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import os
import sys
import tempfile
import streamlit as st
import pandas as pd
from io import StringIO
import contextlib
# 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")
# === 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
# Live log capture
log_output = st.empty()
string_buffer = StringIO()
with contextlib.redirect_stdout(string_buffer), st.spinner("🔍 Running analysis..."):
output_path = run_pipeline(csv_path, tavily_api_key)
logs = string_buffer.getvalue()
log_output.code(logs)
if output_path and isinstance(output_path, list):
st.success("✅ Analysis complete!")
for path in output_path:
if os.path.exists(path):
with open(path, 'r', encoding='utf-8') as file:
html_content = file.read()
filename = os.path.basename(path)
st.download_button(
label=f"📥 Download {filename}",
data=html_content,
file_name=filename,
mime="text/html"
)
st.components.v1.html(html_content, height=600, scrolling=True)
else:
st.error("❌ No reports were generated.")
# 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.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 do you want to analyze?", min_value=1, max_value=10, step=1, value=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:
# timespan = st.number_input(f"Timespan (days) for Topic {i+1}", min_value=1, max_value=30, value=7, key=f"days_{i}")
# topics_data.append({"topic": topic, "timespan_days": timespan})
# submitted = st.form_submit_button("Analyze Topics")
# # === Run pipeline on submit ===
# 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:
# # Set environment variables so downstream modules can use them
# os.environ["OPENAI_API_KEY"] = openai_api_key
# os.environ["TAVILY_API_KEY"] = tavily_api_key
# # Save user topics to temp CSV
# 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
# with st.spinner("Running analysis..."):
# output_path = run_pipeline(csv_path, tavily_api_key)
# if os.path.exists(output_path):
# st.success("✅ Analysis complete!")
# with open(output_path, 'r', encoding='utf-8') as file:
# html_content = file.read()
# st.download_button("📥 Download HTML Report", html_content, file_name="news_report.html", mime="text/html")
# st.components.v1.html(html_content, height=600, scrolling=True)
# else:
# st.error("❌ Something went wrong during the analysis.")
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