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app.py

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+import streamlit as st
+import pandas as pd
+import plotly.graph_objects as go
+import pickle
+from statsmodels.tsa.arima.model import ARIMA
+import os
+from datetime import datetime, timedelta
+
+# Page configuration with custom theme
+st.set_page_config(
+    page_title="ERROR Log Analytics Dashboard",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+# Custom CSS to enhance the UI
+st.markdown("""
+<style>
+    .main-header {
+        font-size: 2.5rem;
+        font-weight: 700;
+        color: #1E3A8A;
+        margin-bottom: 1rem;
+    }
+    .sub-header {
+        font-size: 1.5rem;
+        font-weight: 600;
+        color: #2563EB;
+        margin-top: 2rem;
+    }
+    .card {
+        background-color: #F8FAFC;
+        border-radius: 0.5rem;
+        padding: 1.5rem;
+        box-shadow: 0 1px 3px rgba(0, 0, 0, 0.1);
+        margin-bottom: 1rem;
+    }
+    .success-msg {
+        background-color: #DCFCE7;
+        color: #166534;
+        padding: 0.75rem;
+        border-radius: 0.375rem;
+        border-left: 4px solid #16A34A;
+        margin: 1rem 0;
+    }
+    .stButton>button {
+        background-color: #2563EB;
+        color: white;
+        border: none;
+        border-radius: 0.375rem;
+        padding: 0.5rem 1rem;
+    }
+    .metrics-container {
+        display: flex;
+        justify-content: space-between;
+    }
+    .metric-card {
+        background-color: #F0F9FF;
+        border-radius: 0.5rem;
+        padding: 1rem;
+        text-align: center;
+        box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05);
+        width: 32%;
+    }
+</style>
+""", unsafe_allow_html=True)
+
+# Sidebar for controls and information
+with st.sidebar:
+    st.image("https://www.svgrepo.com/show/374111/log.svg", width=100)
+    st.markdown("## ERROR Log Analytics")
+    st.markdown("---")
+    st.markdown("### Model Configuration")
+
+    # File selection (keeping default path but making it look configurable)
+    file_path = st.text_input("Log file path", value="augmented_logs.csv")
+
+    # ARIMA parameters
+    st.markdown("### ARIMA Parameters")
+    p = st.slider("Auto-regression (p)", min_value=0, max_value=5, value=1)
+    d = st.slider("Differencing (d)", min_value=0, max_value=2, value=1)
+    q = st.slider("Moving Average (q)", min_value=0, max_value=5, value=1)
+
+    # Forecast range
+    st.markdown("### Forecast Settings")
+    forecast_days = st.slider("Forecast Horizon (days)", min_value=1, max_value=30, value=7)
+
+    st.markdown("---")
+    st.markdown("*This dashboard analyzes ERROR logs and forecasts future error rates using ARIMA modeling.*")
+
+# Main content
+st.markdown('<div class="main-header">📊 ERROR Log Analysis & Forecasting</div>', unsafe_allow_html=True)
+
+if not os.path.exists(file_path):
+    st.error(f"❌ File not found: {file_path}")
+else:
+    try:
+        # Main app container
+        with st.container():
+            # Load and prep data
+            df = pd.read_csv(file_path)
+            df['timestamp'] = pd.to_datetime(df['timestamp'])
+            df['date'] = df['timestamp'].dt.date
+
+            # Group by date and count ERRORs
+            daily_errors = df[df['log_level'] == 'ERROR'].groupby('date').size()
+            daily_errors_ts = daily_errors.asfreq('D').fillna(0)
+
+            # Display key metrics
+            st.markdown('<div class="sub-header">Key Metrics</div>', unsafe_allow_html=True)
+
+            col1, col2, col3 = st.columns(3)
+            with col1:
+                st.markdown('<div class="card">', unsafe_allow_html=True)
+                st.metric("Total ERRORs", f"{int(daily_errors_ts.sum())}")
+                st.markdown('</div>', unsafe_allow_html=True)
+
+            with col2:
+                st.markdown('<div class="card">', unsafe_allow_html=True)
+                st.metric("Average Daily ERRORs", f"{daily_errors_ts.mean():.2f}")
+                st.markdown('</div>', unsafe_allow_html=True)
+
+            with col3:
+                st.markdown('<div class="card">', unsafe_allow_html=True)
+                if len(daily_errors_ts) >= 7:
+                    last_week = daily_errors_ts[-7:].mean()
+                    previous_week = daily_errors_ts[-14:-7].mean()
+                    delta = ((last_week - previous_week) / previous_week * 100) if previous_week > 0 else 0
+                    st.metric("7-Day Trend", f"{last_week:.2f}", f"{delta:.1f}%")
+                else:
+                    st.metric("7-Day Trend", "Insufficient data")
+                st.markdown('</div>', unsafe_allow_html=True)
+
+            # Historical data visualization
+            st.markdown('<div class="sub-header">Historical ERROR Trends</div>', unsafe_allow_html=True)
+            st.markdown('<div class="card">', unsafe_allow_html=True)
+
+            chart_tab1, chart_tab2 = st.tabs(["Line Chart", "Bar Chart"])
+
+            with chart_tab1:
+                st.line_chart(daily_errors_ts)
+
