Update app.py

app.py

@@ -36,7 +36,7 @@ if not app.debug:
 
 
 class DataStore:
-    def _init_(self):
+    def __init__(self):
         self.uploaded_data = None
         self.current_index = 0
         self.processed_data = []
@@ -54,7 +54,6 @@ def detect_anomalies(df):
         'filters': 90,
         'cables': 90
     }
-
     for idx, row in df.iterrows():
         row_alerts = []
         for component, threshold in thresholds.items():
@@ -76,13 +75,8 @@ def analyze_component_trends(df):
     """Analyze trends and patterns in component data"""
     trends = {}
     for component in ['brakes', 'filters', 'cables']:
-        # Calculate rolling average to identify trends
         rolling_avg = df[component].rolling(window=3).mean()
-
-        # Calculate rate of change
         rate_of_change = df[component].diff().mean()
-
-        # Identify peak usage periods
         peak_threshold = df[component].quantile(0.75)
         peak_periods = df[component] > peak_threshold
 
@@ -103,7 +97,6 @@ def generate_maintenance_insights(df, trends):
         'preventive_measures': [],
         'optimization_suggestions': []
     }
-
     for component in ['brakes', 'filters', 'cables']:
         avg = df[component].mean()
         max_val = df[component].max()
@@ -125,7 +118,7 @@ def generate_maintenance_insights(df, trends):
             insights['maintenance_recommendations'].append({
                 'component': component,
                 'urgency': 'High' if avg > 75 else 'Medium',
-                'action': f"Schedule maintenance within {' 24 hours' if avg > 75 else ' one week'}",
+                'action': f"Schedule maintenance within {'24 hours' if avg > 75 else 'one week'}",
                 'reason': f"Increasing trend with high average ({round(avg, 1)}%)"
             })
 
@@ -146,7 +139,6 @@ def generate_maintenance_insights(df, trends):
                 'potential_impact': 'High',
                 'expected_benefit': 'Reduced wear and extended component life'
             })
-
     return insights
 
 
@@ -160,22 +152,17 @@ def calculate_statistics(df):
         },
         'critical_components': [],
         'maintenance_suggestions': [],
-        'component_health': {},  # New: Component health status
-        'maintenance_priority': [],  # New: Prioritized maintenance list
-        'performance_metrics': {},  # New: Detailed performance metrics
-        'detailed_analysis': {}  # New: Detailed analysis
+        'component_health': {},
+        'maintenance_priority': [],
+        'performance_metrics': {},
+        'detailed_analysis': {}
     }
-
-    # Calculate component health and status
     for component in ['brakes', 'filters', 'cables']:
         avg = df[component].mean()
         max_val = df[component].max()
         min_val = df[component].min()
         std_dev = df[component].std()
-
-        # Calculate health score (0-100)
         health_score = max(0, min(100, 100 - (avg / 100 * 100)))
-
         stats['component_health'][component] = {
             'health_score': round(health_score, 1),
             'average': round(avg, 2),
@@ -184,8 +171,6 @@ def calculate_statistics(df):
             'variability': round(std_dev, 2),
             'status': 'Good' if avg < 60 else 'Warning' if avg < 75 else 'Critical'
         }
-
-        # Identify critical components
         if avg > 70:
             stats['critical_components'].append({
                 'name': component,
@@ -196,7 +181,6 @@ def calculate_statistics(df):
                 'variability': round(std_dev, 2)
             })
 
-    # Generate prioritized maintenance suggestions
     for component, health in stats['component_health'].items():
         if health['average'] > 80:
             stats['maintenance_priority'].append({
@@ -223,25 +207,19 @@ def calculate_statistics(df):
                 'recommendation': f"Monitor {component} performance"
             })
 
-    # Generate detailed maintenance suggestions
     for component, health in stats['component_health'].items():
         suggestions = []
-
         if health['average'] > 80:
             suggestions.append(f"URGENT: Immediate maintenance required - {component} showing critical wear")
         elif health['average'] > 70:
             suggestions.append(f"WARNING: Schedule maintenance soon - {component} performance degrading")
-
         if health['variability'] > 10:
             suggestions.append(f"Monitor {component} - Showing inconsistent readings (±{health['variability']}%)")
-
         if health['max_reading'] > 90:
             suggestions.append(f"Investigate {component} peak readings of {health['max_reading']}%")
-
         if suggestions:
             stats['maintenance_suggestions'].extend(suggestions)
 
-    # Calculate performance metrics
     stats['performance_metrics'] = {
         'overall_health': round(sum(h['health_score'] for h in stats['component_health'].values()) / 3, 1),
         'critical_count': len([h for h in stats['component_health'].values() if h['status'] == 'Critical']),
@@ -249,23 +227,18 @@ def calculate_statistics(df):
         'healthy_count': len([h for h in stats['component_health'].values() if h['status'] == 'Good'])
     }
 
