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import streamlit as st
import pandas as pd
import time
import matplotlib.pyplot as plt
from openpyxl.utils.dataframe import dataframe_to_rows
import io
from rapidfuzz import fuzz
import os
from openpyxl import load_workbook
from langchain.prompts import PromptTemplate
from langchain_core.runnables import RunnablePassthrough
from transformers import pipeline
from io import StringIO, BytesIO
import sys
import contextlib
from langchain_openai import ChatOpenAI # Updated import
import pdfkit
from jinja2 import Template
def display_sentiment_results(row, sentiment, impact=None, reasoning=None):
if sentiment == "Negative":
st.markdown(f"""
<div style='color: red; font-weight: bold;'>
Объект: {row['Объект']}<br>
Новость: {row['Заголовок']}<br>
Тональность: {sentiment}<br>
{"Эффект: " + impact + "<br>" if impact else ""}
{"Обоснование: " + reasoning + "<br>" if reasoning else ""}
</div>
""", unsafe_allow_html=True)
elif sentiment == "Positive":
st.markdown(f"""
<div style='color: green; font-weight: bold;'>
Объект: {row['Объект']}<br>
Новость: {row['Заголовок']}<br>
Тональность: {sentiment}<br>
</div>
""", unsafe_allow_html=True)
else:
st.write(f"Объект: {row['Объект']}")
st.write(f"Новость: {row['Заголовок']}")
st.write(f"Тональность: {sentiment}")
st.write("---")
class StreamlitCapture:
def __init__(self):
self.texts = []
def write(self, text):
self.texts.append(str(text))
def save_streamlit_output_to_pdf(texts):
# Create HTML content
html_content = """
<html>
<head>
<meta charset="UTF-8">
<style>
body { font-family: Arial, sans-serif; }
.content { margin: 20px; }
</style>
</head>
<body>
<div class="content">
{% for text in texts %}
<p>{{ text }}</p>
{% endfor %}
</div>
</body>
</html>
"""
template = Template(html_content)
rendered_html = template.render(texts=texts)
try:
# Convert HTML to PDF
pdfkit.from_string(rendered_html, 'result.pdf')
st.success("PDF файл 'result.pdf' успешно создан")
except Exception as e:
st.error(f"Ошибка при создании PDF: {str(e)}")
st.warning("PDF generation requires wkhtmltopdf to be installed")
# Initialize 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")
def translate_text(llm, text):
template = """
Translate this Russian text into English:
"{text}"
Your response should contain only the English translation.
"""
prompt = PromptTemplate(template=template, input_variables=["text"])
chain = prompt | llm | RunnablePassthrough()
response = chain.invoke({"text": text})
# Handle different response types
if hasattr(response, 'content'): # If it's an AIMessage object
return response.content.strip()
elif isinstance(response, str): # If it's a string
return response.strip()
else:
return str(response).strip() # Convert any other type to string
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 analyze_sentiment(text):
finbert_result = get_mapped_sentiment(finbert(text, truncation=True, max_length=512)[0])
roberta_result = get_mapped_sentiment(roberta(text, truncation=True, max_length=512)[0])
finbert_tone_result = get_mapped_sentiment(finbert_tone(text, truncation=True, max_length=512)[0])
# Consider sentiment negative if any model says it's negative
if any(result == "Negative" for result in [finbert_result, roberta_result, finbert_tone_result]):
return "Negative"
elif all(result == "Positive" for result in [finbert_result, roberta_result, finbert_tone_result]):
return "Positive"
return "Neutral"
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 init_langchain_llm():
try:
if 'groq_key' in st.secrets:
groq_api_key = st.secrets['groq_key']
else:
st.error("Groq API key not found in Hugging Face secrets. Please add it with the key 'groq_key'.")
st.stop()
llm = ChatOpenAI(
base_url="https://api.groq.com/openai/v1",
model="llama-3.1-70b-versatile",
openai_api_key=groq_api_key, # Updated parameter name
temperature=0.0
)
return llm
except Exception as e:
st.error(f"Error initializing the Groq LLM: {str(e)}")
st.stop()
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.
