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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>
67 lines
2.8 KiB
Python
67 lines
2.8 KiB
Python
"""
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KPI section — AI-powered company-specific KPI analysis via Gemini.
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Generates key performance indicators relevant to the company's industry.
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"""
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import streamlit as st
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def render_kpi_section(ticker: str, sector: str, industry: str):
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"""Render AI-generated KPI analysis for a company."""
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st.markdown("---")
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st.markdown("#### Company KPI Analysis (AI-Powered)")
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st.caption(
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"AI identifies and analyzes the most important KPIs for this company's "
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"business model and industry."
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)
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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 KPI analysis.")
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return
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kpi_key = f"kpi_analysis_{ticker}"
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if st.button("Generate KPI Analysis", key=f"btn_kpi_{ticker}"):
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with st.spinner("Analyzing company KPIs with Gemini..."):
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try:
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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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prompt = f"""You are a senior equity research analyst. For **{ticker}** (Sector: {sector}, Industry: {industry}), identify and analyze the **top 5 most critical KPIs** that investors should track.
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For each KPI:
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1. **KPI Name** — what it measures and why it matters for this specific company
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2. **Current Context** — what investors should know about this metric's recent trajectory
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3. **Industry Benchmark** — how to interpret good vs bad values
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Format as a clean markdown list. Focus on KPIs that are:
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- Specific to this company's business model (not generic financial ratios)
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- Forward-looking indicators of growth or risk
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- Examples: For NVIDIA → CUDA developer adoption, data center revenue mix, AI training chip market share
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- Examples: For Starbucks → same-store sales growth, store count, average ticket size
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Also include a brief section: "**Key Risks to Watch**" with 2-3 forward-looking risk indicators.
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Keep under 500 words. Be specific and actionable."""
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r = model.generate_content(
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prompt,
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generation_config={"temperature": 0.3, "max_output_tokens": 2048},
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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[kpi_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. Please 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(kpi_key):
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st.markdown(st.session_state[kpi_key])
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