app.py

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
from sentiment_decorators import sentiment_analysis_decorator
import transformers
from langchain_community.llms import HuggingFacePipeline
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from langchain_core.runnables import RunnablePassthrough
from huggingface_hub import login
from accelerate import init_empty_weights
import logging
import os
from transformers import MarianMTModel, MarianTokenizer

class TranslationModel:
    def __init__(self, model_name="Helsinki-NLP/opus-mt-ru-en"):
        self.tokenizer = MarianTokenizer.from_pretrained(model_name)
        self.model = MarianMTModel.from_pretrained(model_name)
        if torch.cuda.is_available():
            self.model = self.model.to('cuda')

    def translate(self, text):
        inputs = self.tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512)
        if torch.cuda.is_available():
            inputs = {k: v.to('cuda') for k, v in inputs.items()}
        
        with torch.no_grad():
            translated = self.model.generate(**inputs)
        
        return self.tokenizer.decode(translated[0], skip_special_tokens=True)
    

def batch_translate(texts, batch_size=32):
    translator = TranslationModel()
    translated_texts = []
    
    for i in range(0, len(texts), batch_size):
        batch = texts[i:i+batch_size]
        translations = [translator.translate(text) for text in batch]
        translated_texts.extend(translations)
        
        # Update progress
        progress = (i + len(batch)) / len(texts)
        st.progress(progress)
        st.text(f"Предобработано {i + len(batch)} из {len(texts)} текстов")
    
    return translated_texts



logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)


# 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 authenticate_huggingface():
    # Try to get the token from environment variable first
    hf_token = os.environ.get('HF_TOKEN')
    
    # If not in environment, try Streamlit secrets
    if not hf_token and 'hf_token' in st.secrets:
        hf_token = st.secrets['hf_token']
    
    if hf_token:
        login(token=hf_token)
        return True
    else:
        st.error("Hugging Face token not found. Please set HF_TOKEN environment variable or add it to Streamlit secrets.")
        return False
    
@st.cache_resource
def load_model(model_id):
    tokenizer = transformers.AutoTokenizer.from_pretrained(model_id)
    model = transformers.AutoModelForCausalLM.from_pretrained(
        model_id,
        torch_dtype=torch.float16,
        device_map="cpu",
        low_cpu_mem_usage=True
    )
    return tokenizer, model



def init_langchain_llm():
    model_id = "gpt2"  # Using the publicly available GPT-2 model
    tokenizer = transformers.AutoTokenizer.from_pretrained(model_id)
    model = transformers.AutoModelForCausalLM.from_pretrained(model_id)
    
    pipeline = transformers.pipeline(
        "text-generation",
        model=model,
        tokenizer=tokenizer,
        torch_dtype=torch.float32,
        device_map="auto",
    )
    
    def gpt2_wrapper(prompt):
        result = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7)
        return result[0]['generated_text']
    
    llm = HuggingFacePipeline(pipeline=gpt2_wrapper)
    return llm


def estimate_impact(llm, news_text, entity):
    template = """
    Analyze the following news piece about the entity "{entity}" and estimate its monetary impact in Russian rubles for this entity in the next 6 months. You should estimate the risk of loss or probability of profit.
    
    If a precise monetary estimate is not possible, categorize the impact as one of the following:
    1. "Значительный риск убытков" (Significant risk of loss)
    2. "Умеренный риск убытков" (Moderate risk of loss)
    3. "Незначительный риск убытков" (Minor risk of loss)
    4. "Вероятность прибыли" (Probability of profit)
    5. "Неопределенный эффект" (Uncertain effect)

    Also provide a short reasoning (max 100 words) for your assessment.