If precise monetary estimate is not possible, categorize the impact as one of the following:
1. "Значительный риск убытков"
2. "Умеренный риск убытков"
3. "Незначительный риск убытков"
4. "Вероятность прибыли"
5. "Неопределенный эффект"
Provide brief reasoning (maximum 100 words).
News: {news}
Your response should be in the following format:
Impact: [Your estimate or category]
Reasoning: [Your reasoning]
"""
prompt = PromptTemplate(template=template, input_variables=["entity", "news"])
chain = prompt | llm
response = chain.invoke({"entity": entity, "news": news_text})
impact = "Неопределенный эффект"
reasoning = "Не удалось получить обоснование"
# Extract content from response
response_text = response.content if hasattr(response, 'content') else str(response)
try:
if "Impact:" in response_text and "Reasoning:" in response_text:
impact_part, reasoning_part = response_text.split("Reasoning:")
impact = impact_part.split("Impact:")[1].strip()
reasoning = reasoning_part.strip()
except Exception as e:
st.error(f"Error parsing LLM response: {str(e)}")
return impact, reasoning
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:
time_parts.append(f"{seconds} секунд{'а' if seconds == 1 else 'ы' if 2 <= seconds <= 4 else ''}")
return " ".join(time_parts)
def generate_sentiment_visualization(df):
negative_df = df[df['Sentiment'] == 'Negative']
if negative_df.empty:
st.warning("Не обнаружено негативных упоминаний. Отображаем общую статистику по объектам.")
entity_counts = df['Объект'].value_counts()
else:
entity_counts = negative_df['Объект'].value_counts()
if len(entity_counts) == 0:
st.warning("Нет данных для визуализации.")
return None
fig, ax = plt.subplots(figsize=(12, max(6, len(entity_counts) * 0.5)))
entity_counts.plot(kind='barh', ax=ax)
ax.set_title('Количество негативных упоминаний по объектам')
ax.set_xlabel('Количество упоминаний')
plt.tight_layout()
return fig
def process_file(uploaded_file):
output_capture = StreamlitCapture()
old_stdout = sys.stdout
sys.stdout = output_capture
try:
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()
# Initialize LLM
llm = init_langchain_llm()
if not llm:
st.error("Не удалось инициализировать нейросеть. Пожалуйста, проверьте настройки и попробуйте снова.")
st.stop()
# Deduplication
original_news_count = len(df)
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}.")
# Initialize progress
progress_bar = st.progress(0)
status_text = st.empty()
# Process each news item
df['Translated'] = ''
df['Sentiment'] = ''
df['Impact'] = ''
df['Reasoning'] = ''
for index, row in df.iterrows():
translated_text = translate_text(llm, row['Выдержки из текста'])
df.at[index, 'Translated'] = translated_text
sentiment = analyze_sentiment(translated_text)
df.at[index, 'Sentiment'] = sentiment
if sentiment == "Negative":
impact, reasoning = estimate_impact(llm, translated_text, row['Объект'])
df.at[index, 'Impact'] = impact
df.at[index, 'Reasoning'] = reasoning
# Update progress
progress = (index + 1) / len(df)
progress_bar.progress(progress)
status_text.text(f"Проанализировано {index + 1} из {len(df)} новостей")
# Display results with color coding
display_sentiment_results(row, sentiment,
impact if sentiment == "Negative" else None,
reasoning if sentiment == "Negative" else None)
# Generate PDF at the end of processing
save_streamlit_output_to_pdf(output_capture.texts)
return df
finally:
sys.stdout = old_stdout
def create_analysis_data(df):
analysis_data = []
for _, row in df.iterrows():
if row['Sentiment'] == 'Negative':
analysis_data.append([
row['Объект'],
row['Заголовок'],
'РИСК УБЫТКА',
row['Impact'],
row['Reasoning'],
row['Выдержки из текста']
])
return pd.DataFrame(analysis_data, columns=[
'Объект',
'Заголовок',
