app.py

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

        progress_placeholder = st.empty()
        log_output = st.empty()
        string_buffer = StringIO()

        def write_log(msg):
            print(msg)  # Will go to final log
            progress_placeholder.markdown(f"🔄 {msg}")

        with contextlib.redirect_stdout(string_buffer):
            write_log("🚀 Starting analysis...")
            output_path = run_pipeline(csv_path, tavily_api_key)
            write_log("✅ Finished analysis.")

        logs = string_buffer.getvalue()
        progress_placeholder.empty()  # Clear ephemeral log
        log_output.code(logs)         # Show final full log


        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.")