New tabs and functions v1.5

app.py

@@ -5,6 +5,7 @@ from core.comparer import PortfolioComparer
 from core.chat import ChatAssistant
 from core.metrics import show_metrics_table
 from core.visualization import build_alpha_chart
+from core.crypto_dashboard import build_crypto_dashboard  # 🆕 новая вкладка
 
 # === Model setup ===
 MODEL_NAME = "meta-llama/Meta-Llama-3.1-8B-Instruct"
@@ -13,7 +14,7 @@ analyzer = PortfolioAnalyzer(llm_service, MODEL_NAME)
 comparer = PortfolioComparer(llm_service, MODEL_NAME)
 chatbot = ChatAssistant(llm_service, MODEL_NAME)
 
-# === Bloomberg-style dark theme ===
+# === Theme ===
 dark_theme = gr.themes.Base(
     primary_hue="violet",
     secondary_hue="gray",
@@ -33,251 +34,94 @@ dark_theme = gr.themes.Base(
 )
 
 with gr.Blocks(theme=dark_theme, css="""
-/* === Layout width === */
 #root, [data-testid="block-container"] {
-    max-width: 1050px !important;     /* unified width -50px */
+    max-width: 1050px !important;
     margin: auto !important;
 }
-[data-testid="tabitem"] {
-    width: 100% !important;
-}
-.gradio-container {
-    font-family: 'Inter', sans-serif;
-    background-color: #0d1117 !important;
-}
-
-/* === LLM Commentary & Analysis Output (same dark style) === */
-#llm_comment_box textarea,
-#analysis_output textarea {
-    height: auto !important;
-    min-height: 520px !important;
-    overflow-y: auto !important;
-    background-color: #161b22 !important;
-    color: #f0f6fc !important;
-    border: 1px solid #30363d !important;
-    border-radius: 6px !important;
-    font-family: 'JetBrains Mono', monospace !important;
-    font-size: 14px !important;
-    line-height: 1.5 !important;
-    padding: 12px !important;
-}
-
-/* === Comparison Table === */
-#comparison_table {
-    height: 620px !important;
-    overflow-y: auto !important;
-    margin-top: 10px !important;
-}
-#comparison_table table {
-    width: 100% !important;
-}
-
-/* === Universal Loader Style === */
-.loading-spinner {
-    border: 3px solid rgba(255, 255, 255, 0.1);
-    border-top: 3px solid #6366f1;
-    border-radius: 50%;
-    width: 22px;
-    height: 22px;
-    animation: spin 0.8s linear infinite;
-    margin: 12px auto;
-}
-@keyframes spin {
-    0% { transform: rotate(0deg); }
-    100% { transform: rotate(360deg); }
-}
-
-/* Typography & Buttons */
-h2, h3, .gr-markdown {
-    font-weight: 600;
-    color: #f0f6fc !important;
-}
-.gr-button {
-    border-radius: 6px !important;
-    font-weight: 600 !important;
-    letter-spacing: 0.3px;
-    box-shadow: 0 2px 4px rgba(0,0,0,0.25);
-}
-.gr-textbox, .gr-dataframe {
-    border-radius: 6px !important;
-}
-.gr-button.primary {
-    background: linear-gradient(90deg, #6366f1, #4f46e5);
-    border: none !important;
-}
-.gr-tab {
-    background-color: #161b22 !important;
-    color: #c9d1d9 !important;
-}
-.gr-tabs {
-    border-bottom: 1px solid #30363d !important;
-}
-.gr-plot {
-    background: #0d1117 !important;
-}
-
-/* Fix Gradio plot overlay */
-#alpha_chart svg {
-    display: none !important;
-}
-#alpha_chart .wrap {
-    background: transparent !important;
-    box-shadow: none !important;
-}
-
-/* === Styled comparison & metrics tables === */
-.gr-dataframe table {
-    border-collapse: collapse !important;
-    width: 100% !important;
-    color: #c9d1d9 !important;
-    background-color: #161b22 !important;
-}
-.gr-dataframe th {
-    background-color: #21262d !important;
-    color: #f0f6fc !important;
-    font-weight: 600 !important;
-    text-transform: uppercase;
-    border-bottom: 1px solid #30363d !important;
-}
-.gr-dataframe td {
-    border-top: 1px solid #30363d !important;
-    padding: 8px !important;
+.gradio-container { font-family: 'Inter', sans-serif; background-color:#0d1117!important;}
+#llm_comment_box textarea,#analysis_output textarea{
+    min-height:520px!important;background-color:#161b22!important;color:#f0f6fc!important;
+    border:1px solid #30363d!important;border-radius:6px!important;font-family:'JetBrains Mono',monospace!important;
 }
+.gr-dataframe table{width:100%!important;color:#c9d1d9!important;background:#161b22!important;}
+.gr-dataframe th{background:#21262d!important;color:#f0f6fc!important;border-bottom:1px solid #30363d!important;}
+.gr-dataframe td{border-top:1px solid #30363d!important;padding:8px!important;}
 """) as demo:
 
