Sync App files

app.py

@@ -130,6 +130,11 @@ class StockPredictor:
             # Define features
             features = ["year", "month", "day", "ma_5", "ma_10"]
 
+            # Predict the actual values in the dataset
+            X_actual = data[features]
+            actual_predictions = model.predict(X_actual)
+            data["predicted_close"] = actual_predictions
+
             # Use the last available values for features
             last_date = data["date"].max()
             next_30_days = pd.date_range(
@@ -165,22 +170,16 @@ class StockPredictor:
                 {"date": next_30_days, "predicted_close": predictions}
             )
 
-            # Concatenate actual and predicted data for plotting
-            actual_df = data[["date", "close"]].iloc[-30:].copy()
-            actual_df.rename(columns={"close": "actual_close"}, inplace=True)
-            plot_data = pd.concat([actual_df, prediction_df], ignore_index=True)
+            # Concatenate actual and predicted data for plotting, limiting to last 60 days
+            combined_df = pd.concat(
+                [data[["date", "close", "predicted_close"]], prediction_df],
+                ignore_index=True,
+            )
+            plot_data = combined_df.tail(60)
 
             plt.figure(figsize=(14, 7))
-            plt.plot(
-                plot_data["date"].iloc[:30],
-                plot_data["actual_close"].iloc[:30],
-                label="Actual",
-            )
-            plt.plot(
-                plot_data["date"].iloc[30:],
-                plot_data["predicted_close"].iloc[30:],
-                label="Predicted",
-            )
+            plt.plot(plot_data["date"], plot_data["close"], label="Actual")
+            plt.plot(plot_data["date"], plot_data["predicted_close"], label="Predicted")
             plt.xlabel("Date")
             plt.ylabel("Stock Price")
             plt.title(
@@ -229,7 +228,7 @@ def create_gradio_interface(stock_predictor):
         fn=stock_predictor.forecast,
         inputs=[dropdown, slider],
         outputs=[
-            gr.DataFrame(headers=["date", "actual_close", "predicted_close"]),
+            gr.DataFrame(headers=["date", "close", "predicted_close"]),
             gr.Image(type="numpy"),
         ],
         title="Stock Price Forecasting",