Spaces:
Sleeping
Sleeping
| print("Loading commodities.ui ...") | |
| import logging | |
| from datetime import datetime | |
| import plotly.express as px | |
| import plotly.graph_objects as go | |
| import streamlit as st | |
| from commodities.logic import build_commodities_frame | |
| from utils.db import log_audit, register_scan, save_scan_results | |
| logger = logging.getLogger(__name__) | |
| # ───────────────────────────────────────────── | |
| # Colour mapping for conviction labels | |
| # ───────────────────────────────────────────── | |
| LABEL_COLOR = { | |
| "STRONG BUY": "#00c853", | |
| "BUY": "#64dd17", | |
| "HOLD": "#ffd600", | |
| "UNDERPERFORMER":"#ff6d00", | |
| "AVOID": "#d50000", | |
| } | |
| DISPLAY_COLS = [ | |
| "Conviction Emoji", "Commodity", "Conviction Score", "Conviction Label", | |
| "Price (₹)", "Trend", "ATR Regime", "1M Return %", "3M Return %", | |
| "Spread %", "USDINR", | |
| ] | |
| def _scorecard(row: dict): | |
| """Render a single commodity conviction card.""" | |
| label = row.get("Conviction Label", "HOLD") | |
| emoji = row.get("Conviction Emoji", "🟡") | |
| score = row.get("Conviction Score", 50) | |
| color = LABEL_COLOR.get(label, "#ffd600") | |
| decision = row.get("Decision", "") | |
| st.markdown( | |
| f""" | |
| <div style=" | |
| background: linear-gradient(135deg, #1a1a2e 0%, #16213e 100%); | |
| border-left: 5px solid {color}; | |
| border-radius: 12px; | |
| padding: 16px 20px; | |
| margin-bottom: 10px; | |
| "> | |
| <div style="display:flex; justify-content:space-between; align-items:center;"> | |
| <h3 style="margin:0; color:#fff; font-size:1.15rem;"> | |
| {emoji} {row.get('Commodity','—')} | |
| </h3> | |
| <span style=" | |
| background:{color}; color:#000; font-weight:700; | |
| padding:4px 14px; border-radius:20px; font-size:0.9rem; | |
| "> | |
| {label} • {score}/100 | |
| </span> | |
| </div> | |
| <hr style="border-color:#333; margin: 8px 0;"> | |
| <p style="margin:2px 0; color:#ccc; font-size:0.88rem;">{decision}</p> | |
| <div style="margin-top:10px; display:flex; gap:24px; flex-wrap:wrap;"> | |
| <div><span style="color:#8f8f8f">Price</span><br> | |
| <b style="color:#fff">₹ {row.get("Price (₹)", 0):,.2f}</b></div> | |
| <div><span style="color:#8f8f8f">Trend</span><br> | |
| <b style="color:#fff">{row.get("Trend","—")}</b></div> | |
| <div><span style="color:#8f8f8f">1M Return</span><br> | |
| <b style="color:#fff">{row.get("1M Return %",0):+.2f}%</b></div> | |
| <div><span style="color:#8f8f8f">ATR Regime</span><br> | |
| <b style="color:#fff">{row.get("ATR Regime","—")}</b></div> | |
| <div><span style="color:#8f8f8f">Spread</span><br> | |
| <b style="color:#fff">{row.get("Spread %",0):+.2f}%</b></div> | |
| <div><span style="color:#8f8f8f">USDINR</span><br> | |
| <b style="color:#fff">{row.get("USDINR",84.0):.2f}</b></div> | |
| </div> | |
| </div> | |
| """, | |
| unsafe_allow_html=True, | |
| ) | |
| def render(broker_choice="Zerodha"): | |
| st.header("🌍 Commodities Intelligence Terminal") | |
| commodity = st.sidebar.selectbox("Commodity", ["All", "Gold", "Silver", "Crude", "Copper"]) | |
| debug_mode = st.sidebar.toggle("Commodities Debug Mode", value=False) | |
| # ── Load data ──────────────────────────────────────────────────────── | |
| try: | |
| with st.spinner("Loading commodities data…"): | |
| df = build_commodities_frame(None if commodity == "All" else commodity) | |
| except Exception as e: | |
| logger.error(f"commodities error: {e}") | |
| st.warning("Data load failed — retry or check logs") | |
| return | |
| if df.empty: | |