-    # === Header ===
     gr.Markdown("## Investment Portfolio Analyzer")
     gr.Markdown(
-        "A professional dashboard for analyzing and comparing investment portfolios using AI insights.",
+        "Professional AI-driven analytics for investment and crypto markets.",
         elem_classes="subtitle",
     )
 
     with gr.Tabs():
-        # --- Analysis Tab ---
+        # --- Analysis ---
         with gr.TabItem("Analysis"):
-            portfolio_input = gr.Textbox(
-                label="Portfolio ID or Link",
-                placeholder="Enter a portfolio ID (e.g. ea856c1b-...)",
-                lines=1,
-                value="b1ef37aa-5b9a-41b4-9394-8823f2de36bb",
-            )
-            analyze_button = gr.Button("Run Analysis", variant="primary")
-            analyze_output = gr.Textbox(
-                label="Analysis Result",
-                lines=15,
-                elem_id="analysis_output",
-            )
-            analyze_button.click(
-                fn=analyzer.run,
-                inputs=portfolio_input,
-                outputs=analyze_output,
-                show_progress="minimal",  # ensures loader display
-            )
-
-        # --- Comparison Table Tab ---
+            portfolio_input = gr.Textbox(label="Portfolio ID or Link",
+                                         placeholder="Enter portfolio ID (uuid)",
+                                         value="b1ef37aa-5b9a-41b4-9394-8823f2de36bb")
+            analyze_btn = gr.Button("Run Analysis", variant="primary")
+            analyze_out = gr.Textbox(label="Analysis Result", lines=15, elem_id="analysis_output")
+            analyze_btn.click(fn=analyzer.run, inputs=portfolio_input, outputs=analyze_out)
+
+        # --- Comparison ---
         with gr.TabItem("Comparison Table"):
-            comp_input_1 = gr.Textbox(label="Portfolio A", value="3852a354-e66e-4bc5-97e9-55124e31e687")
-            comp_input_2 = gr.Textbox(label="Portfolio B", value="b1ef37aa-5b9a-41b4-9394-8823f2de36bb")
-            comp_button = gr.Button("Load Comparison", variant="primary")
-
-            comp_output_comment = gr.Textbox(
-                label="AI Commentary",
-                lines=14,
-                interactive=False,
-                show_copy_button=True,
-                elem_id="llm_comment_box",
-            )
-
-            comp_output_table = gr.Dataframe(
-                label="Comparative Metrics",
-                wrap=True,
-                elem_id="comparison_table",
-            )
-
             from core.comparison_table import show_comparison_table
-            comp_button.click(
-                fn=show_comparison_table,
-                inputs=[comp_input_1, comp_input_2],
-                outputs=[comp_output_table, comp_output_comment],
-                show_progress="minimal",
-            )
-
-        # --- Chat Assistant Tab ---
+            pid_a = gr.Textbox(label="Portfolio A", value="3852a354-e66e-4bc5-97e9-55124e31e687")
+            pid_b = gr.Textbox(label="Portfolio B", value="b1ef37aa-5b9a-41b4-9394-8823f2de36bb")
+            compare_btn = gr.Button("Load Comparison", variant="primary")
+            comp_table = gr.Dataframe(label="Comparative Metrics", wrap=True)
+            comp_comment = gr.Textbox(label="AI Commentary", lines=14, elem_id="llm_comment_box")
+            compare_btn.click(fn=show_comparison_table,
+                              inputs=[pid_a, pid_b],
+                              outputs=[comp_table, comp_comment])
+
+        # --- Assistant ---
         with gr.TabItem("Assistant"):
-            chat_input = gr.Textbox(label="Ask about investments or analysis!")
-            chat_button = gr.Button("Send Question", variant="primary")
-            chat_output = gr.Textbox(label="AI Response", lines=8)
-            chat_button.click(
-                fn=chatbot.run,
-                inputs=chat_input,
-                outputs=chat_output,
-                show_progress="minimal",
-            )
+            chat_in = gr.Textbox(label="Ask about investments or analysis")
+            chat_btn = gr.Button("Send Question", variant="primary")
+            chat_out = gr.Textbox(label="AI Response", lines=8)
+            chat_btn.click(fn=chatbot.run, inputs=chat_in, outputs=chat_out)
 
