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The result shall be to generate concise, accurate, and informative summaries of financial news articles. The step-by-step framework has two essential and distinguished phases: 1.)First step phase emphasizes the elaboration of the summarization feature and the capability to summarize financial-related text and content both from selected web pages and from financial news-related API-es using pre-trained language models like T5 and BERT. 2.)Second step phase enhances the sentiment analysis for each summarization excited and outlines the sentiment eather is positive, negative or neutral. The objective is to build a financial news summarization and sentiment analysis pipeline, step-by-step starting with the 1.)Firts step phase and then following the 2.)Second step phase. The result shall be to generate concise, accurate, and informative summaries of financial news articles. The summaries 1.)First step should: accurately reflect the main points and essential details of the original article, be written in clear and fluent language, easy for a non-expert to understand, condense the information while preserving the overall meaning and context, focus on information that is relevant to financial analysis and decision-making (e.g., company performance, market trends, regulatory changes), if the original article is positive, negative, or neutral in tone, the summary should reflect this using pre-trained language models like T5 and BERT For the 1.First step for summarizing task use tools for financial Web scraping of pages : Blumberg Reuters Financial times Finance.si with tools and Python libraries like this one: https://pypi.org/project/beautifulsoup4/ https://scrapy.org/ using pre-trained language models like T5 and BERT. https://huggingface.co/ProsusAI/finbert https://github.com/ProsusAI/finBERT News https://newsapi.org/ https://newsapi.ai/intro-python * The result shall be an accurate classification of the sentiment of financial news articles and their summaries. The sentiment analysis 2.)Second step should: reliably classify the sentiment of the text as positive, negative, or neutral, distinguish between different degrees of sentiment (e.g., strongly positive, mildly negative), correctly interpret potentially ambiguous language (e.g., "volatility" can be positive or negative depending on the investor's perspective). For the 2.)second step analyzing task and understanding the structure and content of a financial news dataset prepare the data for AI models as follows: Data tool & library: Pandas, Numpy, Matplotlib, Seaborn, Pydantic, Google BERT Bidirectional Encoder Representations from Transformers, T5 HugginfFace text-to-text transformer. Text Cleaning: Remove special characters, HTML tags, and stop words. Normalize text (e.g., lowercasing, stemming). Tokenization: Split text into sentences or words. Metadata Extraction: Extract publication date, source, and tags for context. For the Data Collection use tools like Beautiful Soup, Scrapy, NewsAPI Additionally, Modernize the Data Visualization using the Python code script Grafana, and for the evaluation metrics of the pipeline model and the deliverables use ROUGE and F-1 score. https://github.com/grafana/grafana https://play.grafana.org/ Follow this next step-by-step instruction, and wait for the prompt trigger before starting with the 1.)First step phase and also wait for the prompt trigger to proceed and continue with the 2.)Second step phase. Start with the workflow pipeline description elaborating the orchestration for the objective. Then, compile comprehensive deliverables as documentation that explains methodologies for each pipeline stage and provides insights on model, libraries, and tool selection. After that, showcase the codebase architecture, functional scripts, and files for the modular structure: Preprocessing, Summarization, Sentiment analysis, Evaluation, and Visualization of pipeline stages, results, and challenges. For the evaluation metrics of the pipeline model and the deliverables use ROUGE and F-1 score. Do it step-by-step, each phase separately!41:o9,b1:T1fdf,41:o1fdf,[V0_FILE]python:file="src/collectors/web_scraper.py" type="code" from bs4 import BeautifulSoup import requests import logging from typing import Dict, List, Optional class FinancialWebScraper: def __init__(self): self.headers = { 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36' } self.logger = logging.getLogger(__name__) def fetch_article(self, url: str) -> Optional[Dict]: try: response = requests.get(url, headers=self.headers) response.raise_for_status() soup = BeautifulSoup(response.text, 'html.parser') return { 'title': self._extract_title(soup), 'content': self._extract_content(soup), 'date': self._extract_date(soup), 'source': url } except Exception as e: self.logger.error(f"Error fetching {url}: {str(e)}") return None def _extract_title(self, soup: BeautifulSoup) -> str: title = soup.find('h1') or soup.find('title') return title.text.strip() if