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import os
import sys
import tempfile
import streamlit as st
import pandas as pd
# Add 'src' to Python path
sys.path.append(os.path.join(os.path.dirname(__file__), 'src'))
from main import run_pipeline
st.set_page_config(page_title="📰 AI News Analyzer", layout="wide")
st.title("🧠 AI-Powered Investing News Analyzer")
# --- API Keys ---
st.subheader("🔐 API Keys")
openai_api_key = st.text_input("OpenAI API Key", type="password").strip()
tavily_api_key = st.text_input("Tavily API Key", type="password").strip()
# --- Topics ---
st.subheader("📈 Topics of Interest")
topics_data = []
with st.form("topics_form"):
topic_count = st.number_input("How many topics?", min_value=1, max_value=10, value=1, step=1)
for i in range(topic_count):
col1, col2 = st.columns(2)
with col1:
topic = st.text_input(f"Topic {i+1}", key=f"topic_{i}")
with col2:
days = st.number_input(f"Timespan (days)", min_value=1, max_value=30, value=7, key=f"days_{i}")
topics_data.append({"topic": topic, "timespan_days": days})
submitted = st.form_submit_button("Run Analysis")
# --- Tabs ---
tab_report, tab_articles, tab_insights = st.tabs(["📝 Report", "📋 Articles", "📊 Insights"])
articles_df = pd.DataFrame()
insights_df = pd.DataFrame()
html_paths = []
if submitted:
if not openai_api_key or not tavily_api_key or not all([td['topic'] for td in topics_data]):
st.warning("Please fill in all fields.")
else:
os.environ["OPENAI_API_KEY"] = openai_api_key
os.environ["TAVILY_API_KEY"] = tavily_api_key
df = pd.DataFrame(topics_data)
with tempfile.NamedTemporaryFile(delete=False, suffix=".csv") as tmp_csv:
df.to_csv(tmp_csv.name, index=False)
csv_path = tmp_csv.name
spinner_box = st.empty()
log_box = st.empty()
logs = []
def log(msg):
logs.append(msg)
log_box.code("\n".join(logs))
try:
spinner_box.markdown("⏳ Running analysis pipeline...")
html_paths, articles_df, insights_df = run_pipeline(csv_path, tavily_api_key, progress_callback=log)
spinner_box.success("✅ Analysis complete!")
# Report Tab
with tab_report:
if html_paths:
for path in html_paths:
with open(path, 'r', encoding='utf-8') as f:
html_content = f.read()
st.components.v1.html(html_content, height=600, scrolling=True)
else:
st.error("❌ No reports were generated.")
# Articles Tab
with tab_articles:
if not articles_df.empty:
st.dataframe(articles_df[["Title", "URL", "Summary", "Priority", "Date"]],
use_container_width=True)
else:
st.info("No articles available.")
# Insights Tab
with tab_insights:
if not insights_df.empty:
st.dataframe(insights_df, use_container_width=True)
else:
st.info("No insights available.")
except Exception as e:
spinner_box.error("❌ Failed.")
log_box.error(f"❌ Error: {e}")
# import os
# import sys
# import tempfile
# import streamlit as st
# import pandas as pd
# # Add 'src' to Python path so we can import main.py
# sys.path.append(os.path.join(os.path.dirname(__file__), 'src'))
# from main import run_pipeline
# st.set_page_config(page_title="📰 AI News Analyzer", layout="wide")
# st.title("🧠 AI-Powered Investing News Analyzer")
# # === API Key Input ===
# st.subheader("🔐 API Keys")
# openai_api_key = st.text_input("OpenAI API Key", type="password").strip()
# tavily_api_key = st.text_input("Tavily API Key", type="password").strip()
# # === Topic Input ===
# st.subheader("📈 Topics of Interest")
# topics_data = []
# with st.form("topics_form"):
# topic_count = st.number_input("How many topics?", min_value=1, max_value=10, value=1, step=1)
# for i in range(topic_count):
# col1, col2 = st.columns(2)
# with col1:
# topic = st.text_input(f"Topic {i+1}", key=f"topic_{i}")
# with col2:
# days = st.number_input(f"Timespan (days)", min_value=1, max_value=30, value=7, key=f"days_{i}")
# topics_data.append({"topic": topic, "timespan_days": days})
# submitted = st.form_submit_button("Run Analysis")
# # === Tabs Setup ===
# tab_report, tab_articles, tab_insights = st.tabs(["📝 Report", "📋 Articles", "📊 Insights"])
# articles_df = pd.DataFrame()
# insights_df = pd.DataFrame()
# html_paths = []
# # === Submission logic ===
# if submitted:
# if not openai_api_key or not tavily_api_key or not all([td['topic'] for td in topics_data]):
# st.warning("Please fill in all fields.")
# else:
# os.environ["OPENAI_API_KEY"] = openai_api_key
# os.environ["TAVILY_API_KEY"] = tavily_api_key
# df = pd.DataFrame(topics_data)
# with tempfile.NamedTemporaryFile(delete=False, suffix=".csv") as tmp_csv:
# df.to_csv(tmp_csv.name, index=False)
# csv_path = tmp_csv.name
# spinner_box = st.empty()
# log_box = st.empty()
# logs = []
# def log(msg):
# logs.append(msg)
# log_box.code("\n".join(logs))
# try:
# spinner_box.markdown("⏳ Running analysis pipeline...")
# # Run the full pipeline
# html_paths, articles_df, insights_df = run_pipeline(csv_path, tavily_api_key, progress_callback=log)
# spinner_box.success("✅ Analysis complete!")
# # --- Report Tab ---
# with tab_report:
# if html_paths:
# for path in html_paths:
# with open(path, 'r', encoding='utf-8') as f:
# html_content = f.read()
# st.components.v1.html(html_content, height=600, scrolling=True)
# else:
# st.error("❌ No reports were generated.")
# # --- Articles Tab ---
# with tab_articles:
# if not articles_df.empty:
# st.dataframe(articles_df, use_container_width=True)
# else:
# st.info("No articles available.")
# # --- Insights Tab ---
# with tab_insights:
# if not insights_df.empty:
# st.dataframe(insights_df, use_container_width=True)
# else:
# st.info("No insights available.")
# except Exception as e:
# spinner_box.error("❌ Failed.")
# log_box.error(f"❌ Error: {e}")
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