src/main.py

import os
import pandas as pd
from datetime import datetime
from dotenv import load_dotenv
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 fin_interpreter import analyze_article

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")

os.makedirs(DATA_DIR, exist_ok=True)
os.makedirs(HTML_DIR, exist_ok=True)

load_dotenv()

# === Priority Logic ===
def derive_priority(sentiment, confidence):
    sentiment = sentiment.lower()
    if sentiment == "positive" and confidence > 0.7:
        return "High"
    if sentiment == "negative" and confidence > 0.6:
        return "High"
    if confidence > 0.5:
        return "Medium"
    return "Low"

# === Metrics Box ===
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 Analysis ===
def run_value_investing_analysis(csv_path, progress_callback=None):
    current_df = pd.read_csv(csv_path)
    all_articles = []
    company_data = []

    for _, row in current_df.iterrows():
        topic = row.get("topic")
        timespan = row.get("timespan_days", 7)
        # if progress_callback:
        #     progress_callback(f"🔍 Processing topic: {topic} ({timespan} days)")
        # try:
        #     news = fetch_deep_news(topic, timespan)
        #     if progress_callback:
        #         progress_callback(f"[DEBUG] fetch_deep_news returned {len(news) if news else 0} articles.")
        # except Exception as e:
        #     if progress_callback:
        #         progress_callback(f"[ERROR] fetch_deep_news failed: {e}")
        #     continue
        try:
            news = fetch_deep_news(topic, timespan)
        except Exception as e:
            if progress_callback:
                progress_callback(f"[ERROR] fetch_deep_news failed: {e}")
            continue

        if not news:
            if progress_callback:
                progress_callback(f"⚠️ No news found for topic: {topic}")
            continue

        for article in news:
            summary = article.get("summary", "") or article.get("content", "")
            title = article.get("title", "Untitled")
            url = article.get("url", "")
            date = article.get("date", datetime.now().strftime("%Y-%m-%d"))

            try:
                result = analyze_article(summary)
                sentiment = result.get("sentiment", "Neutral")
                confidence = float(result.get("confidence", 0.0))
                signal = result.get("investment_decision", "Watch")
                #if progress_callback:
                    #progress_callback(f"📰 [{title[:50]}...] → Sentiment: {sentiment}, Confidence: {confidence}, Signal: {signal}")
            except Exception as e:
                if progress_callback:
                    progress_callback(f"[FinBERT ERROR] {e}")
                sentiment, confidence, signal = "Neutral", 0.0, "Watch"

            priority = derive_priority(sentiment, confidence)

            all_articles.append({
                "Title": title,
                "URL": url,
                "Summary": summary[:300] + "..." if summary else "",
                "Priority": priority,
                "Sentiment": sentiment,
                "Confidence": confidence,
                "Signal": signal,
                "Date": date
            })

            company_data.append({
                "Company": topic,
                "Sentiment": sentiment,
                "Confidence": confidence,
                "Signal": signal,
                "Summary": summary,
                "Priority": priority
            })

        try:
            report_body = generate_value_investor_report(topic, news)
            metrics_md = build_metrics_box(topic, len(news))
            full_md = metrics_md + report_body
            filename = f"{topic.replace(' ', '_').lower()}_{datetime.now().strftime('%Y-%m-%d')}.md"
            filepath = os.path.join(DATA_DIR, filename)
            with open(filepath, "w", encoding="utf-8") as f:
                f.write(full_md)
        except Exception as e:
            if progress_callback:
                progress_callback(f"[REPORT ERROR] {e}")

    return all_articles, company_data

# === Insights Tab Data ===
def build_company_insights(company_data):
    if not company_data:
        return pd.DataFrame()

    df = pd.DataFrame(company_data)
    insights = []
    for company, group in df.groupby("Company"):
        mentions = len(group)
        dominant_signal = group["Signal"].mode()[0] if not group["Signal"].mode().empty else "Watch"
        avg_confidence = round(group["Confidence"].mean(), 2)
        high_priority_ratio = round((group['Priority'] == 'High').sum() / len(group) * 100, 1)
        highlights = " | ".join(group["Summary"].head(2).tolist())
        insights.append({
            "Company": company,
            "Mentions": mentions,
            "Dominant Signal": dominant_signal,
            "Avg Confidence": avg_confidence,
            "Interest % (High Priority)": f"{high_priority_ratio}%",
            "Highlights": highlights
        })
    return pd.DataFrame(insights).sort_values(by="Avg Confidence", ascending=False).head(5)

# === Pipeline ===
def run_pipeline(csv_path, tavily_api_key, progress_callback=None):
    os.environ["TAVILY_API_KEY"] = tavily_api_key

    # === Clean old reports (MD and HTML) ===
    for file in os.listdir(DATA_DIR):
        if file.endswith(".md"):
            os.remove(os.path.join(DATA_DIR, file))
    for file in os.listdir(HTML_DIR):
        if file.endswith(".html"):
            os.remove(os.path.join(HTML_DIR, file))