title else "" def _extract_content(self, soup: BeautifulSoup) -> str: # Remove script and style elements for script in soup(["script", "style"]): script.decompose() # Find main article content (customize selectors based on website structure) article = soup.find('article') or soup.find('main') if article: return ' '.join([p.text.strip() for p in article.find_all('p')]) return "" def _extract_date(self, soup: BeautifulSoup) -> str: date = soup.find('time') or soup.find(class_='date') return date.text.strip() if date else "" [V0_FILE]python:file="src/collectors/news_api.py" type="code" import requests from typing import List, Dict import os from datetime import datetime, timedelta class NewsAPICollector: def __init__(self, api_key: str): self.api_key = api_key self.base_url = "https://newsapi.org/v2" def get_financial_news(self, keywords: List[str] = ["finance", "stock market", "economy"], days: int = 1) -> List[Dict]: endpoint = f"{self.base_url}/everything" # Calculate date range end_date = datetime.now() start_date = end_date - timedelta(days=days) params = { 'q': ' OR '.join(keywords), 'from': start_date.strftime('%Y-%m-%d'), 'to': end_date.strftime('%Y-%m-%d'), 'language': 'en', 'sortBy': 'relevancy', 'apiKey': self.api_key } response = requests.get(endpoint, params=params) response.raise_for_status() return response.json().get('articles', []) [V0_FILE]python:file="src/processors/cleaner.py" type="code" import re from typing import List import nltk from nltk.corpus import stopwords from nltk.tokenize import sent_tokenize class TextCleaner: def __init__(self): nltk.download('stopwords') nltk.download('punkt') self.stop_words = set(stopwords.words('english')) def clean_text(self, text: str) -> str: # Remove HTML tags text = re.sub(r'<[^>]+>', '', text) # Remove special characters and digits text = re.sub(r'[^\w\s.]', '', text) # Remove extra whitespace text = ' '.join(text.split()) return text def split_into_sentences(self, text: str) -> List[str]: return sent_tokenize(text) def remove_stopwords(self, text: str) -> str: words = text.split() filtered_words = [word for word in words if word.lower() not in self.stop_words] return ' '.join(filtered_words) [V0_FILE]python:file="src/analyzers/summarizer.py" type="code" from transformers import T5ForConditionalGeneration, T5Tokenizer import torch from typing import List, Dict import logging class FinancialSummarizer: def __init__(self, model_name: str = "t5-base"): self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') self.tokenizer = T5Tokenizer.from_pretrained(model_name) self.model = T5ForConditionalGeneration.from_pretrained(model_name).to(self.device) self.logger = logging.getLogger(__name__) def generate_summary(self, text: str, max_length: int = 150) -> str: try: # Prepare input text input_text = f"summarize: {text}" # Tokenize input inputs = self.tokenizer.encode( input_text, return_tensors="pt", max_length=1024, truncation=True ).to(self.device) # Generate summary summary_ids = self.model.generate( inputs, max_length=max_length, min_length=40, length_penalty=2.0, num_beams=4, early_stopping=True ) summary = self.tokenizer.decode(summary_ids[0], skip_special_tokens=True) return summary except Exception as e: self.logger.error(f"Error generating summary: {str(e)}") return "" [V0_FILE]python:file="src/analyzers/evaluator.py" type="code" from rouge_score import rouge_scorer from typing import Dict, List import numpy as np class SummaryEvaluator: def __init__(self): self.scorer = rouge_scorer.RougeScorer(['rouge1', 'rouge2', 'rougeL'], use_stemmer=True) def evaluate_summary(self, reference: str, generated: str) -> Dict[str, float]: scores = self.scorer.score(reference, generated) return { 'rouge1_f1': scores['rouge1'].fmeasure, 'rouge2_f1': scores['rouge2'].fmeasure, 'rougeL_f1': scores['rougeL'].fmeasure } def calculate_average_scores(self, all_scores: List[Dict[str, float]]) -> Dict[str, float]: avg_scores = {} for metric in ['rouge1_f1', 'rouge2_f1', 'rougeL_f1']: scores = [score[metric] for score in all_scores] avg_scores[metric] = np.mean(scores) return avg_scores [V0_FILE]python:file="main.py" type="code" import logging from src.collectors.web_scraper import FinancialWebScraper from src.collectors.news_api import NewsAPICollector from src.processors.cleaner import TextCleaner from src.analyzers.summarizer import FinancialSummarizer from src.analyzers.evaluator import SummaryEvaluator def main(): # Setup logging logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) # Initialize components scraper = FinancialWebScraper() news_api = NewsAPICollector(api_key="YOUR_API_KEY") cleaner = TextCleaner() summarizer = FinancialSummarizer() evaluator = SummaryEvaluator() # Collect news logger.info("Collecting news articles...") articles = news_api.get_financial_news(days=1) summaries = [] for article in articles: # Clean text cleaned_text = cleaner.clean_text(article['content']) # Generate summary summary = summarizer.generate_summary(cleaned_text) summaries.append({ 