src/main.py

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("✅ 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"\n🔍 Processing: {topic} ({timespan} days)")

        news = fetch_deep_news(topic, timespan)
        if not news:
            print(f"⚠️ No news found for: {topic}")
            continue

        report_body = generate_value_investor_report(topic, news)
        from image_search import search_unsplash_image

        # Later inside your loop
        image_url, image_credit = search_unsplash_image(topic, os.getenv("OPENAI_API_KEY"))

        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"✅ 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"🌐 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("✅ 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"\n🔍 Processing: {topic} ({timespan} days)")

#         news = fetch_deep_news(topic, timespan)
#         if not news:
#             print(f"⚠️ 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"✅ 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"🌐 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"🌐 All reports converted to HTML at: {HTML_DIR}")