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shawnkim1997andClaude Opus 4.6
b2acda81ee
Complete migration from Streamlit to Next.js 14 App Router + FastAPI backend. Frontend (Next.js 14): - 10 pages: Overview, Research, Valuation, Technical, Markets, Earnings, News, Portfolio, Filings, Settings - Terminal Noir dark theme with custom Tailwind config - TradingView Lightweight Charts for candlestick/volume - Valuation: DCF, Sensitivity Matrix, Monte Carlo, Tornado, Reverse DCF - Financial Statements table with YoY growth badges and margin rows - SEC EDGAR inline filing viewer with section tabs - News split-view with iframe article embedding - Technical Analysis with RSI, MACD, Bollinger, Fibonacci, Moving Averages - Earnings beat/miss visualization - AI Copilot chat panel with Gemini integration Backend (FastAPI): - 13 routers: market_data, financials, valuation, technical, earnings, insider, edgar, news, portfolio, analysis, chat, estimates, fx - Services: DCF engine, Monte Carlo simulation, sensitivity analysis, risk metrics, SEC parser, technical indicators - yfinance + yahooquery data sources with fallback pattern - SQLite caching layer Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
76 lines
3.2 KiB
Python
76 lines
3.2 KiB
Python
"""
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AI Financial Summary — Gemini-powered analyst report.
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Split from tab8 to keep files under 300 lines.
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"""
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import streamlit as st
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def _df_to_summary_text(df, label: str, max_cols: int = 5) -> str:
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if df is None or df.empty:
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return f"[{label}: No data available]\n"
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cols = df.columns[:max_cols]
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sliced = df[cols]
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lines = [f"=== {label} ===", "Period: " + " | ".join(str(c) for c in cols)]
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for idx in sliced.index:
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vals = " | ".join(
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f"{v:,.0f}" if isinstance(v, (int, float)) and v == v else "N/A"
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for v in sliced.loc[idx]
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)
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lines.append(f" {idx}: {vals}")
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return "\n".join(lines) + "\n"
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def _build_prompt(ticker, inc, bal, cf):
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return f"""You are a **Senior Equity Analyst at Franklin Templeton** with 15+ years of experience. Write a comprehensive financial summary for **{ticker}**.
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{inc}
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{bal}
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{cf}
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Produce a professional report with: 1. Executive Summary, 2. Revenue & Profitability Analysis,
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3. Balance Sheet Health, 4. Cash Flow Quality, 5. Key Ratios & Red Flags,
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6. Investment Thesis (Bull vs Bear), 7. Analyst's Bottom Line.
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Use actual numbers. Under 1,200 words. Markdown formatting."""
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def render_ai_summary(ticker, fin_df, bal_df, cf_df):
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"""Render the AI Financial Summary section."""
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st.markdown("---")
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st.markdown("### AI Financial Summary (Powered by Gemini)")
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google_api_key = (st.session_state.get("google_api_key") or "").strip()
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if not google_api_key:
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st.info("Enter your Google API Key in the sidebar to enable AI summaries.")
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return
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report_key = f"ai_summary_report_{ticker}"
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if st.button("Generate Senior Analyst Report", key=f"btn_ai_{ticker}", type="primary"):
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with st.spinner("Generating analyst report..."):
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try:
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if fin_df is None or fin_df.empty:
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st.error("No financial data available.")
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return
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inc = _df_to_summary_text(fin_df, "Income Statement")
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bal = _df_to_summary_text(bal_df, "Balance Sheet")
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cf = _df_to_summary_text(cf_df, "Cash Flow Statement")
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import google.generativeai as genai
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from config.constants import GEMINI_MODEL
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genai.configure(api_key=google_api_key)
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model = genai.GenerativeModel(GEMINI_MODEL)
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r = model.generate_content(
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_build_prompt(ticker, inc, bal, cf),
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generation_config={"temperature": 0.3, "max_output_tokens": 4096},
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)
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text = (r.text or "").strip()
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if text:
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st.session_state[report_key] = text
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else:
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st.error("Empty response from Gemini.")
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except Exception as e:
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err = str(e).lower()
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if "429" in err or "resource" in err:
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st.error("Rate limit reached. Wait and retry.")
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else:
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st.error(f"Error: {e}")
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if st.session_state.get(report_key):
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st.markdown(st.session_state[report_key])
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st.caption("AI-generated. Verify independently before making investment decisions.")
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