    # === Run the new analysis ===
    all_articles, company_data = run_value_investing_analysis(csv_path, progress_callback)

    html_paths = []
    for md_file in os.listdir(DATA_DIR):
        if md_file.endswith(".md"):
            convert_md_to_html(os.path.join(DATA_DIR, md_file), HTML_DIR)
            html_paths.append(os.path.join(HTML_DIR, md_file.replace(".md", ".html")))

    articles_df = pd.DataFrame(all_articles)
    insights_df = build_company_insights(company_data)
    return html_paths, articles_df, insights_df

# import os
# import pandas as pd
# from datetime import datetime
# from dotenv import load_dotenv
# 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 fin_interpreter import analyze_article

# 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")

# os.makedirs(DATA_DIR, exist_ok=True)
# os.makedirs(HTML_DIR, exist_ok=True)

# load_dotenv()

# # === Priority Logic ===
# def derive_priority(sentiment, confidence):
#     sentiment = sentiment.lower()
#     if sentiment == "positive" and confidence > 0.7:
#         return "High"
#     if sentiment == "negative" and confidence > 0.6:
#         return "High"
#     if confidence > 0.5:
#         return "Medium"
#     return "Low"

# # === Main Analysis ===
# def run_value_investing_analysis(csv_path, progress_callback=None):
#     current_df = pd.read_csv(csv_path)
#     all_articles = []
#     company_data = []

#     for _, row in current_df.iterrows():
#         topic = row.get("topic")
#         timespan = row.get("timespan_days", 7)
#         if progress_callback:
#             progress_callback(f"🔍 Processing topic: {topic} ({timespan} days)")

#         try:
#             news = fetch_deep_news(topic, timespan)
#             if progress_callback:
#                 progress_callback(f"[DEBUG] fetch_deep_news returned {len(news) if news else 0} articles.")
#         except Exception as e:
#             if progress_callback:
#                 progress_callback(f"[ERROR] fetch_deep_news failed: {e}")
#             continue

#         if not news:
#             if progress_callback:
#                 progress_callback(f"⚠️ No news found for topic: {topic}")
#             continue

#         for article in news:
#             summary = article.get("summary", "") or article.get("content", "")
#             title = article.get("title", "Untitled")
#             url = article.get("url", "")
#             date = article.get("date", datetime.now().strftime("%Y-%m-%d"))

#             try:
#                 result = analyze_article(summary)
#                 sentiment = result.get("sentiment", "Neutral")
#                 confidence = float(result.get("confidence", 0.0))
#                 if progress_callback:
#                     progress_callback(f"📰 [{title[:50]}...] → Sentiment: {sentiment}, Confidence: {confidence}")
#             except Exception as e:
#                 if progress_callback:
#                     progress_callback(f"[FinBERT ERROR] {e}")
#                 sentiment, confidence = "Neutral", 0.0

#             priority = derive_priority(sentiment, confidence)

#             all_articles.append({
#                 "Title": title,
#                 "URL": url,
#                 "Summary": summary[:300] + "..." if summary else "",
#                 "Priority": priority,
#                 "Date": date,
#                 "Sentiment": sentiment,
#                 "Confidence": confidence
#             })

#             company_data.append({
#                 "Company": topic,
#                 "Sentiment": sentiment,
#                 "Confidence": confidence,
#                 "Summary": summary,
#             })

#         try:
#             report_body = generate_value_investor_report(topic, news)
#             filename = f"{topic.replace(' ', '_').lower()}_{datetime.now().strftime('%Y-%m-%d')}.md"
#             filepath = os.path.join(DATA_DIR, filename)
#             with open(filepath, "w", encoding="utf-8") as f:
#                 f.write(report_body)
#         except Exception as e:
#             if progress_callback:
#                 progress_callback(f"[REPORT ERROR] {e}")

#     return all_articles, company_data

# # === Insights Tab Data ===
# def build_company_insights(company_data):
#     if not company_data:
#         return pd.DataFrame()

#     df = pd.DataFrame(company_data)
#     insights = []
#     for company, group in df.groupby("Company"):
#         mentions = len(group)
#         dominant_sentiment = group["Sentiment"].mode()[0] if not group["Sentiment"].mode().empty else "Neutral"
#         avg_confidence = round(group["Confidence"].mean(), 2)
#         highlights = " | ".join(group["Summary"].head(2).tolist())
#         insights.append({
#             "Company": company,
#             "Mentions": mentions,
#             "Sentiment": dominant_sentiment,
#             "Confidence": avg_confidence,
#             "Highlights": highlights
#         })
#     return pd.DataFrame(insights).sort_values(by="Confidence", ascending=False).head(5)

# # === Pipeline ===
# def run_pipeline(csv_path, tavily_api_key, progress_callback=None):
#     os.environ["TAVILY_API_KEY"] = tavily_api_key
#     all_articles, company_data = run_value_investing_analysis(csv_path, progress_callback)

#     html_paths = []
#     for md_file in os.listdir(DATA_DIR):
#         if md_file.endswith(".md"):
#             convert_md_to_html(os.path.join(DATA_DIR, md_file), HTML_DIR)
#             html_paths.append(os.path.join(HTML_DIR, md_file.replace(".md", ".html")))

#     articles_df = pd.DataFrame(all_articles)
#     insights_df = build_company_insights(company_data)
#     return html_paths, articles_df, insights_df