'Признак',
'Оценка влияния',
'Обоснование',
'Текст сообщения'
])
def create_output_file(df, uploaded_file):
wb = load_workbook("sample_file.xlsx")
# Sort entities by number of negative publications
entity_stats = pd.DataFrame({
'Объект': df['Объект'].unique(),
'Всего': df.groupby('Объект').size(),
'Негативные': df[df['Sentiment'] == 'Negative'].groupby('Объект').size().fillna(0).astype(int),
'Позитивные': df[df['Sentiment'] == 'Positive'].groupby('Объект').size().fillna(0).astype(int)
}).sort_values('Негативные', ascending=False)
# Calculate most negative impact for each entity
entity_impacts = {}
for entity in df['Объект'].unique():
entity_df = df[df['Объект'] == entity]
negative_impacts = entity_df[entity_df['Sentiment'] == 'Negative']['Impact']
entity_impacts[entity] = negative_impacts.iloc[0] if len(negative_impacts) > 0 else 'Неопределенный эффект'
# Update 'Сводка' sheet
ws = wb['Сводка']
for idx, (entity, row) in enumerate(entity_stats.iterrows(), start=4):
ws.cell(row=idx, column=5, value=entity) # Column E
ws.cell(row=idx, column=6, value=row['Всего']) # Column F
ws.cell(row=idx, column=7, value=row['Негативные']) # Column G
ws.cell(row=idx, column=8, value=row['Позитивные']) # Column H
ws.cell(row=idx, column=9, value=entity_impacts[entity]) # Column I
# Update 'Значимые' sheet
ws = wb['Значимые']
row_idx = 3
for _, row in df.iterrows():
if row['Sentiment'] in ['Negative', 'Positive']:
ws.cell(row=row_idx, column=3, value=row['Объект']) # Column C
ws.cell(row=row_idx, column=4, value='релевантно') # Column D
ws.cell(row=row_idx, column=5, value=row['Sentiment']) # Column E
ws.cell(row=row_idx, column=6, value=row['Impact']) # Column F
ws.cell(row=row_idx, column=7, value=row['Заголовок']) # Column G
ws.cell(row=row_idx, column=8, value=row['Выдержки из текста']) # Column H
row_idx += 1
# Copy 'Публикации' sheet
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)
# Update 'Анализ' sheet
ws = wb['Анализ']
row_idx = 4
for _, row in df[df['Sentiment'] == 'Negative'].iterrows():
ws.cell(row=row_idx, column=5, value=row['Объект']) # Column E
ws.cell(row=row_idx, column=6, value=row['Заголовок']) # Column F
ws.cell(row=row_idx, column=7, value="Риск убытка") # Column G
ws.cell(row=row_idx, column=9, value=row['Выдержки из текста']) # Column I
row_idx += 1
# Update 'Тех.приложение' sheet
tech_df = df[['Объект', 'Заголовок', 'Выдержки из текста', 'Translated', 'Sentiment', 'Impact', 'Reasoning']]
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(tech_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 main():
st.markdown(
"""
<style>
.signature {
position: fixed;
right: 12px;
bottom: 12px;
font-size: 14px;
color: #FF0000;
opacity: 0.9;
z-index: 999;
}
</style>
<div class="signature">denis.pokrovsky.npff</div>
""",
unsafe_allow_html=True
)
st.title("::: анализ мониторинга новостей СКАН-ИНТЕРФАКС (v.3.4):::")
if 'processed_df' not in st.session_state:
st.session_state.processed_df = None
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.subheader("Предпросмотр данных")
preview_df = st.session_state.processed_df[['Объект', 'Заголовок', 'Sentiment', 'Impact']].head()
st.dataframe(preview_df)
analysis_df = create_analysis_data(st.session_state.processed_df)
st.subheader("Анализ")
st.dataframe(analysis_df)
output = create_output_file(st.session_state.processed_df, uploaded_file)
end_time = time.time()
elapsed_time = end_time - start_time
formatted_time = format_elapsed_time(elapsed_time)
st.success(f"Обработка и анализ завершены за {formatted_time}.")
st.download_button(
label="Скачать результат анализа",
data=output,
file_name="результат_анализа.xlsx",
mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
)
if __name__ == "__main__":
main() |