import streamlit as st import pandas as pd import time from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline import matplotlib.pyplot as plt from pymystem3 import Mystem import io from rapidfuzz import fuzz from tqdm.auto import tqdm import time import torch from openpyxl import load_workbook from openpyxl import Workbook from openpyxl.utils.dataframe import dataframe_to_rows # Initialize pymystem3 for lemmatization mystem = Mystem() # Set up the sentiment analyzers finbert = pipeline("sentiment-analysis", model="ProsusAI/finbert") roberta = pipeline("sentiment-analysis", model="cardiffnlp/twitter-roberta-base-sentiment") finbert_tone = pipeline("sentiment-analysis", model="yiyanghkust/finbert-tone") rubert1 = pipeline("sentiment-analysis", model = "DeepPavlov/rubert-base-cased") rubert2 = pipeline("sentiment-analysis", model = "blanchefort/rubert-base-cased-sentiment") def create_analysis_data(df): analysis_data = [] for _, row in df.iterrows(): if any(row[model] == 'Negative' for model in ['FinBERT', 'RoBERTa', 'FinBERT-Tone']): analysis_data.append([row['Объект'], 'РИСК УБЫТКА', row['Заголовок'], row['Выдержки из текста']]) return pd.DataFrame(analysis_data, columns=['Объект', 'Тип риска', 'Заголовок', 'Текст']) # Function for lemmatizing Russian text def lemmatize_text(text): words = text.split() lemmatized_words = [] for word in tqdm(words, desc="Lemmatizing", unit="word"): lemmatized_word = ''.join(mystem.lemmatize(word)) lemmatized_words.append(lemmatized_word) return ' '.join(lemmatized_words) # Translation model for Russian to English model_name = "Helsinki-NLP/opus-mt-ru-en" translation_tokenizer = AutoTokenizer.from_pretrained(model_name) translation_model = AutoModelForSeq2SeqLM.from_pretrained(model_name) translator = pipeline("translation", model="Helsinki-NLP/opus-mt-ru-en") def translate(text): # Tokenize the input text inputs = translation_tokenizer(text, return_tensors="pt", truncation=True) # Calculate max_length based on input length (you may need to adjust this ratio) input_length = inputs.input_ids.shape[1] max_length = min(512, int(input_length * 1.5)) # Generate translation translated_tokens = translation_model.generate( **inputs, max_length=max_length, num_beams=5, no_repeat_ngram_size=2, early_stopping=True ) # Decode the translated tokens translated_text = translation_tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0] return translated_text # Functions for FinBERT, RoBERTa, and FinBERT-Tone with label mapping def get_mapped_sentiment(result): label = result['label'].lower() if label in ["positive", "label_2", "pos", "pos_label"]: return "Positive" elif label in ["negative", "label_0", "neg", "neg_label"]: return "Negative" return "Neutral" def get_rubert1_sentiment(text): result = rubert1(text, truncation=True, max_length=512)[0] return get_mapped_sentiment(result) def get_rubert2_sentiment(text): result = rubert2(text, truncation=True, max_length=512)[0] return get_mapped_sentiment(result) def get_finbert_sentiment(text): result = finbert(text, truncation=True, max_length=512)[0] return get_mapped_sentiment(result) def get_roberta_sentiment(text): result = roberta(text, truncation=True, max_length=512)[0] return get_mapped_sentiment(result) def get_finbert_tone_sentiment(text): result = finbert_tone(text, truncation=True, max_length=512)[0] return get_mapped_sentiment(result) #Fuzzy filter out similar news for the same NER def fuzzy_deduplicate(df, column, threshold=65): seen_texts = [] indices_to_keep = [] for i, text in enumerate(df[column]): if pd.isna(text): indices_to_keep.append(i) continue text = str(text) if not seen_texts or all(fuzz.ratio(text, seen) < threshold for seen in seen_texts): seen_texts.append(text) indices_to_keep.append(i) return df.iloc[indices_to_keep] def process_file(uploaded_file): df = pd.read_excel(uploaded_file, sheet_name='Публикации') required_columns = ['Объект', 'Заголовок', 'Выдержки из текста'] missing_columns = [col for col in required_columns if col not in df.columns] if missing_columns: st.error(f"Error: The following required columns are missing from the input file: {', '.join(missing_columns)}") st.stop() original_news_count = len(df) # Apply fuzzy deduplication df = df.groupby('Объект').apply( lambda x: fuzzy_deduplicate(x, 'Выдержки из текста', 65) ).reset_index(drop=True) remaining_news_count = len(df) duplicates_removed = original_news_count - remaining_news_count st.write(f"Из {original_news_count} новостных сообщений удалены {duplicates_removed} дублирующих. Осталось {remaining_news_count}.") # Translate