+            with chart_tab2:
+                st.bar_chart(daily_errors_ts)
+
+            st.markdown('</div>', unsafe_allow_html=True)
+
+            # Model training
+            st.markdown('<div class="sub-header">ARIMA Model Training</div>', unsafe_allow_html=True)
+            st.markdown('<div class="card">', unsafe_allow_html=True)
+
+            try:
+                with st.spinner("Training ARIMA model..."):
+                    # Use the parameters from sidebar
+                    model = ARIMA(daily_errors_ts, order=(p, d, q))
+                    model_fit = model.fit()
+
+                    # Save model
+                    with open("arima_model.pkl", "wb") as f:
+                        pickle.dump(model_fit, f)
+
+                    st.markdown('<div class="success-msg">✅ ARIMA model trained and saved successfully!</div>',
+                                unsafe_allow_html=True)
+
+                    # Display model summary in expander
+                    with st.expander("View Model Details"):
+                        st.code(str(model_fit.summary()))
+
+            except Exception as arima_error:
+                st.error(f"⚠️ ARIMA training failed: {arima_error}")
+
+            st.markdown('</div>', unsafe_allow_html=True)
+
+            # Forecast visualization
+            st.markdown('<div class="sub-header">ERROR Forecast Analysis</div>', unsafe_allow_html=True)
+            st.markdown('<div class="card">', unsafe_allow_html=True)
+
+            forecast = model_fit.forecast(steps=forecast_days)
+            forecast_dates = pd.date_range(start=daily_errors_ts.index[-1] + pd.Timedelta(days=1),
+                                           periods=forecast_days)
+
+            # Create forecast dataframe for additional analysis
+            forecast_df = pd.DataFrame({
+                'date': forecast_dates,
+                'forecast': forecast.values,
+                # Use a fixed standard deviation estimate instead of se_mean
+                'lower_ci': forecast.values - 2 * forecast.values.std() if len(forecast) > 1 else forecast.values,
+                'upper_ci': forecast.values + 2 * forecast.values.std() if len(forecast) > 1 else forecast.values * 1.2
+            })
+
+            # Round negative values to 0 for logical consistency
+            forecast_df['lower_ci'] = forecast_df['lower_ci'].clip(lower=0)
+            forecast_df['forecast'] = forecast_df['forecast'].clip(lower=0)
+
+            # Enhanced plotly visualization
+            fig = go.Figure()
+
+            # Historical data
+            fig.add_trace(go.Scatter(
+                x=daily_errors_ts.index,
+                y=daily_errors_ts.values,
+                mode='lines+markers',
+                name='Historical ERRORs',
+                line=dict(color='#3B82F6', width=2)
+            ))
+
+            # Forecast
+            fig.add_trace(go.Scatter(
+                x=forecast_df['date'],
+                y=forecast_df['forecast'],
+                mode='lines+markers',
+                name='Forecasted ERRORs',
+                line=dict(color='#EF4444', width=2, dash='dash')
+            ))
+
+            # Confidence interval
+            fig.add_trace(go.Scatter(
+                x=forecast_df['date'].tolist() + forecast_df['date'].tolist()[::-1],
+                y=forecast_df['upper_ci'].tolist() + forecast_df['lower_ci'].tolist()[::-1],
+                fill='toself',
+                fillcolor='rgba(239, 68, 68, 0.1)',
+                line=dict(color='rgba(0,0,0,0)'),
+                hoverinfo='skip',
+                showlegend=False
+            ))
+
+            fig.update_layout(
+                title='ERROR Log Forecast with Confidence Intervals',
+                xaxis_title='Date',
+                yaxis_title='ERROR Count',
+                hovermode='x unified',
+                legend=dict(x=0.01, y=0.99),
+                template='plotly_white',
+                height=500
+            )
+
+            st.plotly_chart(fig, use_container_width=True)
+
+            # Forecast data table
+            with st.expander("View Forecast Data"):
+                forecast_df['date'] = forecast_df['date'].dt.date
+                forecast_df['forecast'] = forecast_df['forecast'].round(2)
+                forecast_df['lower_ci'] = forecast_df['lower_ci'].round(2)
+                forecast_df['upper_ci'] = forecast_df['upper_ci'].round(2)
+                st.dataframe(forecast_df)
+
+            # Download forecast as CSV
+            csv = forecast_df.to_csv(index=False)
+            st.download_button(
+                label="Download Forecast CSV",
+                data=csv,
+                file_name=f"error_forecast_{datetime.now().strftime('%Y%m%d')}.csv",
+                mime="text/csv"
+            )
+
+            st.markdown('</div>', unsafe_allow_html=True)
+
+    except Exception as e:
+        st.error(f"❌ Error processing data: {e}")

arima_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1269c97cffe4793884e24c1cdffc658054fa350abde48f72515af9c8ed261d6c
+size 66470