adding dashboard to the commit

PlantDashboard/data_handler.py

@@ -0,0 +1,133 @@
+import pandas as pd
+import json
+import csv
+import os
+from datetime import datetime
+
+class DataHandler:
+    def __init__(self):
+        self.data_directory = "plant_data"
+        self.ensure_data_directory()
+        
+    def ensure_data_directory(self):
+        """Ensure the data directory exists"""
+        if not os.path.exists(self.data_directory):
+            os.makedirs(self.data_directory)
+            
+    def save_prediction_data(self, parameters, prediction, filename=None):
+        """Save prediction data to CSV"""
+        if filename is None:
+            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+            filename = f"prediction_{timestamp}.csv"
+            
+        filepath = os.path.join(self.data_directory, filename)
+        
+        # Combine parameters and prediction results
+        data = {**parameters}
+        data.update({
+            'final_height': prediction['final_height'],
+            'growth_rate': prediction['growth_rate'],
+            'health_score': prediction['health_score'],
+            'optimal_conditions': prediction['optimal_conditions'],
+            'yield': prediction['yield'],
+            'timestamp': datetime.now().isoformat()
+        })
+        
+        # Convert to DataFrame
+        df = pd.DataFrame([data])
+        
+        try:
+            df.to_csv(filepath, index=False)
+            return filepath
+        except Exception as e:
+            print(f"Error saving data: {e}")
+            return None
+            
+    def load_historical_data(self):
+        """Load all historical prediction data"""
+        data_files = [f for f in os.listdir(self.data_directory) if f.endswith('.csv')]
+        
+        if not data_files:
+            return pd.DataFrame()
+            
+        all_data = []
+        for file in data_files:
+            filepath = os.path.join(self.data_directory, file)
+            try:
+                df = pd.read_csv(filepath)
+                all_data.append(df)
+            except Exception as e:
+                print(f"Error loading {file}: {e}")
+                
+        if all_data:
+            return pd.concat(all_data, ignore_index=True)
+        else:
+            return pd.DataFrame()
+            
+    def export_to_json(self, data, filename):
+        """Export data to JSON format"""
+        filepath = os.path.join(self.data_directory, filename)
+        
+        try:
+            with open(filepath, 'w') as f:
+                json.dump(data, f, indent=2, default=str)
+            return filepath
+        except Exception as e:
+            print(f"Error exporting to JSON: {e}")
+            return None
+            
+    def import_from_json(self, filename):
+        """Import data from JSON format"""
+        filepath = os.path.join(self.data_directory, filename)
+        
+        try:
+            with open(filepath, 'r') as f:
+                data = json.load(f)
+            return data
+        except Exception as e:
+            print(f"Error importing from JSON: {e}")
+            return None
+            
+    def get_plant_statistics(self, plant_type=None):
+        """Get statistics for plant predictions"""
+        df = self.load_historical_data()
+        
+        if df.empty:
+            return {}
+            
+        if plant_type:
+            df = df[df['plant_type'] == plant_type]
+            
+        if df.empty:
+            return {}
+            
+        stats = {
+            'total_predictions': len(df),
+            'avg_final_height': df['final_height'].mean(),
+            'avg_health_score': df['health_score'].mean(),
+            'avg_growth_rate': df['growth_rate'].mean(),
+            'best_conditions': df['optimal_conditions'].max(),
+            'worst_conditions': df['optimal_conditions'].min()
+        }
+        
+        return stats
+        
+    def create_comparison_report(self, plant_types):
+        """Create a comparison report for different plant types"""
+        df = self.load_historical_data()
+        
+        if df.empty:
+            return {}
+            
+        report = {}
+        for plant_type in plant_types:
+            plant_data = df[df['plant_type'] == plant_type]
+            if not plant_data.empty:
+                report[plant_type] = {
+                    'count': len(plant_data),
+                    'avg_height': plant_data['final_height'].mean(),
+                    'avg_health': plant_data['health_score'].mean(),
+                    'success_rate': len(plant_data[plant_data['health_score'] > 70]) / len(plant_data) * 100
+                }
+                
+        return report