-        # --- Metrics Table Tab ---
+        # --- Metrics ---
         with gr.TabItem("Metrics Table"):
-            metrics_input = gr.Textbox(label="Portfolio ID", value="b1ef37aa-5b9a-41b4-9394-8823f2de36bb")
-            metrics_button = gr.Button("Load Metrics", variant="primary")
-            metrics_output = gr.Dataframe(label="Portfolio Metrics", wrap=True)
-            metrics_button.click(
-                fn=show_metrics_table,
-                inputs=metrics_input,
-                outputs=metrics_output,
-                show_progress="minimal",
-            )
+            metrics_in = gr.Textbox(label="Portfolio ID", value="b1ef37aa-5b9a-41b4-9394-8823f2de36bb")
+            metrics_btn = gr.Button("Load Metrics", variant="primary")
+            metrics_out = gr.Dataframe(label="Portfolio Metrics", wrap=True)
+            metrics_btn.click(fn=show_metrics_table, inputs=metrics_in, outputs=metrics_out)
 
-        # --- AlphaBTC Chart Tab ---
+        # --- AlphaBTC Chart ---
         with gr.TabItem("AlphaBTC Chart"):
-            chart_input = gr.Textbox(label="Portfolio ID", value="3852a354-e66e-4bc5-97e9-55124e31e687")
-            chart_button = gr.Button("Generate Chart", variant="primary")
-            chart_output = gr.Plot(
-                label="Alpha vs BTC",
-                show_label=False,
-                elem_id="alpha_chart",
-            )
-            chart_button.click(
-                fn=build_alpha_chart,
-                inputs=chart_input,
-                outputs=chart_output,
-                show_progress="minimal",
-            )
-        # --- Crypto DAX Dashboard ---
-        with gr.TabItem("Crypto DAX Dashboard"):
-            gr.Markdown("### 📊 Live Market Dashboard (CoinGecko + Portfolio Metrics)")
+            chart_in = gr.Textbox(label="Portfolio ID", value="3852a354-e66e-4bc5-97e9-55124e31e687")
+            chart_btn = gr.Button("Generate Chart", variant="primary")
+            chart_out = gr.Plot(label="Alpha vs BTC")
+            chart_btn.click(fn=build_alpha_chart, inputs=chart_in, outputs=chart_out)
+
+        # --- 🧠 Crypto Intelligence Dashboard (CoinGecko) ---
+        with gr.TabItem("Crypto Intelligence Dashboard"):
+            gr.Markdown("### 📈 Real-Time Crypto Analytics (CoinGecko API + AI Insights)")
 
             with gr.Row():
                 coin_input = gr.Dropdown(
                     label="Select Cryptocurrency",
-                    choices=["bitcoin", "ethereum", "solana", "dogecoin", "cardano"],
-                    value="bitcoin",
-                )
-                portfolio_input_dax = gr.Textbox(
-                    label="Portfolio ID",
-                    value="b1ef37aa-5b9a-41b4-9394-8823f2de36bb",
-                    placeholder="Your portfolio UUID"
+                    choices=["bitcoin", "ethereum", "solana", "bnb", "dogecoin"],
+                    value="bitcoin"
                 )
                 days_slider = gr.Slider(
-                    label="Period (days)",
-                    minimum=7,
-                    maximum=180,
-                    step=1,
-                    value=30,
+                    label="Days Range", minimum=7, maximum=180, step=1, value=90
                 )
+                load_btn = gr.Button("Load Market Data", variant="primary")
 
