import os import sys from datetime import datetime from dotenv import load_dotenv from image_search import search_unsplash_image from md_html import convert_single_md_to_html as convert_md_to_html from news_analysis import fetch_deep_news, generate_value_investor_report import pandas as pd from csv_utils import detect_changes # Setup paths BASE_DIR = os.path.dirname(os.path.dirname(__file__)) # one level up from src/ DATA_DIR = os.path.join(BASE_DIR, "data") HTML_DIR = os.path.join(BASE_DIR, "html") CSV_PATH = os.path.join(BASE_DIR, "investing_topics.csv") os.makedirs(DATA_DIR, exist_ok=True) os.makedirs(HTML_DIR, exist_ok=True) # Load .env load_dotenv() def build_metrics_box(topic, num_articles): now = datetime.now().strftime("%Y-%m-%d %H:%M") return f""" > Topic: `{topic}` > Articles Collected: `{num_articles}` > Generated: `{now}` > """ def run_value_investing_analysis(csv_path): current_df = pd.read_csv(csv_path) prev_path = os.path.join(BASE_DIR, "investing_topics_prev.csv") if os.path.exists(prev_path): previous_df = pd.read_csv(prev_path) changed_df = detect_changes(current_df, previous_df) if changed_df.empty: print("āœ… No changes detected. Skipping processing.") return [] else: changed_df = current_df new_md_files = [] for _, row in changed_df.iterrows(): topic = row.get("topic") timespan = row.get("timespan_days", 7) print(f"\nšŸ” Processing: {topic} ({timespan} days)") news = fetch_deep_news(topic, timespan) if not news: print(f"āš ļø No news found for: {topic}") continue report_body = generate_value_investor_report(topic, news) image_url, image_credit = search_unsplash_image(topic) metrics_md = build_metrics_box(topic, len(news)) full_md = metrics_md + report_body base_filename = f"{topic.replace(' ', '_').lower()}_{datetime.now().strftime('%Y-%m-%d')}" filename = base_filename + ".md" filepath = os.path.join(DATA_DIR, filename) counter = 1 while os.path.exists(filepath): filename = f"{base_filename}_{counter}.md" filepath = os.path.join(DATA_DIR, filename) counter += 1 with open(filepath, "w", encoding="utf-8") as f: f.write(full_md) new_md_files.append(filepath) print(f"āœ… Markdown saved to: {DATA_DIR}") current_df.to_csv(prev_path, index=False) return new_md_files def run_pipeline(csv_path, tavily_api_key): os.environ["TAVILY_API_KEY"] = tavily_api_key new_md_files = run_value_investing_analysis(csv_path) new_html_paths = [] for md_path in new_md_files: convert_md_to_html(md_path, HTML_DIR) html_path = os.path.join(HTML_DIR, os.path.basename(md_path).replace(".md", ".html")) new_html_paths.append(html_path) return new_html_paths if __name__ == "__main__": md_files = run_value_investing_analysis(CSV_PATH) for md in md_files: convert_md_to_html(md, HTML_DIR) print(f"šŸŒ All reports converted to HTML at: {HTML_DIR}") # import os # import sys # from datetime import datetime # from dotenv import load_dotenv # #rom news_analysis import load_csv, fetch_deep_news, generate_value_investor_report # from image_search import search_unsplash_image # from md_html import convert_md_folder_to_html # from md_html import convert_single_md_to_html as convert_md_to_html # from news_analysis import fetch_deep_news, generate_value_investor_report # import pandas as pd # from csv_utils import detect_changes # # Adds the absolute path of /external to your module path # BASE_DIR = os.path.dirname(os.path.abspath(__file__)) # EXTERNAL_PATH = os.path.join(BASE_DIR, "external") # if EXTERNAL_PATH not in sys.path: # sys.path.append(EXTERNAL_PATH) # # Load .env # load_dotenv() # # === Base Folder Setup === # BASE_DIR = os.path.dirname(os.path.dirname(__file__)) # one level up from src/ # DATA_DIR = os.path.join(BASE_DIR, "data") # HTML_DIR = os.path.join(BASE_DIR, "html") # CSV_PATH = os.path.join(BASE_DIR, "investing_topics.csv") # # Ensure output folders exist # os.makedirs(DATA_DIR, exist_ok=True) # os.makedirs(HTML_DIR, exist_ok=True) # # === Metrics Block === # def build_metrics_box(topic, num_articles): # now = datetime.now().strftime("%Y-%m-%d %H:%M") # return f""" # > Topic: `{topic}` # > Articles Collected: `{num_articles}` # > Generated: `{now}` # > # """ # # === Main Logic === # def run_value_investing_analysis(csv_path): # current_df = pd.read_csv(csv_path) # prev_path = os.path.join(BASE_DIR, "investing_topics_prev.csv") # if os.path.exists(prev_path): # previous_df = pd.read_csv(prev_path) # changed_df = detect_changes(current_df, previous_df) # if changed_df.empty: # print("āœ… No changes detected. Skipping processing.") # return # else: # changed_df = current_df # for _, row in changed_df.iterrows(): # topic = row.get("topic") # timespan = row.get("timespan_days", 7) # print(f"\nšŸ” Processing: {topic} ({timespan} days)") # news = fetch_deep_news(topic, timespan) # if not news: # print(f"āš ļø No news found for: {topic}") # continue # report_body = generate_value_investor_report(topic, news) # image_url, image_credit = search_unsplash_image(topic) # metrics_md = build_metrics_box(topic, len(news)) # full_md = metrics_md + report_body # base_filename = f"{topic.replace(' ', '_').lower()}_{datetime.now().strftime('%Y-%m-%d')}" # filename = base_filename + ".md" # filepath = os.path.join(DATA_DIR, filename) # counter = 1 # while os.path.exists(filepath): # filename = f"{base_filename}_{counter}.md" # filepath = os.path.join(DATA_DIR, filename) # counter += 1 # with open(filepath, "w", encoding="utf-8") as f: # f.write(full_md) # print(f"āœ… Markdown saved to: {DATA_DIR}") # current_df.to_csv(prev_path, index=False) # Save current as previous for next run # #convert_md_folder_to_html(DATA_DIR, HTML_DIR) # #print(f"šŸŒ All reports converted to HTML at: {HTML_DIR}") # # === Streamlit Integration Wrapper === # def run_pipeline(csv_path, tavily_api_key): # """ # Runs the full analysis pipeline for Streamlit. # Returns: # str: Path to the generated HTML report. # """ # os.environ["TAVILY_API_KEY"] = tavily_api_key # run_value_investing_analysis(csv_path) # # Combine all generated markdown into one file # combined_md_path = os.path.join(DATA_DIR, "combined_report.md") # with open(combined_md_path, "w", encoding="utf-8") as outfile: # for fname in os.listdir(DATA_DIR): # if fname.endswith(".md"): # with open(os.path.join(DATA_DIR, fname), "r", encoding="utf-8") as f: # outfile.write(f.read() + "\n\n---\n\n") # # Convert to HTML # # html_output_path = os.path.join(HTML_DIR, "news_report.html") # # convert_md_to_html(combined_md_path, html_output_path) # convert_md_to_html(combined_md_path, HTML_DIR) # html_output_path = os.path.join(HTML_DIR, "combined_report.html") # return html_output_path # # === Run === # if __name__ == "__main__": # run_value_investing_analysis(CSV_PATH) # convert_md_folder_to_html(DATA_DIR, HTML_DIR) # print(f"šŸŒ All reports converted to HTML at: {HTML_DIR}")