Spaces:

sigridveronica
/

ai-news-analyzer

Running

File size: 8,819 Bytes

import os
import sys
import time
import itertools
import streamlit as st
import pandas as pd
from io import StringIO

# 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  # We will update this to return report, articles_df, insights_df

# --- Page Config ---
st.set_page_config(page_title="📰 AI News Analyzer", layout="wide")
st.title("🧠 AI-Powered Investing News Analyzer")

# --- API Key Inputs ---
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:
            timespan = st.number_input(f"Timespan (days) for Topic {i+1}", min_value=1, max_value=30, value=7, step=1, key=f"timespan_{i}")
        topics_data.append((topic, timespan))
    run_button = st.form_submit_button("🚀 Run Analysis")

# --- Placeholder for logs ---
log_placeholder = st.empty()

# --- Results Tabs ---
tabs = st.tabs(["📝 Report", "📋 Articles", "📊 Insights"])

if run_button:
    if not openai_api_key or not tavily_api_key:
        st.error("Please provide both OpenAI and Tavily API keys.")
    else:
        run_button = False  # Disable button
        log_placeholder.info("🔍 Starting analysis...")

        # Rotating status messages
        status_placeholder = st.empty()
        steps = ["Running Tavily search...", "Analyzing with FinBERT & FinGPT...", "Generating LLM summary..."]
        cycle = itertools.cycle(steps)

        # Display rotating messages while running pipeline
        with st.spinner("Working..."):
            for _ in range(3):
                status_placeholder.text(next(cycle))
                time.sleep(0.8)

            # Run the pipeline
            try:
                report_md, articles_df, insights_df = run_pipeline(
                    topics=topics_data,
                    openai_api_key=openai_api_key,
                    tavily_api_key=tavily_api_key
                )
                log_placeholder.success("✅ Analysis completed.")
            except Exception as e:
                log_placeholder.error(f"❌ Error: {e}")
                st.stop()

        # --- Report Tab ---
        with tabs[0]:
            st.subheader("📝 AI-Generated Report")
            st.markdown(report_md, unsafe_allow_html=True)

        # --- Articles Tab ---
        with tabs[1]:
            st.subheader("📋 Articles & Priorities")
            if not articles_df.empty:
                # Color code priority
                articles_df['Priority'] = articles_df['Priority'].map(
                    lambda x: "🔴 High" if str(x).lower() == "haute" or str(x).lower() == "high" else "🟢 Low"
                )

                st.dataframe(articles_df, use_container_width=True)

                # CSV download
                csv = articles_df.to_csv(index=False)
                st.download_button("⬇️ Download Articles CSV", data=csv, file_name="articles.csv", mime="text/csv")
            else:
                st.warning("No articles found.")

        # --- Insights Tab ---
        with tabs[2]:
            st.subheader("📊 Company Insights (FinGPT + FinBERT)")
            if insights_df is not None and not insights_df.empty:
                st.dataframe(insights_df, use_container_width=True)
                csv = insights_df.to_csv(index=False)
                st.download_button("⬇️ Download Insights CSV", data=csv, file_name="insights.csv", mime="text/csv")
            else:
                st.info("No company insights generated yet.")



# import os
# import sys
# import tempfile
# import time
# import itertools
# import streamlit as st
# import pandas as pd
# from threading import Thread
# from io import StringIO

# # 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

#         # === UI Elements ===
#         spinner_box = st.empty()   # For rotating messages
#         log_box = st.empty()       # For logs
#         logs = []
#         rotating = True

#         def log(msg):
#             logs.append(msg)
#             log_box.code("\n".join(logs))

#         # === Rotating UI Messages ===
#         def rotating_messages():
#             messages = itertools.cycle([
#                 "🔍 Searching financial news...",
#                 "🧠 Running language models...",
#                 "📊 Analyzing investor sentiment...",
#                 "📝 Summarizing key takeaways...",
#                 "💹 Building markdown reports..."
#             ])
#             while rotating:
#                 spinner_box.markdown(f"⏳ {next(messages)}")
#                 time.sleep(1.5)

#         rotator_thread = Thread(target=rotating_messages)
#         rotator_thread.start()

#         try:
#             # Check API Keys
#             import openai
#             openai.OpenAI(api_key=openai_api_key).models.list()
#             log("✅ OpenAI API key is valid.")

#             import requests
#             tavily_test = requests.post(
#                 "https://api.tavily.com/search",
#                 headers={"Authorization": f"Bearer {tavily_api_key}"},
#                 json={"query": "test", "days": 1, "max_results": 1}
#             )
#             if tavily_test.status_code == 200:
#                 log("✅ Tavily API key is valid.")
#             else:
#                 raise ValueError(f"Tavily error: {tavily_test.status_code} - {tavily_test.text}")

#             # Run the full pipeline
#             log("🚀 Running analysis pipeline...")
#             output_path = run_pipeline(csv_path, tavily_api_key, progress_callback=log)

#             rotating = False
#             rotator_thread.join()
#             spinner_box.success("✅ Analysis complete!")

#             if output_path and isinstance(output_path, list):
#                 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.")

#         except Exception as e:
#             rotating = False
#             rotator_thread.join()
#             spinner_box.error("❌ Failed.")
#             log_box.error(f"❌ Error: {e}")