app.py

import os
import sys
import tempfile
import time
import streamlit as st
import pandas as pd
import requests
import openai

# 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 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"])

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:
        # Reset old results
        articles_df = pd.DataFrame()
        insights_df = pd.DataFrame()
        html_paths = []

        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("⏳ Checking API keys...")

            # === Check OpenAI Key ===
            try:
                client = openai.OpenAI(api_key=openai_api_key)
                client.models.list()
                log("✅ OpenAI API key is valid.")
            except Exception as e:
                log(f"❌ OpenAI API Key Error: {e}")
                st.stop()

            # === Check Tavily Key ===
            try:
                response = requests.post(
                    "https://api.tavily.com/search",
                    headers={"Authorization": f"Bearer {tavily_api_key}"},
                    json={"query": "test", "days": 1, "max_results": 1}
                )
                if response.status_code == 200:
                    log("✅ Tavily API key is valid.")
                else:
                    log(f"❌ Tavily Key Error: {response.status_code} {response.text}")
                    st.stop()
            except Exception as e:
                log(f"❌ Tavily API Key Error: {e}")
                st.stop()

            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:
                st.subheader("📋 Articles Table")
                if not articles_df.empty:
                    st.dataframe(articles_df[["Title", "URL", "Summary", "Priority", "Date"]],
                                 use_container_width=True)
                    st.download_button(
                        label="⬇️ Download Articles CSV",
                        data=articles_df.to_csv(index=False).encode("utf-8"),
                        file_name="articles.csv",
                        mime="text/csv"
                    )
                else:
                    st.info("No articles available.")

            # === Insights Tab ===
            with tab_insights:
                st.subheader("📊 Investment Insights")
                if not insights_df.empty:
                    st.dataframe(insights_df, use_container_width=True)
                    st.download_button(
                        label="⬇️ Download Insights CSV",
                        data=insights_df.to_csv(index=False).encode("utf-8"),
                        file_name="insights.csv",
                        mime="text/csv"
                    )
                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}")