Update scripts/app.py

scripts/app.py

@@ -528,59 +528,135 @@ def get_recent_transactions():
 
 custom_css = """
 .metric-box { background-color: #1f2937; padding: 20px; border-radius: 12px; border: 1px solid #374151; text-align: center; }
-.metric-label { font-size: 1.1em; color: #9ca3af; }
+.metric-label { font-size: 1.1em; color: #9ca3af; margin-bottom: 5px; }
 .metric-value { font-size: 2.2em; font-weight: 700; color: #e5e7eb; }
+.disclaimer-box { background-color: #374151; padding: 15px; border-radius: 8px; border-left: 4px solid #f59e0b; color: #d1d5db; font-size: 0.9em; margin-bottom: 20px; }
 """
 
-with gr.Blocks(css=custom_css, title="Deep RL Manager") as demo:
+theme = gr.themes.Soft(primary_hue="emerald", secondary_hue="slate", neutral_hue="zinc").set(
+    body_background_fill="#111827", block_background_fill="#1f2937", block_border_width="1px", block_border_color="#374151"
+)
+
+with gr.Blocks(theme=theme, css=custom_css, title="Deep RL Portfolio Manager") as demo:
     gr.HTML("""<script>function forceDark(){document.body.classList.add('dark');} forceDark(); setTimeout(forceDark, 500);</script>""")
+    
     gr.Markdown("# 🧠 Deep RL & LLM Portfolio Manager")
     
     with gr.Tabs():
-        with gr.TabItem("📊 Dashboard"):
-            nw, chg = get_dash_metrics()
-            with gr.Row():
-                gr.HTML(f"<div class='metric-box'><div class='metric-label'>Simulated Net Worth</div><div class='metric-value'>{nw}</div></div>")
-                gr.HTML(f"<div class='metric-box'><div class='metric-label'>24h Change</div><div class='metric-value'>{chg}</div></div>")
+        # ================= TAB 1: DASHBOARD (RESTORED) =================
+        with gr.TabItem("📊 Live Dashboard"):
+            # Metrics Row
             with gr.Row():
-                with gr.Column(scale=3): gr.Plot(value=get_hist_plot(), label="History")
+                # MOVED THIS LINE INSIDE THE TAB
+                nw_val, dc_val = get_dashboard_metrics()
+                with gr.Column(elem_classes=["metric-box"]):
+                    gr.HTML(f"<div class='metric-label'>Current Net Worth</div><div class='metric-value'>{nw_val}</div>")
+                with gr.Column(elem_classes=["metric-box"]):
+                    gr.HTML(f"<div class='metric-label'>24h Change</div><div class='metric-value' style='color: #10b981;'>{daily_change}</div>")
+            
+            # Main Chart row
             with gr.Row():
-                with gr.Column(scale=1): gr.Plot(value=get_current_allocation_plot(), label="Allocation")
-                with gr.Column(scale=2): gr.Dataframe(value=get_trans(), label="Transactions")
+                with gr.Column(scale=3):
+                    history_chart = gr.Plot(value=get_portfolio_history_plot(), label="Net Worth History")
 