    Entity: {entity}
    News: {news}

    Your response should be in the following format:
    Estimated Impact: [Your estimate or category]
    Reasoning: [Your reasoning]
    """
    prompt = PromptTemplate(template=template, input_variables=["entity", "news"])
    chain = prompt | llm | RunnablePassthrough()
    response = chain.invoke({"entity": entity, "news": news_text})
    
    # Parse the response
    impact = "Неопределенный эффект"
    reasoning = "Не удалось получить обоснование"
    
    if isinstance(response, str) and "Estimated Impact:" in response and "Reasoning:" in response:
        impact_part, reasoning_part = response.split("Reasoning:")
        impact = impact_part.split("Estimated Impact:")[1].strip()
        reasoning = reasoning_part.strip()
    
    return impact, reasoning

def process_file_with_llm(df, llm):
    df['LLM_Impact'] = ''
    df['LLM_Reasoning'] = ''
    
    # Create a progress bar
    progress_bar = st.progress(0)
    status_text = st.empty()
    
    total_rows = len(df)
    rows_to_process = df[df[['FinBERT', 'RoBERTa', 'FinBERT-Tone']].isin(['Negative', 'Positive']).any(axis=1)]
    

    for index, row in df.iterrows():
        if any(row[model] in ['Negative', 'Positive'] for model in ['FinBERT', 'RoBERTa', 'FinBERT-Tone']):
            impact, reasoning = estimate_impact(llm, row['Translated'], row['Объект'])  # Use translated text
            df.at[index, 'LLM_Impact'] = impact
            df.at[index, 'LLM_Reasoning'] = reasoning
    # Display each LLM response
            t.write(f"Объект: {row['Объект']}")
            st.write(f"Новость: {row['Заголовок']}")
            st.write(f"Эффект: {impact}")
            st.write(f"Обоснование: {reasoning}")
            st.write("---")  # Add a separator between responses


    # Update progress
        progress = (index + 1) / total_rows
        progress_bar.progress(progress)
        status_text.text(f"Проанализировано {index + 1} из {total_rows} новостей")
    
    # Clear the progress bar and status text
    progress_bar.empty()
    status_text.empty()


    return df

def create_output_file_with_llm(df, uploaded_file, analysis_df):
    wb = load_workbook("sample_file.xlsx")
    
    # Update 'Сводка' sheet
    summary_df = pd.DataFrame({
        'Объект': df['Объект'].unique(),
        'Всего новостей': df.groupby('Объект').size(),
        'Отрицательные': df[df[['FinBERT', 'RoBERTa', 'FinBERT-Tone']].eq('Negative').any(axis=1)].groupby('Объект').size(),
        'Положительные': df[df[['FinBERT', 'RoBERTa', 'FinBERT-Tone']].eq('Positive').any(axis=1)].groupby('Объект').size(),
        'Impact': df.groupby('Объект')['LLM_Impact'].agg(lambda x: x.value_counts().index[0] if x.any() else 'Неопределенный')
    })
    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)
    
    # Update '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'], 'релевантен', sentiment, row['LLM_Impact'], row['Заголовок'], row['Выдержки из текста']])
    
    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)
    
    # Update 'Анализ' sheet
    analysis_df['LLM_Reasoning'] = df['LLM_Reasoning']
    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)

    output = io.BytesIO()
    wb.save(output)
    output.seek(0)
    return output

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'], row['Заголовок'], 'РИСК УБЫТКА', '', row['Выдержки из текста']])
    return pd.DataFrame(analysis_data, columns=['Объект', 'Заголовок', 'Признак', 'Пояснение', 'Текст сообщения'])

# Function for lemmatizing Russian text
def lemmatize_text(text):
    if pd.isna(text):
        return ""
    
    if not isinstance(text, str):
        text = str(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
    input_length = inputs.input_ids.shape[1]
    max_length = max(input_length + 10, int(input_length * 1.5))  # Ensure at least 10 new tokens
    
    # Generate translation
    translated_tokens = translation_model.generate(
        **inputs,
        max_new_tokens=max_length,  # Use max_new_tokens instead of 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"