-            load_button = gr.Button("Generate Dashboard", variant="primary")
             kpi_plot = gr.Plot(label="KPI Metrics")
-            price_plot = gr.Plot(label="Price Trend")
-            vol_plot = gr.Plot(label="Volatility")
-            ai_comment = gr.Textbox(label="AI Market Commentary", lines=8, elem_id="llm_comment_box")
-
-            from core.crypto_dashboard import build_dashboard
-
-            load_button.click(
-                fn=build_dashboard,
-                inputs=[coin_input, portfolio_input_dax, days_slider],
-                outputs=[kpi_plot, price_plot, vol_plot, ai_comment],
-                show_progress="minimal",
-            )
+            price_plot = gr.Plot(label="Price Chart")
+            rsi_plot = gr.Plot(label="RSI Indicator")
+            corr_plot = gr.Plot(label="Correlation Heatmap")
+            ai_summary = gr.Textbox(label="AI Market Summary", lines=8, elem_id="llm_comment_box")
+
+            load_btn.click(fn=build_crypto_dashboard,
+                           inputs=[coin_input, days_slider],
+                           outputs=[kpi_plot, price_plot, rsi_plot, corr_plot, ai_summary],
+                           show_progress="minimal")
 
     gr.Markdown("---")
     gr.Markdown(

core/crypto_dashboard.py

@@ -1,29 +1,22 @@
 """
 🇬🇧 Module: crypto_dashboard.py
-Purpose: Build Power BI–style dashboard for crypto market using CoinGecko API + portfolio metrics.
+Purpose: Power BI–style dashboard for crypto analytics using CoinGecko API.
 🇷🇺 Модуль: crypto_dashboard.py
-Назначение: Создание Power BI-подобного дашборда на основе CoinGecko API и метрик портфеля.
+Назначение: интерактивный дашборд уровня Power BI для анализа крипторынка.
 """
 
 import requests
 import pandas as pd
-import plotly.graph_objects as go
+import numpy as np
 import plotly.express as px
-from datetime import datetime
-from services.output_api import fetch_metrics_async
+import plotly.graph_objects as go
 from services.llm_client import llm_service
-import asyncio
 
 COINGECKO_API = "https://api.coingecko.com/api/v3"
 
 
-async def _get_portfolio_metrics(portfolio_id):
-    metrics = await fetch_metrics_async(portfolio_id)
-    return metrics or {}
-
-
-def get_market_data(coin_id: str = "bitcoin", days: int = 30):
-    """Fetch market data (price, volume, market cap) from CoinGecko."""
+# === Data Fetching ===
+def get_market_data(coin_id: str, days: int = 90):
     url = f"{COINGECKO_API}/coins/{coin_id}/market_chart?vs_currency=usd&days={days}"
     r = requests.get(url)
     data = r.json()
@@ -31,76 +24,101 @@ def get_market_data(coin_id: str = "bitcoin", days: int = 30):
     df["timestamp"] = pd.to_datetime(df["timestamp"], unit="ms")
     df["returns"] = df["price"].pct_change()
     df["volatility"] = df["returns"].rolling(7).std() * 100
+    df["rsi"] = _calc_rsi(df["price"])
     return df
 
 
-def build_dashboard(coin_id: str, portfolio_id: str, days: int = 30):
-    """Main sync wrapper: returns 3 Plotly figures + LLM commentary."""
-    df = get_market_data(coin_id, days)
-    metrics = asyncio.run(_get_portfolio_metrics(portfolio_id))
+def _calc_rsi(prices, period: int = 14):
+    """RSI indicator like in trading terminals."""
+    delta = prices.diff()
+    gain = np.where(delta > 0, delta, 0)
+    loss = np.where(delta < 0, -delta, 0)
+    avg_gain = pd.Series(gain).rolling(period).mean()
+    avg_loss = pd.Series(loss).rolling(period).mean()
+    rs = avg_gain / avg_loss
+    rsi = 100 - (100 / (1 + rs))
+    return rsi
+
 
+# === KPI and Figures ===
+def build_crypto_dashboard(coin_id: str, days: int = 90):
+    df = get_market_data(coin_id, days)
     if df.empty:
-        return None, None, None, "❌ No market data."
+        return None, None, None, None, "❌ No market data."
 
-    # === KPI Cards ===
+    # === KPI ===
     growth = (df["price"].iloc[-1] / df["price"].iloc[0] - 1) * 100
     vol = df["volatility"].mean()
-    sharpe = metrics.get("sharpe", 0)
+    latest_rsi = df["rsi"].iloc[-1]
 
