import os
import sys
from datetime import datetime
from dotenv import load_dotenv
import pandas as pd
from io import BytesIO
import base64
import matplotlib.pyplot as plt

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 create_sentiment_chart_md(topic):
    # Placeholder dummy chart
    dates = pd.date_range(end=datetime.today(), periods=7)
    values = [100 + i * 3 for i in range(7)]

    plt.figure(figsize=(6, 3))
    plt.plot(dates, values, marker='o')
    plt.title(f"📈 Sentiment Trend: {topic}")
    plt.xlabel("Date")
    plt.ylabel("Sentiment")
    plt.grid(True)

    buffer = BytesIO()
    plt.savefig(buffer, format='png')
    plt.close()
    buffer.seek(0)
    encoded = base64.b64encode(buffer.read()).decode("utf-8")
    return f"![Sentiment Trend](data:image/png;base64,{encoded})"

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)
        chart_md = create_sentiment_chart_md(topic)

        metrics_md = build_metrics_box(topic, len(news))
        full_md = metrics_md + report_body + "\n\n" + chart_md

        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
# import pandas as pd

# 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
# 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, 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)

#         if progress_callback:
#             progress_callback(f"🔍 Processing: {topic} ({timespan} days)")

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

#         if progress_callback:
#             progress_callback(f"🧠 Analyzing news for: {topic}")

#         report_body = generate_value_investor_report(topic, news)

#         # Use placeholder image instead of API call
#         image_url = "https://via.placeholder.com/1281x721?text=No+Image"
#         image_credit = "Image unavailable"

#         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

#         if progress_callback:
#             progress_callback(f"📝 Saving markdown for: {topic}")

#         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 reports 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:
#         if progress_callback:
#             progress_callback(f"🌐 Converting to HTML: {os.path.basename(md_path)}")

#         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}")