Update src/streamlit_app.py

src/streamlit_app.py

@@ -164,10 +164,23 @@ try:
 except Exception as e:
     st.error(f"Could not decompose series: {e}")
 
-# ACF / PACF Plots
+# ACF / PACF Plots (CORREGIDO)
 st.subheader("🔗 Autocorrelation (ACF) & Partial Autocorrelation (PACF)")
 
-max_lags = st.slider("Max lags:", min_value=10, max_value=100, value=40, step=5)
+# Calculate safe max lags (must be < 50% of sample size for PACF)
+n = len(ts.dropna())
+safe_max_lag = max(1, int(n * 0.49))  # Must be strictly less than 50%
+
+# Adjust slider dynamically
+max_lags_default = min(40, safe_max_lag)
+max_lags = st.slider(
+    "Max lags:",
+    min_value=1,
+    max_value=safe_max_lag,
+    value=max_lags_default,
+    step=1,
+    help=f"Max allowed lags: {safe_max_lag} (based on sample size: {n})"
+)
 
 col1, col2 = st.columns(2)
 
@@ -179,9 +192,13 @@ with col1:
 
 with col2:
     st.write("**PACF Plot**")
-    fig_pacf, ax_pacf = plt.subplots(figsize=(6, 4))
-    plot_pacf(ts.dropna(), lags=max_lags, ax=ax_pacf)
-    st.pyplot(fig_pacf)
+    try:
+        fig_pacf, ax_pacf = plt.subplots(figsize=(6, 4))
+        plot_pacf(ts.dropna(), lags=max_lags, ax=ax_pacf)
+        st.pyplot(fig_pacf)
+    except Exception as e:
+        st.error(f"Could not generate PACF plot: {e}")
+        st.write("Try reducing the number of lags.")
 
 # === FORECASTING MODELS ===
 st.header("🤖 Forecasting Models")
@@ -255,94 +272,4 @@ if forecast is not None:
     # Metrics
     mae = mean_absolute_error(test, forecast)
     mse = mean_squared_error(test, forecast)
-    rmse = np.sqrt(mse)
-    
-    st.subheader("📈 Forecast Results")
-    col1, col2, col3 = st.columns(3)
-    col1.metric("MAE", f"{mae:.2f}")
-    col2.metric("MSE", f"{mse:.2f}")
-    col3.metric("RMSE", f"{rmse:.2f}")
-
-    # Plot forecast vs actual
-    fig = go.Figure()
-    fig.add_trace(go.Scatter(x=train.index, y=train, mode='lines', name='Training', line=dict(color='blue')))
-    fig.add_trace(go.Scatter(x=test.index, y=test, mode='lines', name='Actual', line=dict(color='green')))
-    fig.add_trace(go.Scatter(x=test.index, y=forecast, mode='lines+markers', name='Forecast', line=dict(color='red', dash='dash')))
-    fig.update_layout(
-        title=f"{model_choice} Forecast",
-        xaxis_title="Date",
-        yaxis_title=value_col,
-        legend=dict(x=0, y=1)
-    )
-    st.plotly_chart(fig, use_container_width=True)
-
-    # Allow forecasting into future
-    st.subheader("🔮 Forecast Future Periods")
-    future_periods = st.number_input("Number of future periods to forecast:", min_value=1, max_value=365, value=30, step=1)
-    
-    if st.button("🚀 Generate Future Forecast"):
-        try:
-            if model_choice == "Holt-Winters Exponential Smoothing":
-                future_forecast = model.forecast(future_periods)
-                last_date = ts.index[-1]
-                if freq == 'D':
-                    future_dates = pd.date_range(start=last_date + pd.Timedelta(days=1), periods=future_periods, freq='D')
-                elif freq == 'W':
-                    future_dates = pd.date_range(start=last_date + pd.Timedelta(weeks=1), periods=future_periods, freq='W')
-                elif freq == 'M':
-                    future_dates = pd.date_range(start=last_date + pd.DateOffset(months=1), periods=future_periods, freq='M')
-                else:
-                    future_dates = pd.date_range(start=last_date + pd.Timedelta(days=1), periods=future_periods, freq='D')
-                
-            elif model_choice == "ARIMA":
-                future_forecast = model.forecast(future_periods)
-                last_date = ts.index[-1]
-                if freq == 'D':
-                    future_dates = pd.date_range(start=last_date + pd.Timedelta(days=1), periods=future_periods, freq='D')
-                elif freq == 'W':
-                    future_dates = pd.date_range(start=last_date + pd.Timedelta(weeks=1), periods=future_periods, freq='W')
-                elif freq == 'M':
-                    future_dates = pd.date_range(start=last_date + pd.DateOffset(months=1), periods=future_periods, freq='M')
-                else:
-                    future_dates = pd.date_range(start=last_date + pd.Timedelta(days=1), periods=future_periods, freq='D')
-                
-            elif model_choice == "Prophet":
-                last_date = ts.index[-1]
-                if freq == 'D':
-                    future_dates = pd.date_range(start=last_date + pd.Timedelta(days=1), periods=future_periods, freq='D')
-                elif freq == 'W':
-                    future_dates = pd.date_range(start=last_date + pd.Timedelta(weeks=1), periods=future_periods, freq='W')
-                elif freq == 'M':
-                    future_dates = pd.date_range(start=last_date + pd.DateOffset(months=1), periods=future_periods, freq='M')
-                else:
-                    future_dates = pd.date_range(start=last_date + pd.Timedelta(days=1), periods=future_periods, freq='D')
-                
-                future_df = pd.DataFrame({'ds': future_dates})
-                forecast_df = model.predict(future_df)
-                future_forecast = forecast_df['yhat'].values
-
-            # Plot future forecast
-            fig_future = go.Figure()
-            fig_future.add_trace(go.Scatter(x=ts.index, y=ts.values, mode='lines', name='Historical', line=dict(color='blue')))
-            fig_future.add_trace(go.Scatter(x=future_dates, y=future_forecast, mode='lines+markers', name='Future Forecast', line=dict(color='red', dash='dash')))
-            fig_future.update_layout(
-                title="Future Forecast",
-                xaxis_title="Date",
-                yaxis_title=value_col
-            )
-            st.plotly_chart(fig_future, use_container_width=True)
-
-            # Show as table
-            forecast_df = pd.DataFrame({
-                'Date': future_dates,
-                'Forecast': future_forecast
-            })
-            with st.expander("📋 View Forecast Table"):
-                st.dataframe(forecast_df)
-
-        except Exception as e:
-            st.error(f"Could not generate future forecast: {e}")
-
-# Footer
-st.markdown("---")
-st.caption(f"© {AUTHOR} | License {LICENSE} | Contact: {EMAIL}")
+    rm