'title': article['title'], 'original_text': article['content'], 'summary': summary, 'source': article['source'] }) # Evaluate summaries logger.info("Evaluating summaries...") evaluation_scores = [] for summary in summaries: scores = evaluator.evaluate_summary( summary['original_text'], summary['summary'] ) evaluation_scores.append(scores) # Calculate average scores avg_scores = evaluator.calculate_average_scores(evaluation_scores) logger.info(f"Average evaluation scores: {avg_scores}") if __name__ == "__main__": main() [V0_FILE]bash pip install transformers torch nltk beautifulsoup4 requests rouge-score numpy [V0_FILE]bash python main.py41:o9,b2:T45e3,41:o45e3,[V0_FILE]python:file="project_structure.py" type="code" project="Financial News Analysis" isMerged="true" financial_news_analysis/ ├── config/ │ ├── __init__.py │ └── settings.py # Configuration parameters ├── data/ │ ├── raw/ # Raw scraped data │ └── processed/ # Processed data ├── src/ │ ├── collectors/ # Data collection modules │ │ ├── __init__.py │ │ ├── web_scraper.py │ │ └── news_api.py │ ├── processors/ # Text processing modules │ │ ├── __init__.py │ │ ├── cleaner.py │ │ └── tokenizer.py │ ├── analyzers/ # Analysis modules │ │ ├── __init__.py │ │ ├── summarizer.py │ │ └── sentiment.py │ └── visualization/ # Visualization modules │ ├── __init__.py │ └── dashboard.py ├── tests/ # Unit tests ├── requirements.txt # Project dependencies └── main.py # Entry point [V0_FILE]python:file="src/collectors/web_scraper.py" type="code" isMerged="true" from bs4 import BeautifulSoup import requests import logging from typing import Dict, List, Optional class FinancialWebScraper: def __init__(self): self.headers = { 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36' } self.logger = logging.getLogger(__name__) def fetch_article(self, url: str) -> Optional[Dict]: try: response = requests.get(url, headers=self.headers) response.raise_for_status() soup = BeautifulSoup(response.text, 'html.parser') return { 'title': self._extract_title(soup), 'content': self._extract_content(soup), 'date': self._extract_date(soup), 'source': url } except Exception as e: self.logger.error(f"Error fetching {url}: {str(e)}") return None def _extract_title(self, soup: BeautifulSoup) -> str: title = soup.find('h1') or soup.find('title') return title.text.strip() if title else "" def _extract_content(self, soup: BeautifulSoup) -> str: # Remove script and style elements for script in soup(["script", "style"]): script.decompose() # Find main article content (customize selectors based on website structure) article = soup.find('article') or soup.find('main') if article: return ' '.join([p.text.strip() for p in article.find_all('p')]) return "" def _extract_date(self, soup: BeautifulSoup) -> str: date = soup.find('time') or soup.find(class_='date') return date.text.strip() if date else "" [V0_FILE]python:file="src/collectors/news_api.py" type="code" isMerged="true" import requests from typing import List, Dict import os from datetime import datetime, timedelta class NewsAPICollector: def __init__(self, api_key: str): self.api_key = api_key self.base_url = "https://newsapi.org/v2" def get_financial_news(self, keywords: List[str] = ["finance", "stock market", "economy"], days: int = 1) -> List[Dict]: endpoint = f"{self.base_url}/everything" # Calculate date range end_date = datetime.now() start_date = end_date - timedelta(days=days) params = { 'q': ' OR '.join(keywords), 'from': start_date.strftime('%Y-%m-%d'), 'to': end_date.strftime('%Y-%m-%d'), 'language': 'en', 'sortBy': 'relevancy', 'apiKey': self.api_key } response = requests.get(endpoint, params=params) response.raise_for_status() return response.json().get('articles', []) [V0_FILE]python:file="src/processors/cleaner.py" type="code" isMerged="true" import re from typing import List import nltk from nltk.corpus import stopwords from nltk.tokenize import sent_tokenize class TextCleaner: def __init__(self): nltk.download('stopwords') nltk.download('punkt') self.stop_words = set(stopwords.words('english')) def clean_text(self, text: str) -> str: # Remove HTML tags text = re.sub(r'<[^>]+>', '', text) # Remove special characters and digits text = re.sub(r'[^\w\s.]', '', text) # Remove extra whitespace text = ' '.join(text.split()) return text def split_into_sentences(self, text: str) -> List[str]: return sent_tokenize(text) def remove_stopwords(self, text: str) -> str: words = text.split() filtered_words = [word for word in words if word.lower() not in self.stop_words] return ' '.join(filtered_words) [V0_FILE]python:file="src/analyzers/summarizer.py" type="code" isMerged="true" from transformers import T5ForConditionalGeneration, T5Tokenizer import torch from typing import List, Dict import logging class FinancialSummarizer: def __init__(self, model_name: str = "t5-base"): self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') self.tokenizer = T5Tokenizer.from_pretrained(model_name) self.model = T5ForConditionalGeneration.from_pretrained(model_name).to(self.device) self.logger = logging.getLogger(__name__) def generate_summary(self, text: str, max_length: int = 150) -> str: try: # Prepare input text input_text = f"summarize: {text}" # Tokenize input inputs = self.tokenizer.encode( input_text, return_tensors="pt", max_length=1024, truncation=True ).to(self.device) # Generate summary summary_ids = self.model.generate( inputs, max_length=max_length, min_length=40, length_penalty=2.0, num_beams=4, early_stopping=True ) summary = self.tokenizer.decode(summary_ids[0], skip_special_tokens=True) return summary except Exception as e: self.logger.error(f"Error generating summary: {str(e)}") return "" [V0_FILE]python:file="src/analyzers/evaluator.py" type="code" isMerged="true" from rouge_score import rouge_scorer from typing import Dict, List import numpy as np class SummaryEvaluator: def __init__(self): self.scorer = rouge_scorer.RougeScorer(['rouge1', 'rouge2', 'rougeL'], use_stemmer=True) def evaluate_summary(self, reference: str, generated: str) -> Dict[str, float]: scores = self.scorer.score(reference, generated) return { 'rouge1_f1': scores['rouge1'].fmeasure, 'rouge2_f1': scores['rouge2'].fmeasure, 'rougeL_f1': scores['rougeL'].fmeasure } def calculate_average_scores(self, all_scores: List[Dict[str, float]]) -> Dict[str, float]: avg_scores = {} for metric in ['rouge1_f1', 'rouge2_f1', 'rougeL_f1']: scores = [score[metric] for score in all_scores] avg_scores[metric] = np.mean(scores) return avg_scores [V0_FILE]python:file="src/analyzers/sentiment.py" type="code" from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch import numpy as np from typing import Dict, List class FinancialSentimentAnalyzer: def __init__(self, model_name: str = "ProsusAI/finbert"): self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') self.tokenizer = AutoTokenizer.from_pretrained(model_name) self.model = AutoModelForSequenceClassification.from_pretrained(model_name).to(self.device) self.labels = ['negative', 'neutral', 'positive'] def analyze_sentiment(self, text: str) -> Dict[str, float]: inputs = self.tokenizer(text, return_tensors="pt", truncation=True, max_length=512).to(self.device) outputs = self.model(**inputs) probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1) sentiment_scores = {label: float(prob) for label, prob in zip(self.labels, probabilities[0])} sentiment_label = max(sentiment_scores, key=sentiment_scores.get) return { 'label': sentiment_label, 'scores': sentiment_scores } def analyze_batch(self, texts: List[str]) -> List[Dict[str, float]]: return [self.analyze_sentiment(text) for text in texts] [V0_FILE]python:file="main.py" type="code" isEdit="true" import logging from src.collectors.web_scraper import FinancialWebScraper from src.collectors.news_api import NewsAPICollector from src.processors.cleaner import TextCleaner from src.analyzers.summarizer import FinancialSummarizer from src.analyzers.sentiment import FinancialSentimentAnalyzer from src.analyzers.evaluator import SummaryEvaluator import json from datetime import datetime def main(): # Setup logging logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) # Initialize components scraper = FinancialWebScraper() news_api = NewsAPICollector(api_key="YOUR_API_KEY") cleaner = TextCleaner() summarizer = FinancialSummarizer() sentiment_analyzer = FinancialSentimentAnalyzer() evaluator = SummaryEvaluator() # Collect news logger.info("Collecting news articles...") articles = news_api.get_financial_news(days=1) results = [] for article in articles: # Clean text cleaned_text = cleaner.clean_text(article['content']) # Generate summary summary = summarizer.generate_summary(cleaned_text) # Analyze sentiment sentiment = sentiment_analyzer.analyze_sentiment(summary) # Evaluate summary evaluation = evaluator.evaluate_summary(article['content'], summary) results.append({ 'title': article['title'], 'original_text': article['content'], 'summary': summary, 'source': article['url'], 'sentiment': sentiment, 'evaluation': evaluation }) # Calculate average scores avg_scores = evaluator.calculate_average_scores([r['evaluation'] for r in results]) logger.info(f"Average evaluation scores: {avg_scores}") # Save results to JSON for visualization timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") with open(f"data/processed/results_{timestamp}.json", "w") as f: json.dump(results, f) logger.info(f"Results saved to data/processed/results_{timestamp}.json") if __name__ == "__main__": main() [V0_FILE]python:file="src/visualization/data_server.py" type="code" from http.server import BaseHTTPRequestHandler, HTTPServer import json from urllib.parse import urlparse, parse_qs import os from typing import List, Dict class DataHandler(BaseHTTPRequestHandler): def do_GET(self): parsed_path = urlparse(self.path) if parsed_path.path == '/data': self.send_response(200) self.send_header('Content-type', 'application/json') self.end_headers() query_components = parse_qs(parsed_path.query) metric = query_components.get('metric', ['sentiment'])[0] data = self.get_data(metric) self.wfile.write(json.dumps(data).encode()) else: self.send_error(404) def get_data(self, metric: str) -> List[Dict]: # Load the most recent results file results_dir = 'data/processed' files = os.listdir(results_dir) if not files: return [] latest_file = max(files, key=lambda f: os.path.getctime(os.path.join(results_dir, f))) with open(os.path.join(results_dir, latest_file), 'r') as f: results = json.load(f) if metric == 'sentiment': return [ { 'target': article['title'], 'datapoints': [ [article['sentiment']['scores']['positive'], article['sentiment']['scores']['neutral'], article['sentiment']['scores']['negative']] ] } for article in results ] elif metric == 'evaluation': return [ { 'target': 'ROUGE Scores', 'datapoints': [ [results[0]['evaluation']['rouge1_f1'], results[0]['evaluation']['rouge2_f1'], results[0]['evaluation']['rougeL_f1']] ] } ] else: return [] def run_server(port=8000): server_address = ('', port) httpd = HTTPServer(server_address, DataHandler) print(f'Starting server on port {port}...') httpd.serve_forever() if __name__ == '__main__': run_server() [V0_FILE]json:file="grafana/dashboard.json" type="code" { "annotations": { "list": [ { "builtIn": 1, "datasource": "-- Grafana --", "enable": true, "hide": true, "iconColor": "rgba(0, 211, 255, 1)", "name": "Annotations & Alerts", "type": "dashboard" } ] }, "editable": true, "gnetId": null, "graphTooltip": 0, "id": 1, "links": [], "panels": [ { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": null, "fieldConfig": { "defaults": {}, "overrides": [] }, "fill": 1, "fillGradient": 0, "gridPos": { "h": 9, "w": 12, "x": 0, "y": 0 }, "hiddenSeries": false, "id": 2, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 1, "nullPointMode": "null", "options": { "alertThreshold": true }, "percentage": false, "pluginVersion": "7.5.7", "pointradius": 2, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "refId": "A", "target": "sentiment", "type": "timeseries" } ], "thresholds": [], "timeFrom": null, "timeRegions": [], "timeShift": null, "title": "Sentiment Analysis", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "buckets": null, "mode": "time", "name": null, "show": true, "values": [] }, "yaxes": [ { "format": "short", "label": null, "logBase": 1, "max": null, "min": null, "show": true }, { "format": "short", "label": null, "logBase": 1, "max": null, "min": null, "show": true } ], "yaxis": { "align": false, "alignLevel": null } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": null, "fieldConfig": { "defaults": {}, "overrides": [] }, "fill": 1, "fillGradient": 0, "gridPos": { "h": 8, "w": 12, "x": 12, "y": 0 }, "hiddenSeries": false, "id": 4, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 1, "nullPointMode": "null", "options": { "alertThreshold": true }, "percentage": false, "pluginVersion": "7.5.7", "pointradius": 2, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "refId": "A", "target": "evaluation", "type": "timeseries" } ], "thresholds": [], "timeFrom": null, "timeRegions": [], "timeShift": null, "title": "ROUGE Scores", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "buckets": null, "mode": "time", "name": null, "show": true, "values": [] }, "yaxes": [ { "format": "short", "label": null, "logBase": 1, "max": null, "min": null, "show": true }, { "format": "short", "label": null, "logBase": 1, "max": null, "min": null, "show": true } ], "yaxis": { "align": false, "alignLevel": null } } ], "schemaVersion": 27, "style": "dark", "tags": [], "templating": { "list": [] }, "time": { "from": "now-6h", "to": "now" }, "timepicker": {}, "timezone": "", "title": "Financial News Analysis", "uid": "financial_news_analysis", "version": 1 } 41:o9,b3:T45e3,41:o45e3,[V0_FILE]python:file="project_structure.py" type="code" project="Financial News Analysis" isMerged="true" financial_news_analysis/ ├── config/ │ ├── __init__.py │ └── settings.py # Configuration parameters ├── data/ │ ├── raw/ # Raw scraped data │ └── processed/ # Processed data ├── src/ │ ├── collectors/ # Data collection modules │ │ ├── __init__.py │ │ ├── web_scraper.py │ │ └── news_api.py │ ├── processors/ # Text processing modules │ │ ├── __init__.py │ │ ├── cleaner.py │ │ └── tokenizer.py │ ├── analyzers/ # Analysis modules │ │ ├── __init__.py │ │ ├── summarizer.py │ │ └── sentiment.py │ └── visualization/ # Visualization modules │ ├── __init__.py │ └── dashboard.py ├── tests/ # Unit tests ├── requirements.txt # Project dependencies └── main.py # Entry point [V0_FILE]python:file="src/collectors/web_scraper.py" type="code" isMerged="true" from bs4 import BeautifulSoup import requests import logging from typing import Dict, List, Optional class FinancialWebScraper: def __init__(self): self.headers = { 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36' } self.logger = logging.getLogger(__name__) def fetch_article(self, url: str) -> Optional[Dict]: try: response = requests.get(url, headers=self.headers) response.raise_for_status() soup = BeautifulSoup(response.text, 