Update core/plot.py

core/plot.py

@@ -32,4 +32,60 @@ def plot_future_forecast(df, result, timeframe):
         if "latest_prediction" in result:
             last_date = df['Date'].iloc[-1]
             horizon = len(result["latest_prediction"])
-            freq_map = {'1m':'T', '2m':'2T', '5m':'5T
+            freq_map = {'1m':'T', '2m':'2T', '5m':'5T', '15m':'15T', '30m':'30T', '60m':'H', '90m':'90T', '1h':'H', '1d':'D', '5d':'5D', '1wk':'W', '1mo':'M', '3mo':'3M'}
+            freq = freq_map.get(timeframe, 'D')
+            future_dates = pd.date_range(start=last_date + pd.to_timedelta(1, unit=freq.replace('T', 'min').replace('M', 'ME')), periods=horizon, freq=freq)
+            fig.add_trace(go.Scatter(x=future_dates, y=result["latest_prediction"], name="Forecast", mode="lines", line=dict(color="orange", dash="dash")))
+        fig.update_layout(title="Historical Data and Future Forecast", xaxis_title="Date", yaxis_title="Value")
+        return fig
+    except Exception as e:
+        logging.error(f"Plot future forecast error: {e}")
+        return None
+
+def plot_indicators(df):
+    try:
+        fig = go.Figure()
+        fig.add_trace(go.Candlestick(x=df['Date'], open=df['Open'], high=df['High'], low=df['Low'], close=df['value'], name='Price'))
+        for col in df.columns:
+            if col not in ['Date', 'Open', 'High', 'Low', 'value', 'Volume']:
+                fig.add_trace(go.Scatter(x=df['Date'], y=df[col], name=col))
+        fig.update_layout(title="Stock Price with Indicators", yaxis_title="Value", xaxis_rangeslider_visible=True)
+        return fig
+    except Exception as e:
+        logging.error(f"Plot indicators error: {e}")
+        return None
+
+def plot_metrics_r2(result):
+    if 'metrics' in result:
+        metrics = {k: v for k, v in result['metrics'].items() if k in ['R2', 'MAPE']}
+        df = pd.DataFrame(list(metrics.items()), columns=['Metric', 'Value'])
+        fig = px.bar(df, x='Metric', y='Value', title="R² and MAPE Metrics")
+        return fig
+    return None
+
+def plot_metrics_errors(result):
+    if 'metrics' in result:
+        metrics = {k: v for k, v in result['metrics'].items() if k in ['RMSE', 'MAE']}
+        df = pd.DataFrame(list(metrics.items()), columns=['Metric', 'Value'])
+        fig = px.bar(df, x='Metric', y='Value', title="RMSE and MAE Metrics")
+        return fig
+    return None
+
+def plot_loss_curve(result):
+    train_losses = result.get('train_loss', [])
+    val_losses = result.get('val_loss', [])
+    fig = go.Figure()
+    fig.add_trace(go.Scatter(y=train_losses, name='Train Loss', mode="lines", line=dict(color="blue")))
+    if val_losses:
+        fig.add_trace(go.Scatter(y=val_losses, name='Val Loss', mode="lines", line=dict(color="orange")))
+    fig.update_layout(title="Training Loss Curve", xaxis_title="Epoch", yaxis_title="Loss")
+    return fig
+
+def plot_model_architecture(result):
+    if "model_summary" in result:
+        fig, ax = plt.subplots(figsize=(10, 6))
+        ax.text(0, 1, result["model_summary"], fontsize=8, family='monospace', va='top')
+        ax.axis('off')
+        plt.tight_layout()
+        return fig
+    return None