-        with gr.TabItem("🔮 Forecast"):
-            run_btn = gr.Button("🚀 Run Analysis", variant="primary")
-            stat = gr.Textbox(label="Status", lines=1)
+            # Bottom Row: Allocations and Transactions
             with gr.Row():
+                with gr.Column(scale=1):
+                    allocation_chart = gr.Plot(value=get_current_allocation_plot(), label="Current Allocation")
                 with gr.Column(scale=2):
-                    alloc_out = gr.Dataframe(headers=["Asset", "Allocation"])
-                    xai_out = gr.Plot(label="XAI Factors")
+                    gr.Markdown("### Recent Transactions")
+                    transactions_table = gr.Dataframe(value=get_recent_transactions(), interactive=False, wrap=True)
+
+        # ================= TAB 2: FORECAST (UPDATED with XAI) =================
+        with gr.TabItem("🔮 Forecast & AI Analysis"):
+            gr.Markdown("### Generate Tomorrow's Portfolio Strategy")
+            run_btn = gr.Button("🚀 Run Overnight Analysis", variant="primary", size="lg")
+            status_output = gr.Textbox(label="System Status", placeholder="Ready...", interactive=False, lines=1)
+            gr.Markdown("---")
+            
+            with gr.Row():
+                # Left Column: Allocations & XAI Plot
+                with gr.Column(scale=2):
+                    gr.Markdown("### 📈 Suggested Position")
+                    allocation_output = gr.Dataframe(headers=["Asset", "Allocation"], datatype=["str", "str"], interactive=False)
+                    
+                    # NEW: XAI Feature Importance Plot
+                    gr.Markdown("### 🧠 Why did the agent choose this?")
+                    xai_output_plot = gr.Plot(label="Top Influential Factors (XAI)", show_label=False)
+
+                # Right Column: AI Analysis Report
                 with gr.Column(scale=3):
-                    ai_out = gr.HTML(label="Report")
-            run_btn.click(predict_and_analyze, None, [stat, alloc_out, xai_out, ai_out])
-
-        with gr.TabItem("📅 Analyst"):
+                    analysis_report_html = gr.HTML(label="AI Risk Analysis Report")
+            
+            # Updated click event with new XAI output
+            run_btn.click(
+                fn=predict_and_analyze,
+                inputs=None,
+                outputs=[status_output, allocation_output, xai_output_plot, analysis_report_html]
+            )
+
+        # ================= TAB 3: HISTORICAL DATA ANALYST =================
+        with gr.TabItem("📅 Historical Data Analyst"):
+            gr.Markdown("### Analyze Past Market Performance with AI")
+            
             with gr.Row():
                 with gr.Column(scale=1):
-                    ticks = ASSETS + FRED_IDS.values() if DASHBOARD_DATA_DF is None else [c for c in ASSETS + list(FRED_IDS.values()) if c in DASHBOARD_DATA_DF.columns]
-                    sel = gr.Dropdown(choices=ticks, multiselect=True, label="Assets")
-                    per = gr.Dropdown(choices=list(TIME_PERIODS.keys()), value="1 Year", label="Period")
-                    btn = gr.Button("Analyze", variant="primary")
-                with gr.Column(scale=3): plot = gr.Plot()
-            rep = gr.Markdown()
-            btn.click(run_historical_analysis, [sel, per], [plot, rep])
-
-        with gr.TabItem("🔙 Simulation"):
-            gr.Markdown("### Backtest")
+                    all_tickers_hist = ASSETS + list(FRED_IDS.values())
+                    if DASHBOARD_DATA_DF is not None:
+                        available_tickers_hist = [t for t in all_tickers_hist if t in DASHBOARD_DATA_DF.columns]
+                    else:
+                        available_tickers_hist = []
+                    default_tickers_hist = available_tickers_hist[:3] if available_tickers_hist else []
+
+                    asset_selector = gr.Dropdown(choices=available_tickers_hist, value=default_tickers_hist, multiselect=True, label="1. Select Assets")
+                    period_selector = gr.Dropdown(choices=list(TIME_PERIODS.keys()), value="1 Year", label="2. Select Period")
+                    analyze_btn = gr.Button("🔎 Run Analysis", variant="primary")
+                
+                with gr.Column(scale=3):
+                    historical_plot = gr.Plot(label="Performance Plot")
+
+            gr.Markdown("---")
+            historical_analysis_md = gr.Markdown("### 🤖 AI Analyst Report\n\n*Click 'Run Analysis' to generate.*")
+            
+            analyze_btn.click(
+                fn=run_historical_analysis,
+                inputs=[asset_selector, period_selector],
+                outputs=[historical_plot, historical_analysis_md]
+            )
+
+        # ================= TAB 4: HISTORICAL SIMULATION (UPDATED with Pro Metrics) =================
+        with gr.TabItem("🔙 Historical Simulation"):
+            gr.Markdown("### Backtest the RL Agent against Baselines")
+            
+            # Disclaimer Box
+            gr.HTML(f"""
+            <div class='disclaimer-box'>
+                <strong>⚠️ IMPORTANT DISCLAIMER:</strong> The RL model was trained on data from approximately 
+                <strong>{TRAIN_START_DATE} to {TRAIN_END_DATE}</strong>. Running simulations outside or overlapping significantly 
+                with this period may not accurately reflect real-world performance (lookahead bias or out-of-distribution data). 
+                Use for educational purposes only.
+            </div>
+            """)
+
             with gr.Row():
-                s_date = gr.Textbox(label="Start", value=(datetime.now()-timedelta(days=365)).strftime('%Y-%m-%d'))
-                e_date = gr.Textbox(label="End", value=(datetime.now()-timedelta(days=1)).strftime('%Y-%m-%d'))
-                s_btn = gr.Button("Run", variant="primary")
-                s_stat = gr.Textbox(label="Status")
-            s_plot = gr.Plot()
-            # FIX: Using Dataframe instead of Markdown for metrics table
-            s_metrics = gr.Dataframe(label="Professional Metrics")
-            s_btn.click(run_historical_simulation, [s_date, e_date], [s_plot, s_stat, s_metrics])
+                with gr.Column(scale=1):
+                    start_date_input = gr.Textbox(label="Start Date (YYYY-MM-DD)", value=(datetime.now() - timedelta(days=365)).strftime('%Y-%m-%d'))
+                    end_date_input = gr.Textbox(label="End Date (YYYY-MM-DD)", value=(datetime.now() - timedelta(days=1)).strftime('%Y-%m-%d'))
+                    sim_btn = gr.Button("▶️ Run Simulation", variant="primary")
+                    sim_status = gr.Textbox(label="Status", interactive=False, lines=1)
+                
+                with gr.Column(scale=3):
+                    sim_plot = gr.Plot(label="Simulation Performance")
+
+            gr.Markdown("---")
+            # Updated to Markdown component for better table formatting
+            sim_metrics_md = gr.Markdown("### 📊 Professional Performance Metrics\n\n*Run simulation to see metrics.*")
+
+            sim_btn.click(
+                fn=run_historical_simulation,
+                inputs=[start_date_input, end_date_input],
+                outputs=[sim_plot, sim_status, sim_metrics_md]
+            )
 
 if __name__ == "__main__":
     demo.queue().launch(server_name="0.0.0.0", server_port=7860, debug=True, share=True)