'html.parser') return { 'title': self._extract_title(soup), 'content': self._extract_content(soup), 'date': self._extract_date(soup), 'source': url } except Exception as e: self.logger.error(f"Error fetching {url}: {str(e)}") return None def _extract_title(self, soup: BeautifulSoup) -> str: title = soup.find('h1') or soup.find('title') return title.text.strip() if title else "" def _extract_content(self, soup: BeautifulSoup) -> str: # Remove script and style elements for script in soup(["script", "style"]): script.decompose() # Find main article content (customize selectors based on website structure) article = soup.find('article') or soup.find('main') if article: return ' '.join([p.text.strip() for p in article.find_all('p')]) return "" def _extract_date(self, soup: BeautifulSoup) -> str: date = soup.find('time') or soup.find(class_='date') return date.text.strip() if date else "" [V0_FILE]python:file="src/collectors/news_api.py" type="code" isMerged="true" import requests from typing import List, Dict import os from datetime import datetime, timedelta class NewsAPICollector: def __init__(self, api_key: str): self.api_key = api_key self.base_url = "https://newsapi.org/v2" def get_financial_news(self, keywords: List[str] = ["finance", "stock market", "economy"], days: int = 1) -> List[Dict]: endpoint = f"{self.base_url}/everything" # Calculate date range end_date = datetime.now() start_date = end_date - timedelta(days=days) params = { 'q': ' OR '.join(keywords), 'from': start_date.strftime('%Y-%m-%d'), 'to': end_date.strftime('%Y-%m-%d'), 'language': 'en', 'sortBy': 'relevancy', 'apiKey': self.api_key } response = requests.get(endpoint, params=params) response.raise_for_status() return response.json().get('articles', []) [V0_FILE]python:file="src/processors/cleaner.py" type="code" isMerged="true" import re from typing import List import nltk from nltk.corpus import stopwords from nltk.tokenize import sent_tokenize class TextCleaner: def __init__(self): nltk.download('stopwords') nltk.download('punkt') self.stop_words = set(stopwords.words('english')) def clean_text(self, text: str) -> str: # Remove HTML tags text = re.sub(r'<[^>]+>', '', text) # Remove special characters and digits text = re.sub(r'[^\w\s.]', '', text) # Remove extra whitespace text = ' '.join(text.split()) return text def split_into_sentences(self, text: str) -> List[str]: return sent_tokenize(text) def remove_stopwords(self, text: str) -> str: words = text.split() filtered_words = [word for word in words if word.lower() not in self.stop_words] return ' '.join(filtered_words) [V0_FILE]python:file="src/analyzers/summarizer.py" type="code" isMerged="true" from transformers import T5ForConditionalGeneration, T5Tokenizer import torch from typing import List, Dict import logging class FinancialSummarizer: def __init__(self, model_name: str = "t5-base"): self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') self.tokenizer = T5Tokenizer.from_pretrained(model_name) self.model = T5ForConditionalGeneration.from_pretrained(model_name).to(self.device) self.logger = logging.getLogger(__name__) def generate_summary(self, text: str, max_length: int = 150) -> str: try: # Prepare input text input_text = f"summarize: {text}" # Tokenize input inputs = self.tokenizer.encode( input_text, return_tensors="pt", max_length=1024, truncation=True ).to(self.device) # Generate summary summary_ids = self.model.generate( inputs, max_length=max_length, min_length=40, length_penalty=2.0, num_beams=4, early_stopping=True ) summary = self.tokenizer.decode(summary_ids[0], skip_special_tokens=True) return summary except Exception as e: self.logger.error(f"Error generating summary: {str(e)}") return "" [V0_FILE]python:file="src/analyzers/evaluator.py" type="code" isMerged="true" from rouge_score import rouge_scorer from typing import Dict, List import numpy as np class SummaryEvaluator: def __init__(self): self.scorer = rouge_scorer.RougeScorer(['rouge1', 'rouge2', 'rougeL'], use_stemmer=True) def evaluate_summary(self, reference: str, generated: str) -> Dict[str, float]: scores = self.scorer.score(reference, generated) return { 'rouge1_f1': scores['rouge1'].fmeasure, 'rouge2_f1': scores['rouge2'].fmeasure, 'rougeL_f1': scores['rougeL'].fmeasure } def calculate_average_scores(self, all_scores: List[Dict[str, float]]) -> Dict[str, float]: avg_scores = {} for metric in ['rouge1_f1', 'rouge2_f1', 'rougeL_f1']: scores = [score[metric] for score in all_scores] avg_scores[metric] = np.mean(scores) return avg_scores [V0_FILE]python:file="src/analyzers/sentiment.py" type="code" from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch import numpy as np from typing import Dict, List class FinancialSentimentAnalyzer: def __init__(self, model_name: str = "ProsusAI/finbert"): self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') self.tokenizer = AutoTokenizer.from_pretrained(model_name) self.model = AutoModelForSequenceClassification.from_pretrained(model_name).to(self.device) self.labels = ['negative', 'neutral', 'positive'] def analyze_sentiment(self, text: str) -> Dict[str, float]: inputs = self.tokenizer(text, return_tensors="pt", truncation=True, max_length=512).to(self.device) outputs = self.model(**inputs) probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1) sentiment_scores = {label: float(prob) for label, prob in zip(self.labels, probabilities[0])} sentiment_label = max(sentiment_scores, key=sentiment_scores.get) return { 'label': sentiment_label, 'scores': sentiment_scores } def analyze_batch(self, texts: List[str]) -> List[Dict[str, float]]: return [self.analyze_sentiment(text) for text in texts] [V0_FILE]python:file="main.py" type="code" isEdit="true" import logging from src.collectors.web_scraper import FinancialWebScraper from src.collectors.news_api import NewsAPICollector from src.processors.cleaner import TextCleaner from src.analyzers.summarizer import FinancialSummarizer from src.analyzers.sentiment import FinancialSentimentAnalyzer from src.analyzers.evaluator import SummaryEvaluator import json from datetime import datetime def main(): # Setup logging logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) # Initialize components scraper = FinancialWebScraper() news_api = NewsAPICollector(api_key="YOUR_API_KEY") cleaner = TextCleaner() summarizer = FinancialSummarizer() sentiment_analyzer = FinancialSentimentAnalyzer() evaluator = SummaryEvaluator() # Collect news logger.info("Collecting news articles...") articles = news_api.get_financial_news(days=1) results = [] for article in articles: # Clean text cleaned_text = cleaner.clean_text(article['content']) # Generate summary summary = summarizer.generate_summary(cleaned_text) # Analyze sentiment sentiment = sentiment_analyzer.analyze_sentiment(summary) # Evaluate summary evaluation = evaluator.evaluate_summary(article['content'], summary) results.append({ 'title': article['title'], 'original_text': article['content'], 'summary': summary, 'source': article['url'], 'sentiment': sentiment, 'evaluation': evaluation }) # Calculate average scores avg_scores = evaluator.calculate_average_scores([r['evaluation'] for r in results]) logger.info(f"Average evaluation scores: {avg_scores}") # Save results to JSON for visualization timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") with open(f"data/processed/results_{timestamp}.json", "w") as f: json.dump(results, f) logger.info(f"Results saved to data/processed/results_{timestamp}.json") if __name__ == "__main__": main() [V0_FILE]python:file="src/visualization/data_server.py" type="code" from http.server import BaseHTTPRequestHandler, HTTPServer import json from urllib.parse import urlparse, parse_qs import os from typing import List, Dict class DataHandler(BaseHTTPRequestHandler): def do_GET(self): parsed_path = urlparse(self.path) if parsed_path.path == '/data': self.send_response(200) self.send_header('Content-type', 'application/json') self.end_headers() query_components = parse_qs(parsed_path.query) metric = query_components.get('metric', ['sentiment'])[0] data = self.get_data(metric) self.wfile.write(json.dumps(data).encode()) else: self.send_error(404) def get_data(self, metric: str) -> List[Dict]: # Load the most recent results file results_dir = 'data/processed' files = os.listdir(results_dir) if not files: return [] latest_file = max(files, key=lambda f: os.path.getctime(os.path.join(results_dir, f))) with open(os.path.join(results_dir, latest_file), 'r') as f: results = json.load(f) if metric == 'sentiment': return [ { 'target': article['title'], 'datapoints': [ [article['sentiment']['scores']['positive'], article['sentiment']['scores']['neutral'], article['sentiment']['scores']['negative']] ] } for article in results ] elif metric == 'evaluation': return [ { 'target': 'ROUGE Scores', 'datapoints': [ [results[0]['evaluation']['rouge1_f1'], results[0]['evaluation']['rouge2_f1'], results[0]['evaluation']['rougeL_f1']] ] } ] else: return [] def run_server(port=8000): server_address = ('', port) httpd = HTTPServer(server_address, DataHandler) print(f'Starting server on port {port}...') httpd.serve_forever() if __name__ == '__main__': run_server() [V0_FILE]json:file="grafana/dashboard.json" type="code" { "annotations": { "list": [ { "builtIn": 1, "datasource": "-- Grafana --", "enable": true, "hide": true, "iconColor": "rgba(0, 211, 255, 1)", "name": "Annotations & Alerts", "type": "dashboard" } ] }, "editable": true, "gnetId": null, "graphTooltip": 0, "id": 1, "links": [], "panels": [ { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": null, "fieldConfig": { "defaults": {}, "overrides": [] }, "fill": 1, "fillGradient": 0, "gridPos": { "h": 9, "w": 12, "x": 0, "y": 0 }, "hiddenSeries": false, "id": 2, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 1, "nullPointMode": "null", "options": { "alertThreshold": true }, "percentage": false, "pluginVersion": 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NEWS summarization & sentiment .jpg","size":47358,"contentType":"image/jpeg"}],"type":"message","authorId":"b3vwAPdlLT4AkxRmH0zP2gIE","createdAt":"$D2025-01-19T17:14:44.999Z"},{"content":{"type":"message-binary-format","value":[[0,[["p",{},["text",{},"I'll help you create a comprehensive framework for financial news summarization and sentiment analysis. Let's start with the workflow pipeline description."]],["p",{},["text",{},"First, let's visualize the high-level architecture:"]],["Codeblock",{"lang":"mermaid","meta":{"title":"Financial News Analysis Pipeline","type":"diagram"},"closed":true},"graph TD\n A[\"Data Collection Layer\"]\n B[\"Processing Layer\"]\n C[\"Analysis Layer\"]\n D[\"Visualization Layer\"]\n\n A -->|\"Web Scraping/API\"| B\n B -->|\"Clean & Tokenize\"| C\n C -->|\"Results\"| D\n\n subgraph \"Data Collection Layer\"\n A1[\"Web Scraping (BeautifulSoup/Scrapy)\"]\n A2[\"News APIs\"]\n end\n\n subgraph \"Processing Layer\"\n B1[\"Text Cleaning\"]\n B2[\"Tokenization\"]\n B3[\"Metadata Extraction\"]\n end\n\n subgraph \"Analysis Layer\"\n C1[\"Text Summarization (T5)\"]\n C2[\"Sentiment Analysis (FinBERT)\"]\n end\n\n subgraph \"Visualization Layer\"\n D1[\"Grafana Dashboard\"]\n D2[\"ROUGE Metrics\"]\n D3[\"F1 Score\"]\n end\n\n A1 --> B\n A2 --> B\n B1 --> C\n B2 --> C\n B3 --> C\n C1 --> D\n C2 --> D"],["p",{},["text",{},"Let's create the project structure:"]],["Codeblock",{"lang":"python","meta":{"project":"Financial News Analysis","file":"project_structure.py","type":"code"},"closed":true,"blockId":"b_42YnfP1ogMz","source":"financial_news_analysis/\n├── config/\n│ ├── __init__.py\n│ └── settings.py # Configuration parameters\n├── data/\n│ ├── raw/ # Raw scraped data\n│ └── processed/ # Processed data\n├── src/\n│ ├── collectors/ # Data collection modules\n│ │ ├── __init__.py\n│ │ ├── web_scraper.py\n│ │ └── news_api.py\n│ ├── processors/ # Text processing modules\n│ │ ├── __init__.py\n│ │ ├── cleaner.py\n│ │ └── tokenizer.py\n│ ├── analyzers/ # Analysis modules\n│ │ ├── __init__.py\n│ │ ├── summarizer.py\n│ │ └── sentiment.py\n│ └── visualization/ # Visualization modules\n│ ├── __init__.py\n│ └── dashboard.py\n├── tests/ # Unit tests\n├── requirements.txt # Project dependencies\n└── main.py # Entry point","needsBlockSource":false},"financial_news_analysis/\n├── config/\n│ ├── __init__.py\n│ └── settings.py # Configuration parameters\n├── data/\n│ ├── raw/ # Raw scraped data\n│ └── processed/ # Processed data\n├── src/\n│ ├── collectors/ # Data collection modules\n│ │ ├── __init__.py\n│ │ ├── web_scraper.py\n│ │ └── news_api.py\n│ ├── processors/ # Text processing modules\n│ │ ├── __init__.py\n│ │ ├── cleaner.py\n│ │ └── tokenizer.py\n│ ├── analyzers/ # Analysis modules\n│ │ ├── __init__.py\n│ │ ├── summarizer.py\n│ │ └── sentiment.py\n│ └── visualization/ # Visualization modules\n│ ├── __init__.py\n│ └── dashboard.py\n├── tests/ # Unit tests\n├── requirements.txt # Project dependencies\n└── main.py # Entry point"],["p",{},["text",{},"Now, I'll wait for your prompt to proceed with the implementation of the First Phase (Summarization). Once you give the go-ahead, I'll provide:"]],["ol",{"start":1},["li",{"index":1},["text",{},"Data collection implementation (web scraping and API integration)"]],["li",{"index":2},["text",{},"Text preprocessing pipeline"]],["li",{"index":3},["text",{},"Summarization model implementation using T5"]],["li",{"index":4},["text",{},"Initial evaluation metrics"]]],["p",{},["text",{},"After that, we'll wait for another prompt to proceed with the Second Phase (Sentiment Analysis)."]],["p",{},["text",{},"Would you like me to proceed with the implementation of the First Phase?"]]]],[1,{}]]},"context":[],"isContentLoading":false,"role":"assistant","id":"HMOZ0gp39DBhYO2NBYjicb0VyIzBB3t1","gitRevision":null,"streaming":false,"finishReason":"stop","parentId":"r5aP4PrhPZsGxVncVkVXqNWa7LwPPlbD","type":"message","creditCost":0.048201,"workMetrics":{"diffMap":{}},"createdAt":"$D2025-01-19T17:14:45.000Z","updatedAt":"$D2025-01-19T17:15:08.958Z"},{"content":{"type":"parts","parts":[{"type":"mdx","content":"yes, do it. Implement the 1.)First phase - that is financial news summarization feature tool"}],"version":1},"role":"user","id":"wB2gsKr5iMpKF22mMO9nW9UYRRKDxG2k","attachments":[],"parentId":"HMOZ0gp39DBhYO2NBYjicb0VyIzBB3t1","type":"message","authorId":"b3vwAPdlLT4AkxRmH0zP2gIE","createdAt":"$D2025-01-19T17:15:57.299Z"},{"content":{"type":"message-binary-format","value":[[0,[["p",{},["text",{},"Loading..."]]]],[1,{"title":"Loading message"}]]},"loadingBlocks":[{"blockId":"b_K05t1jpdH2g","type":"code-project","isVersioned":true}],"isContentLoading":true,"role":"assistant","id":"CrO8s1p9byfvy8KQOD9BQIbJhpioXKIT","gitRevision":null,"streaming":false,"finishReason":"stop","parentId":"wB2gsKr5iMpKF22mMO9nW9UYRRKDxG2k","type":"message","createdAt":"$D2025-01-19T17:15:57.300Z"},{"content":{"type":"message-binary-format","value":[[0,[["p",{},["text",{},"I'll implement the First Phase focusing on financial news summarization. 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