src/main.py

import os
import pandas as pd
from datetime import datetime
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
from csv_utils import detect_changes
from fin_interpreter import analyze_article  # For FinBERT + FinGPT signals

# === Paths ===
BASE_DIR = os.path.dirname(os.path.dirname(__file__))
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)

def run_pipeline(topics, openai_api_key=None, tavily_api_key=None):
    """
    Main pipeline:
    1. Fetch articles for topics.
    2. Analyze with FinBERT + FinGPT.
    3. Generate markdown report.
    4. Return (report_md, articles_df, insights_df).
    """
    all_articles = []

    # Fetch and analyze articles
    for topic, days in topics:
        try:
            articles = fetch_deep_news(topic, days, tavily_api_key)
            for article in articles:
                sentiment, confidence, signal = analyze_article(article.get("summary", ""))
                all_articles.append({
                    "Title": article.get("title"),
                    "URL": article.get("url"),
                    "Summary": article.get("summary"),
                    "Priority": article.get("priority", "Low"),
                    "Date": article.get("date"),
                    "Company": article.get("company", topic),  # fallback if no company detected
                    "Sentiment": sentiment,
                    "Confidence": confidence,
                    "Signal": signal
                })
        except Exception as e:
            print(f"Error fetching/analyzing articles for topic '{topic}': {e}")

    # Convert to DataFrame
    articles_df = pd.DataFrame(all_articles)

    # Generate Markdown report (existing behavior)
    report_md = ""
    try:
        report_md = generate_value_investor_report(all_articles, openai_api_key)
    except Exception as e:
        print(f"Error generating report: {e}")
        report_md = "Error generating report."

    # Build insights (aggregated by company)
    insights_df = build_company_insights(articles_df)

    return report_md, articles_df, insights_df


def build_company_insights(articles_df):
    """
    Aggregates article data into a company-level insights table.
    Columns: Company, Mentions, Avg Sentiment, Top Signal, Sector
    """
    if articles_df.empty:
        return pd.DataFrame()

    # Simple aggregation
    grouped = (
        articles_df
        .groupby("Company")
        .agg({
            "Title": "count",
            "Sentiment": lambda x: x.mode()[0] if not x.mode().empty else "Neutral",
            "Signal": lambda x: x.mode()[0] if not x.mode().empty else "Watch"
        })
        .reset_index()
        .rename(columns={"Title": "Mentions"})
    )

    # Add a placeholder Sector column (can improve later with classification)
    grouped["Sector"] = grouped["Company"].apply(lambda c: detect_sector_from_company(c))

    return grouped


def detect_sector_from_company(company_name):
    """
    Simple keyword-based sector detection (can be replaced with GPT classification).
    """
    company_name = company_name.lower()
    if "energy" in company_name or "nuclear" in company_name:
        return "Energy"
    elif "fin" in company_name or "bank" in company_name:
        return "Finance"
    elif "chip" in company_name or "semiconductor" in company_name:
        return "Tech Hardware"
    else:
        return "General"


if __name__ == "__main__":
    # Test run (local)
    test_topics = [("nuclear energy", 7)]
    md, art_df, ins_df = run_pipeline(test_topics)
    print(md)
    print(art_df.head())
    print(ins_df.head())



# import os
# import sys
# from datetime import datetime
# from dotenv import load_dotenv
# import pandas as pd

# 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
# from csv_utils import detect_changes

# # === Setup Paths ===
# BASE_DIR = os.path.dirname(os.path.dirname(__file__))
# 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, progress_callback=None):
#     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:
#             if progress_callback:
#                 progress_callback("✅ 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)
#         msg = f"🔍 Processing: {topic} ({timespan} days)"
#         print(msg)
#         if progress_callback:
#             progress_callback(msg)

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

#         report_body = generate_value_investor_report(topic, news)
#         image_url = "https://via.placeholder.com/1281x721?text=No+Image+Available"
#         image_credit = "Image placeholder"

#         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)

#     if progress_callback:
#         progress_callback(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, progress_callback=None):
#     os.environ["TAVILY_API_KEY"] = tavily_api_key

#     new_md_files = run_value_investing_analysis(csv_path, progress_callback)
#     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}")