| st.warning("No commodity data available right now.") | |
| return | |
| # ── Conviction summary bar ──────────────────────────────────────────── | |
| st.subheader("📊 Conviction Summary") | |
| if "Conviction Score" in df.columns: | |
| summary_cols = st.columns(len(df)) | |
| for i, (_, row) in enumerate(df.iterrows()): | |
| with summary_cols[i]: | |
| score = row.get("Conviction Score", 50) | |
| label = row.get("Conviction Label", "HOLD") | |
| emoji = row.get("Conviction Emoji", "🟡") | |
| color = LABEL_COLOR.get(label, "#ffd600") | |
| st.markdown( | |
| f"""<div style="text-align:center; background:#1a1a2e; | |
| border-top: 4px solid {color}; border-radius:10px; padding:12px;"> | |
| <div style="font-size:1.3rem">{emoji}</div> | |
| <div style="font-weight:700; color:#fff; font-size:0.95rem"> | |
| {row.get('Commodity','—')}</div> | |
| <div style="font-size:1.6rem; font-weight:900; color:{color}">{score}</div> | |
| <div style="color:#aaa; font-size:0.8rem">{label}</div> | |
| </div>""", | |
| unsafe_allow_html=True, | |
| ) | |
| st.markdown("---") | |
| # ── Conviction cards ───────────────────────────────────────────────── | |
| st.subheader("🎯 Decision Cards") | |
| for _, row in df.iterrows(): | |
| _scorecard(row.to_dict()) | |
| st.markdown("---") | |
| # ── Data table ─────────────────────────────────────────────────────── | |
| with st.expander("📋 Full Data Table", expanded=False): | |
| show_cols = [c for c in DISPLAY_COLS if c in df.columns] | |
| st.dataframe(df[show_cols], use_container_width=True, hide_index=True) | |
| # ── Heatmap ──────────────────────────────────────────────────────── | |
| with st.expander("🔥 Conviction & Spread Heatmap", expanded=False): | |
| heat_data = df.set_index("Commodity")[["Conviction Score", "Spread %", "1M Return %", "3M Return %"]] | |
| heat = px.imshow( | |
| heat_data.T, | |
| aspect="auto", | |
| color_continuous_scale="RdYlGn", | |
| title="Commodity Intelligence Heatmap", | |
| labels={"color": "Value"}, | |
| ) | |
| heat.update_xaxes(tickvals=list(range(len(df))), ticktext=df["Commodity"].tolist()) | |
| st.plotly_chart(heat, use_container_width=True) | |
| # ── Price Momentum Chart ────────────────────────────────────────────── | |
| with st.expander("📈 Return Comparison", expanded=False): | |
| ret_cols = [c for c in ["1M Return %", "3M Return %", "6M Return %"] if c in df.columns] | |
| if ret_cols: | |
| fig = go.Figure() | |
| for col in ret_cols: | |
| fig.add_trace(go.Bar(name=col, x=df["Commodity"], y=df[col])) | |
| fig.update_layout( | |
| barmode="group", | |
| title="Return Comparison across Time Horizons", | |
| template="plotly_dark", | |
| legend=dict(orientation="h", y=1.1), | |
| ) | |
| st.plotly_chart(fig, use_container_width=True) | |
| # ── Export ──────────────────────────────────────────────────────────── | |
| st.download_button( | |
| "⬇️ Export Commodities CSV", | |
| df.to_csv(index=False).encode("utf-8"), | |
| f"commodities_{datetime.now().strftime('%Y%m%d')}.csv", | |
| "text/csv", | |
| ) | |
| # ── Save to DB ──────────────────────────────────────────────────────── | |
| try: | |
| scan_id = register_scan( | |
| datetime.now().strftime("%Y-%m-%d %H:%M:%S"), | |
| universe="Commodities", scan_type="COMMODITY", status="Completed", | |
| ) | |
| save_scan_results(scan_id, df) | |
| log_audit("Commodity scan", "Commodities", f"Rows logged: {len(df)}") | |
| except Exception as e: | |
| logger.error(f"commodities DB error: {e}") | |
| if debug_mode: | |
| st.markdown("### 🔍 Debug — Raw DataFrame") | |
| st.dataframe(df, use_container_width=True) | |