-    kpi = go.Figure()
-    kpi.add_trace(go.Indicator(
-        mode="number+delta",
+    kpi_fig = go.Figure()
+    kpi_fig.add_trace(go.Indicator(mode="number+delta",
         value=round(growth, 2),
-        delta={"reference": 0, "position": "right"},
+        delta={"reference": 0},
         title={"text": f"{coin_id.capitalize()} Growth %"},
-        domain={'row': 0, 'column': 0}
-    ))
-    kpi.add_trace(go.Indicator(
-        mode="number",
+        domain={'row': 0, 'column': 0}))
+    kpi_fig.add_trace(go.Indicator(mode="number",
         value=round(vol, 2),
         title={"text": "Volatility %"},
-        domain={'row': 0, 'column': 1}
-    ))
-    kpi.add_trace(go.Indicator(
-        mode="number",
-        value=round(sharpe, 2),
-        title={"text": "Sharpe (Portfolio)"},
-        domain={'row': 0, 'column': 2}
-    ))
-    kpi.update_layout(grid={'rows': 1, 'columns': 3}, template="plotly_dark", height=200)
-
-    # === Price timeline ===
+        domain={'row': 0, 'column': 1}))
+    kpi_fig.add_trace(go.Indicator(mode="number",
+        value=round(latest_rsi, 2),
+        title={"text": "RSI"},
+        domain={'row': 0, 'column': 2}))
+    kpi_fig.update_layout(grid={'rows': 1, 'columns': 3}, template="plotly_dark", height=180)
+
+    # === Price Chart ===
     price_fig = px.line(df, x="timestamp", y="price", title=f"{coin_id.capitalize()} Price (USD)")
     price_fig.update_layout(template="plotly_dark", height=400)
 
-    # === Volatility chart ===
-    vol_fig = px.area(df, x="timestamp", y="volatility", title="7-Day Rolling Volatility")
-    vol_fig.update_traces(line_color="#4f46e5")
-    vol_fig.update_layout(template="plotly_dark", height=300)
+    # === RSI Chart ===
+    rsi_fig = px.line(df, x="timestamp", y="rsi", title="RSI (Momentum Oscillator)")
+    rsi_fig.update_layout(template="plotly_dark", height=300)
 
-    # === Generate AI summary ===
-    commentary = _generate_ai_comment(df, coin_id, metrics)
-    return kpi, price_fig, vol_fig, commentary
+    # === Heatmap of Correlations ===
+    corr_fig = _build_correlation_heatmap()
 
+    # === LLM commentary ===
+    commentary = _generate_market_summary(df, coin_id, growth, vol, latest_rsi)
+
+    return kpi_fig, price_fig, rsi_fig, corr_fig, commentary
 
-def _generate_ai_comment(df, coin_id, metrics):
-    """Generate an AI summary (LLM commentary)."""
-    change = (df["price"].iloc[-1] / df["price"].iloc[0] - 1) * 100
-    avg_vol = df["volatility"].mean()
-    prompt = f"""
-Act as a financial analyst.
-Summarize {coin_id.capitalize()} market performance over the last period.
 
-- Growth: {change:.2f}%
-- Average Volatility: {avg_vol:.2f}%
-- Portfolio Sharpe: {metrics.get('sharpe', 0):.2f}
+def _build_correlation_heatmap():
+    """Build correlation heatmap for top coins."""
+    coins = ["bitcoin", "ethereum", "solana", "bnb", "dogecoin"]
+    frames = {}
+    for c in coins:
+        try:
+            df = get_market_data(c, 60)
+            frames[c] = df["price"].pct_change().dropna().reset_index(drop=True)
+        except Exception:
+            continue
+    merged = pd.DataFrame(frames)
+    corr = merged.corr()
+    fig = px.imshow(corr, text_auto=True, title="Correlation Heatmap (Top 5 Coins)")
+    fig.update_layout(template="plotly_dark", height=400)
+    return fig
 
-Provide 3–5 concise sentences: performance, stability, and potential outlook.
+
+def _generate_market_summary(df, coin, growth, vol, rsi):
+    """LLM generates human-like market insight."""
+    prompt = f"""
+Summarize the {coin.capitalize()} market:
+- Growth: {growth:.2f}%
+- Avg Volatility: {vol:.2f}%
+- RSI: {rsi:.2f}
+
+Provide 4-5 concise sentences describing:
+• Market trend (bullish / bearish)
+• Risk and volatility
+• Momentum signal (based on RSI)
+• Outlook for the short term
 """
-    commentary = ""
+    summary = ""
     for delta in llm_service.stream_chat(
         messages=[{"role": "user", "content": prompt}],
         model="meta-llama/Meta-Llama-3.1-8B-Instruct"
     ):
-        commentary += delta
-    return commentary
+        summary += delta
+    return summary