-    # Add new detailed analyses
     trends = analyze_component_trends(df)
     maintenance_insights = generate_maintenance_insights(df, trends)
-
     stats['detailed_analysis'] = {
         'trends': trends,
         'insights': maintenance_insights
     }
-
     return stats
 
 
 def create_graphs(df):
     """Create all visualization graphs"""
     graphs = {}
-
-    # Gauge Charts for all components
     components = ['brakes', 'filters', 'cables']
     for component in components:
         latest_value = df[component].iloc[-1]
@@ -292,10 +265,8 @@ def create_graphs(df):
         gauge.update_layout(height=300)
         graphs[f'{component}_gauge'] = gauge.to_html(full_html=False)
 
-    # Bar Chart for current readings with historical average
     current_values = [df[comp].iloc[-1] for comp in components]
     avg_values = [df[comp].mean() for comp in components]
-
     bar = go.Figure(data=[
         go.Bar(name='Current Reading', x=components, y=current_values),
         go.Bar(name='Historical Average', x=components, y=avg_values)
@@ -307,16 +278,13 @@ def create_graphs(df):
     )
     graphs['bar'] = bar.to_html(full_html=False)
 
-    # Time Series Chart with Moving Average
     fig_time = go.Figure()
     for component in components:
-        # Add raw data
         fig_time.add_trace(go.Scatter(
             y=df[component],
             name=component.title(),
             mode='lines'
         ))
-        # Add moving average
         ma = df[component].rolling(window=3).mean()
         fig_time.add_trace(go.Scatter(
             y=ma,
@@ -324,7 +292,6 @@ def create_graphs(df):
             line=dict(dash='dash'),
             opacity=0.5
         ))
-
     fig_time.update_layout(
         title='Component Readings Over Time',
         height=400,
@@ -339,7 +306,6 @@ def create_graphs(df):
     )
     graphs['timeseries'] = fig_time.to_html(full_html=False)
 
-    # Correlation Matrix Heatmap
     corr_matrix = df[components].corr()
     heatmap = go.Figure(data=go.Heatmap(
         z=corr_matrix,
@@ -355,7 +321,6 @@ def create_graphs(df):
     )
     graphs['heatmap'] = heatmap.to_html(full_html=False)
 
-    # Box Plot for Distribution Analysis
     box_data = [go.Box(y=df[component], name=component.title()) for component in components]
     box_plot = go.Figure(data=box_data)
     box_plot.update_layout(
@@ -364,7 +329,6 @@ def create_graphs(df):
     )
     graphs['box_plot'] = box_plot.to_html(full_html=False)
 
-    # Scatter Matrix
     scatter_matrix = px.scatter_matrix(
         df[components],
         dimensions=components,
@@ -378,6 +342,8 @@ def create_graphs(df):
 
 @app.route('/')
 def index():
+    # Render the homepage with an "Analyze" button
+    # (Assuming your index.html contains the button that directs to the /analyze route)
     return render_template('index.html',
                            data=None,
                            graphs=None,
@@ -385,57 +351,34 @@ def index():
                            anomalies=None)
 
 
-@app.route('/upload', methods=['POST'])
-def upload():
-    if 'file' not in request.files:
-        flash('No file uploaded', 'error')
-        return redirect(url_for('index'))
-
-    file = request.files['file']
-    if file.filename == '':
-        flash('No file selected', 'error')
-        return redirect(url_for('index'))
-
+@app.route('/analyze', methods=['GET'])
+def analyze():
     try:
-        # Read the file
-        if file.filename.endswith('.csv'):
-            df = pd.read_csv(file)
-        elif file.filename.endswith(('.xlsx', '.xls')):
-            df = pd.read_excel(file)
-        else:
-            flash('Unsupported file format. Please upload CSV or Excel file.', 'error')
+        # Hardcoded CSV file path (ensure data.csv is present in the same directory)
+        file_path = 'test_data (1).csv'
+        if not os.path.exists(file_path):
+            flash('Data file not found', 'error')
             return redirect(url_for('index'))
-
-        # Validate required columns
+        df = pd.read_csv(file_path)
         required_columns = ['brakes', 'filters', 'cables']
         missing_columns = [col for col in required_columns if col not in df.columns]
-
         if missing_columns:
             flash(f"Missing required columns: {', '.join(missing_columns)}", 'error')
             return redirect(url_for('index'))
-
-        # Store the data and process it
         data_store.uploaded_data = df
         data_store.anomalies = detect_anomalies(df)
-
-        # Calculate statistics
         stats = calculate_statistics(df)
-
-        # Create graphs
         graphs = create_graphs(df)
-
-        # Render template with all the data
         return render_template('index.html',
                                data=df.to_dict('records'),
                                graphs=graphs,
                                stats=stats,
                                anomalies=data_store.anomalies)
-
     except Exception as e:
-        flash(f"Error processing file: {str(e)}", 'error')
+        flash(f"Error processing data: {str(e)}", 'error')
         return redirect(url_for('index'))
 
 
 if __name__ == '__main__':
     port = int(os.environ.get("PORT", 7860))
-    app.run(host='0.0.0.0', port=port)
+    app.run(host='0.0.0.0', port=port)