texts translated_texts = [] lemmatized_texts = [] progress_bar = st.progress(0) progress_text = st.empty() total_news = len(df) texts = df['Выдержки из текста'].tolist() for text in df['Выдержки из текста']: lemmatized_texts.append(lemmatize_text(text)) for i, text in enumerate(lemmatized_texts): translated_text = translate(str(text)) translated_texts.append(translated_text) progress_bar.progress((i + 1) / len(df)) progress_text.text(f"{i + 1} из {total_news} сообщений предобработано") # Perform sentiment analysis rubert2_results = [get_rubert2_sentiment(text) for text in 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] # Create a new DataFrame with processed data processed_df = pd.DataFrame({ 'Объект': df['Объект'], 'Заголовок': df['Заголовок'], # Preserve original 'Заголовок' 'ruBERT2': rubert2_results, 'FinBERT': finbert_results, 'RoBERTa': roberta_results, 'FinBERT-Tone': finbert_tone_results, 'Выдержки из текста': df['Выдержки из текста'], 'Translated': translated_texts }) return processed_df def create_output_file(df, uploaded_file, analysis_df): # Load the sample file to use as a template wb = load_workbook("sample_file.xlsx") # Process data for 'Сводка' sheet entities = df['Объект'].unique() summary_data = [] for entity in entities: entity_df = df[df['Объект'] == entity] total_news = len(entity_df) negative_news = sum((entity_df['FinBERT'] == 'Negative') | (entity_df['RoBERTa'] == 'Negative') | (entity_df['FinBERT-Tone'] == 'Negative')) positive_news = sum((entity_df['FinBERT'] == 'Positive') | (entity_df['RoBERTa'] == 'Positive') | (entity_df['FinBERT-Tone'] == 'Positive')) summary_data.append([entity, total_news, negative_news, positive_news]) summary_df = pd.DataFrame(summary_data, columns=['Объект', 'Всего новостей', 'Отрицательные', 'Положительные']) summary_df = summary_df.sort_values('Отрицательные', ascending=False) # Write 'Сводка' sheet ws = wb['Сводка'] for r_idx, row in enumerate(dataframe_to_rows(summary_df, index=False, header=False), start=4): for c_idx, value in enumerate(row, start=5): ws.cell(row=r_idx, column=c_idx, value=value) # Process data for 'Значимые' sheet significant_data = [] for _, row in df.iterrows(): if any(row[model] in ['Negative', 'Positive'] for model in ['FinBERT', 'RoBERTa', 'FinBERT-Tone']): sentiment = 'Negative' if any(row[model] == 'Negative' for model in ['FinBERT', 'RoBERTa', 'FinBERT-Tone']) else 'Positive' significant_data.append([row['Объект'], sentiment, row['Заголовок'], row['Выдержки из текста']]) # Write 'Значимые' sheet ws = wb['Значимые'] for r_idx, row in enumerate(significant_data, start=3): for c_idx, value in enumerate(row, start=3): ws.cell(row=r_idx, column=c_idx, value=value) # Write 'Анализ' sheet ws = wb['Анализ'] for r_idx, row in enumerate(dataframe_to_rows(analysis_df, index=False, header=False), start=4): for c_idx, value in enumerate(row, start=5): ws.cell(row=r_idx, column=c_idx, value=value) # Copy 'Публикации' sheet from original uploaded file original_df = pd.read_excel(uploaded_file, sheet_name='Публикации') ws = wb['Публикации'] for r_idx, row in enumerate(dataframe_to_rows(original_df, index=False, header=True), start=1): for c_idx, value in enumerate(row, start=1): ws.cell(row=r_idx, column=c_idx, value=value) # Add 'Тех.приложение' sheet with processed data if 'Тех.приложение' not in wb.sheetnames: wb.create_sheet('Тех.приложение') ws = wb['Тех.приложение'] for r_idx, row in enumerate(dataframe_to_rows(df, index=False, header=True), start=1): for c_idx, value in enumerate(row, start=1): ws.cell(row=r_idx, column=c_idx, value=value) # Save the workbook to a BytesIO object output = io.BytesIO() wb.save(output) output.seek(0) return output def main(): st.title("... приступим к анализу... версия 37+") uploaded_file = st.file_uploader("Выбирайте Excel-файл", type="xlsx") if uploaded_file is not None: df = process_file(uploaded_file) st.subheader("Предпросмотр данных") st.write(df.head()) st.subheader("Распределение окраски") fig, axs = plt.subplots(2, 2, figsize=(12, 8)) fig.suptitle("Распределение окраски по моделям") models = ['ruBERT2','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) analysis_df = create_analysis_data(df) st.subheader("Анализ") st.dataframe(analysis_df) # Offer download of results output = create_output_file(df, uploaded_file, analysis_df) st.download_button( label="Скачать результат анализа новостей", data=output, file_name="результат_анализа_новостей.xlsx", mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet" ) if __name__ == "__main__": main()