel("Count")
        
        plt.tight_layout()
        st.pyplot(fig)
        
        # Offer download of results
        output = io.BytesIO()
        with pd.ExcelWriter(output, engine='openpyxl') as writer:
            df.to_excel(writer, index=False)
        output.seek(0)
        st.download_button(
            label="Download results as Excel",
            data=output,
            file_name="sentiment_analysis_results.xlsx",
            mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
        )

if __name__ == "__main__":
    main()