fast_explorer.py

#!/usr/bin/env python3
"""
Fast Enhanced Data Explorer (skips slow AI setup)
"""
import pandas as pd
import os
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')

# CSV file path
csv_file_path = "C:/Users/Cosmo/Desktop/NTU Peak Singtel/outsystems_sample_logs_6months.csv"

class FastDataExplorer:
    """Fast data explorer with optional AI capabilities"""
    
    def __init__(self, csv_path=csv_file_path):
        self.csv_path = csv_path
        self.df = None
        self.agent = None
        print("🚀 Fast Data Explorer initialized!")
        print("💡 AI features can be setup later via option 9")
    
    def setup_ai_on_demand(self):
        """Setup AI only when requested"""
        if self.agent is not None:
            print("✅ AI already configured!")
            return True
            
        print("🤖 Setting up AI on demand...")
        try:
            # Try Ollama first
            import ollama
            models = ollama.list()
            if models and 'models' in models and len(models['models']) > 0:
                print("✅ Ollama detected - configuring...")
                
                # Simple Ollama wrapper
                class SimpleOllama:
                    def run(self, prompt):
                        try:
                            response = ollama.generate(model='llama2', prompt=prompt)
                            return response['response']
                        except Exception as e:
                            return f"Error: {e}"
                
                self.agent = SimpleOllama()
                print("✅ AI configured with Ollama!")
                return True
                
        except Exception as e:
            print(f"⚠️ AI setup failed: {e}")
            
        # Fallback: No AI
        print("❌ AI not available - using manual analysis only")
        return False
    
    def load_data(self):
        """Load the CSV data"""
        print(f"\n📁 Loading data from: {self.csv_path}")
        
        try:
            if not os.path.exists(self.csv_path):
                print(f"❌ Error: File not found at {self.csv_path}")
                return None
            
            self.df = pd.read_csv(self.csv_path)
            
            print("=== DATA LOADED SUCCESSFULLY ===")
            print(f"📁 File: {os.path.basename(self.csv_path)}")
            print(f"📊 Dataset shape: {self.df.shape}")
            print(f"📋 Columns: {list(self.df.columns)}")
            print("\n=== FIRST 5 ROWS ===")
            print(self.df.head())
            
            return self.df
            
        except Exception as e:
            print(f"Error loading data: {str(e)}")
            return None
    
    def quick_analysis(self):
        """Quick manual analysis"""
        if self.df is None:
            print("❌ No data loaded.")
            return
            
        print("\n=== QUICK ANALYSIS ===")
        print(f"📊 Shape: {self.df.shape}")
        print(f"📋 Columns: {list(self.df.columns)}")
        
        # Log level analysis
        if 'LogLevel' in self.df.columns:
            print("\n📈 Log Level Distribution:")
            print(self.df['LogLevel'].value_counts())
            
        # Error analysis
        if 'ErrorId' in self.df.columns:
            error_count = self.df['ErrorId'].notna().sum()
            print(f"\n🚨 Errors found: {error_count} out of {len(self.df)} records")
            
        # Time analysis
        if 'Timestamp' in self.df.columns:
            print(f"\n📅 Time range: {self.df['Timestamp'].min()} to {self.df['Timestamp'].max()}")
    
    def ai_analysis(self, query):
        """AI analysis with on-demand setup"""
        if self.df is None:
            print("❌ No data loaded.")
            return
            
        if self.agent is None:
            print("🤖 Setting up AI...")
            if not self.setup_ai_on_demand():
                return
        
        print(f"\n🔍 Analyzing: {query}")
        
        # Prepare simple data summary
        data_summary = f"""
Data Analysis Request:
Dataset has {self.df.shape[0]} rows and {self.df.shape[1]} columns.
Columns: {list(self.df.columns)}

Sample data:
{self.df.head(2).to_string()}

Question: {query}

Please provide insights about this OutSystems log data.
"""
        
        try:
            response = self.agent.run(data_summary)
            print("\n" + "="*50)
            print("🤖 AI ANALYSIS RESULT")
            print("="*50)
            print(response)
            print("="*50)
        except Exception as e:
            print(f"❌ AI analysis failed: {e}")
    
    def interactive_menu(self):
        """Interactive menu"""
        while True:
            print("\n" + "="*40)
            print("🚀 FAST DATA EXPLORER")
            print("="*40)
            print("1. Load data")
            print("2. Quick analysis")
            print("3. Show data summary")
            print("4. AI analysis (auto-setup)")
            print("5. Setup AI manually")
            print("6. Exit")
            print("="*40)
            
            choice = input("Choice (1-6): ").strip()
            
            if choice == '1':
                self.load_data()
            elif choice == '2':
                self.quick_analysis()
            elif choice == '3':
                if self.df is not None:
                    print(f"\n📊 Summary: {self.df.shape[0]} rows, {self.df.shape[1]} columns")
                    print(f"📋 Columns: {list(self.df.columns)}")
                else:
                    print("❌ No data loaded.")
            elif choice == '4':
                query = input("💬 Your question: ").strip()
                if query:
                    self.ai_analysis(query)
                else:
                    print("❌ No question entered.")
            elif choice == '5':
                self.setup_ai_on_demand()
            elif choice == '6':
                print("👋 Goodbye!")
                break
            else:
                print("❌ Invalid choice.")

if __name__ == "__main__":
    explorer = FastDataExplorer()
    explorer.interactive_menu()