Spaces:
Running
Running
File size: 7,658 Bytes
9df4cc0 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 |
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)
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"✅ 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}")
|