Add Jared read-only token OHLCV ingestion and manual contract lookup

app.py

@@ -52,17 +52,18 @@ def update_horizon_label(timeframe):
     return gr.update(label=f"Horizon ({units.get(timeframe, 'days')})")
 
 @spaces.GPU
-def run_dashboard(data_src, ticker, file_upload, timeframe, start_date, end_date, horizon, indicators, 
+def run_dashboard(data_src, ticker, manual_token, file_upload, timeframe, start_date, end_date, horizon, indicators, 
                  include_sentiment, news_api_key, alpha_api_key, account_size, risk_percent, model, 
                  hidden_units, n_layers, epochs, learning_rate, beta1, beta2, weight_decay, dropout, 
                  window_size, test_split, rsi_mid, macd_sens, adx_thr, sent_thr, vote_buy, vote_sell, 
                  feat_selector, feat_threshold):
     try:
-        logging.info(f"Running dashboard for {ticker}, timeframe: {timeframe}, model: {model}")
+        requested_ticker = (manual_token or (ticker if ticker in {"JARED-LATEST", "CON"} else "JARED-LATEST")).strip() if data_src == "jared" else ticker
+        logging.info(f"Running dashboard for {requested_ticker}, timeframe: {timeframe}, model: {model}")
         start_date = pd.to_datetime(start_date).strftime("%Y-%m-%d")
         end_date = pd.to_datetime(end_date).strftime("%Y-%m-%d")
         
-        df = load_data(data_src=data_src, ticker=ticker, start=start_date, end=end_date, 
+        df = load_data(data_src=data_src, ticker=requested_ticker, start=start_date, end=end_date, 
                        interval=timeframe, file_upload=file_upload, alpha_api_key=alpha_api_key)
         if df.empty:
             logging.error("Failed to load data")
@@ -103,9 +104,10 @@ def run_dashboard(data_src, ticker, file_upload, timeframe, start_date, end_date
             logging.error("Failed to generate signals")
             return [None] * 12 + ["Failed to generate signals", None, None, None, None, "Failed to generate signals"]
 
-        chart_plot = plot_indicators(df, ticker)
-        signals_plot = plot_signals(signals_df, ticker)
-        backtest_plot = plot_backtest(equity_df, trades_df, ticker)
+        display_ticker = df.attrs.get("token_symbol", requested_ticker)
+        chart_plot = plot_indicators(df, display_ticker)
+        signals_plot = plot_signals(signals_df, display_ticker)
+        backtest_plot = plot_backtest(equity_df, trades_df, display_ticker)
 
 
         future_plot = plot_future_forecast(df, result, indicators)
@@ -121,17 +123,18 @@ def run_dashboard(data_src, ticker, file_upload, timeframe, start_date, end_date
         loss_plot = plot_loss_curve(result)
         architecture_plot = plot_model_architecture(result)
 
-        signals_csv = f"signals_{ticker}.csv"
+        safe_name = "".join(ch if ch.isalnum() or ch in "-_" else "_" for ch in str(display_ticker))
+        signals_csv = f"signals_{safe_name}.csv"
         signals_df.to_csv(signals_csv)
 
 
 
-        predictions_csv = f"predictions_{ticker}.csv"
+        predictions_csv = f"predictions_{safe_name}.csv"
         pd.DataFrame({
             "Actual": result["actual"],
             "Forecast": result["forecast"]
         }).to_csv(predictions_csv)
-        chart_png = f"chart_{ticker}.png"
+        chart_png = f"chart_{safe_name}.png"
         pio.write_image(chart_plot, chart_png, format='png')
 
         with open(log_path, 'r') as log_file:
@@ -141,7 +144,8 @@ def run_dashboard(data_src, ticker, file_upload, timeframe, start_date, end_date
         return [
             chart_plot, sentiment_text, signals_table, backtest_plot, future_plot, future_table,
             r2_plot, error_plot, precision_recall_plot, risk_plot, loss_plot, architecture_plot,
-            "Dashboard generated successfully", chart_png, signals_csv, predictions_csv, signals_plot, log_output
+            f"Dashboard generated successfully | source={df.attrs.get('source', data_src)} | token={df.attrs.get('token_address', display_ticker)}",
+            chart_png, signals_csv, predictions_csv, signals_plot, log_output
         ]
     except Exception as e:
         logging.error(f"Dashboard error: {str(e)}")
@@ -153,8 +157,9 @@ def main_interface():
             gr.Markdown("# Market Prediction Pro")
             with gr.Row():
                 with gr.Column(scale=1):
-                    data_src = gr.Dropdown(["yahoo", "csv"], label="Data Source", value="yahoo")
-                    ticker = gr.Dropdown(DEFAULT_TICKERS, label="Ticker", value="AAPL")
+                    data_src = gr.Dropdown(["yahoo", "jared", "csv"], label="Data Source", value="yahoo")
+                    ticker = gr.Dropdown(DEFAULT_TICKERS, label="Ticker", value="AAPL", allow_custom_value=True)
+                    manual_token = gr.Textbox(label="Manual token / contract address (Jared mode)", placeholder="CON or 0x…", visible=False)
                     file_upload = gr.File(label="Upload CSV", visible=False)
                     timeframe = gr.Dropdown(AVAILABLE_TIMEFRAMES, label="Timeframe", value="1d")
                     start_date = gr.Textbox("2020-01-01", label="Start Date (YYYY-MM-DD)")
@@ -215,13 +220,13 @@ def main_interface():
                     export_predictions = gr.File(label="Export Predictions (CSV)")
                     log_output = gr.Textbox(label="Debug Logs", lines=10)
 
-            data_src.change(fn=lambda src: gr.update(visible=(src == "csv")), inputs=[data_src], outputs=[file_upload])
+            data_src.change(fn=lambda src: (gr.update(visible=(src == "csv")), gr.update(visible=(src == "jared"))), inputs=[data_src], outputs=[file_upload, manual_token])
             include_sentiment.change(fn=lambda sent: gr.update(visible=sent), inputs=[include_sentiment], outputs=[news_api_key])
             timeframe.change(fn=update_horizon_label, inputs=[timeframe], outputs=[horizon])
 
             run_btn.click(
                 fn=run_dashboard,
-                inputs=[data_src, ticker, file_upload, timeframe, start_date, end_date, horizon, indicators, 
+                inputs=[data_src, ticker, manual_token, file_upload, timeframe, start_date, end_date, horizon, indicators, 
                         include_sentiment, news_api_key, alpha_api_key, account_size, risk_percent, model, 
                         hidden_units, n_layers, epochs, learning_rate, beta1, beta2, weight_decay, dropout, 
                         window_size, test_split, rsi_mid, macd_sens, adx_thr, sent_thr, vote_buy, vote_sell, 
@@ -237,5 +242,3 @@ def main_interface():
 
 if __name__ == "__main__":
     main_interface().launch(server_name="0.0.0.0", server_port=7860, share=False)
-
-