@sentiment_analysis_decorator
def get_rubert1_sentiment(text):
    result = rubert1(text, truncation=True, max_length=512)[0]
    return get_mapped_sentiment(result)

@sentiment_analysis_decorator
def get_rubert2_sentiment(text):
    result = rubert2(text, truncation=True, max_length=512)[0]
    return get_mapped_sentiment(result)

@sentiment_analysis_decorator
def get_finbert_sentiment(text):
    result = finbert(text, truncation=True, max_length=512)[0]
    return get_mapped_sentiment(result)

@sentiment_analysis_decorator
def get_roberta_sentiment(text):
    result = roberta(text, truncation=True, max_length=512)[0]
    return get_mapped_sentiment(result)

@sentiment_analysis_decorator
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 format_elapsed_time(seconds):
    hours, remainder = divmod(int(seconds), 3600)
    minutes, seconds = divmod(remainder, 60)
    
    time_parts = []
    if hours > 0:
        time_parts.append(f"{hours} час{'ов' if hours != 1 else ''}")
    if minutes > 0:
        time_parts.append(f"{minutes} минут{'' if minutes == 1 else 'ы' if 2 <= minutes <= 4 else ''}")
    if seconds > 0 or not time_parts:  # always show seconds if it's the only non-zero value
        time_parts.append(f"{seconds} секунд{'а' if seconds == 1 else 'ы' if 2 <= seconds <= 4 else ''}")
    
    return " ".join(time_parts)


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)
    
    st.write("Начинаем предобработку текстов...")

    texts = df['Выдержки из текста'].tolist()
    # Data validation
    texts = [str(text) if not pd.isna(text) else "" for text in texts]
    
    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")
    
    translated_texts = batch_translate(lemmatized_texts)
    df['Translated'] = translated_texts


    # 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 61")
    
    # Initialize session state
    if 'processed_df' not in st.session_state:
        st.session_state.processed_df = None
    if 'analysis_df' not in st.session_state:
        st.session_state.analysis_df = None
    if 'llm_analyzed' not in st.session_state:
        st.session_state.llm_analyzed = False
    
    uploaded_file = st.file_uploader("Выбирайте Excel-файл", type="xlsx")
    
    if uploaded_file is not None and st.session_state.processed_df is None:
        start_time = time.time()
        
        st.session_state.processed_df = process_file(uploaded_file)
        st.session_state.analysis_df = create_analysis_data(st.session_state.processed_df)
        
        st.subheader("Предпросмотр данных")
        st.write(st.session_state.processed_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 = st.session_state.processed_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)
        
        st.subheader("Анализ")
        st.dataframe(st.session_state.analysis_df)
        
        output = create_output_file(st.session_state.processed_df, uploaded_file, st.session_state.analysis_df)     
        
        end_time = time.time()
        elapsed_time = end_time - start_time
        formatted_time = format_elapsed_time(elapsed_time)
        st.success(f"Обработка завершена за {formatted_time}.")

        st.download_button(
            label="Скачать результат анализа новостей",
            data=output,
            file_name="результат_анализа_новостей.xlsx",
            mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
        )

    if st.session_state.processed_df is not None and not st.session_state.llm_analyzed:
        if st.button("Что скажет нейросеть?"):
            st.info("Анализ нейросетью начался. Это может занять некоторое время...")
            llm = init_langchain_llm()
            df_with_llm = process_file_with_llm(st.session_state.processed_df, llm)
            output_with_llm = create_output_file_with_llm(df_with_llm, uploaded_file, st.session_state.analysis_df)
            st.success("Анализ нейросетью завершен!")
            st.session_state.llm_analyzed = True
            st.session_state.output_with_llm = output_with_llm

    if st.session_state.llm_analyzed:
        st.download_button(
            label="Скачать результат анализа с оценкой нейросети",
            data=st.session_state.output_with_llm,
            file_name="результат_анализа_с_нейросетью.xlsx",
            mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
        )

if __name__ == "__main__":
    main()