Update scripts/app.py

scripts/app.py

@@ -533,130 +533,70 @@ custom_css = """
 .disclaimer-box { background-color: #374151; padding: 15px; border-radius: 8px; border-left: 4px solid #f59e0b; color: #d1d5db; font-size: 0.9em; margin-bottom: 20px; }
 """
 
-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:
+with gr.Blocks(css=custom_css, title="Deep RL Portfolio Manager") as demo:
+    # JS to force dark mode
     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():
-        # ================= TAB 1: DASHBOARD (RESTORED) =================
         with gr.TabItem("📊 Live Dashboard"):
-            # Metrics Row
+            nw_val, dc_val = get_dashboard_metrics()
             with gr.Row():
-                # 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
+                gr.HTML(f"<div class='metric-box'><div class='metric-label'>Current Net Worth</div><div class='metric-value'>{nw_val}</div></div>")
+                gr.HTML(f"<div class='metric-box'><div class='metric-label'>24h Change</div><div class='metric-value' style='color: #10b981;'>{dc_val}</div></div>")
             with gr.Row():
-                with gr.Column(scale=3):
-                    history_chart = gr.Plot(value=get_portfolio_history_plot(), label="Net Worth History")
-
-            # Bottom Row: Allocations and Transactions
+                with gr.Column(scale=3): history_chart = gr.Plot(value=get_portfolio_history_plot())
             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):
+                with gr.Column(scale=1): allocation_chart = gr.Plot(value=get_current_allocation_plot())
+                with gr.Column(scale=2): 
                     gr.Markdown("### Recent Transactions")
-                    transactions_table = gr.Dataframe(value=get_recent_transactions(), interactive=False, wrap=True)
+                    transactions_table = gr.Dataframe(value=get_recent_transactions(), interactive=False)
 
-        # ================= TAB 2: FORECAST (UPDATED with XAI) =================
         with gr.TabItem("🔮 Forecast & AI Analysis"):
-            gr.Markdown("### Generate Tomorrow's Portfolio Strategy")
+            gr.Markdown("### Generate Tomorrow's 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)
+            status_output = gr.Textbox(label="System Status", 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
+                    allocation_output = gr.Dataframe(headers=["Asset", "Allocation"], interactive=False)
+                    gr.Markdown("### 🧠 XAI Feature Importance")
+                    xai_output_plot = gr.Plot(label="Influential Factors", show_label=False)
                 with gr.Column(scale=3):
-                    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 =================
+                    analysis_report_html = gr.HTML(label="AI Report")
+            run_btn.click(fn=predict_and_analyze, inputs=None, outputs=[status_output, allocation_output, xai_output_plot, analysis_report_html])
+
         with gr.TabItem("📅 Historical Data Analyst"):
-            gr.Markdown("### Analyze Past Market Performance with AI")
-            
+            gr.Markdown("### Analyze Past Market Performance")
             with gr.Row():
                 with gr.Column(scale=1):
-                    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")
+                    all_tickers = ASSETS + list(FRED_IDS.values())
+                    avail_tickers = [t for t in all_tickers if DASHBOARD_DATA_DF is not None and t in DASHBOARD_DATA_DF.columns]
+                    asset_selector = gr.Dropdown(choices=avail_tickers, value=avail_tickers[:3] if avail_tickers else [], multiselect=True, label="Select Assets")
+                    period_selector = gr.Dropdown(choices=list(TIME_PERIODS.keys()), value="1 Year", label="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]
-            )
+            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>
-            """)
-
+            gr.Markdown("### Backtest Strategy")
+            gr.HTML(f"<div class='disclaimer-box'>⚠️ Model trained on {TRAIN_START_DATE} to {TRAIN_END_DATE}. Outside usage may be inaccurate.</div>")
             with gr.Row():
                 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'))
+                    start_date_input = gr.Textbox(label="Start Date", value=(datetime.now()-timedelta(days=365)).strftime('%Y-%m-%d'))
+                    end_date_input = gr.Textbox(label="End Date", 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")
-
+                    sim_plot = gr.Plot(label="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]
-            )
+            sim_metrics_df = gr.Dataframe(interactive=False, wrap=True, label="Professional Metrics")
+            sim_btn.click(fn=run_historical_simulation, inputs=[start_date_input, end_date_input], outputs=[sim_plot, sim_status, sim_metrics_df])
 
 if __name__ == "__main__":
     demo.queue().launch(server_name="0.0.0.0", server_port=7860, debug=True, share=True)