#!/usr/bin/env python3
"""
Test script to verify the analysis functions work correctly without Streamlit.
"""

import json
import sys
import os

# Add src directory to path
sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'src'))

from structure_analysis import detect_summary_fields, classify_data_structure, get_hierarchy_summary

def analyze_with_llm(data, target_field="rotation_enabled"):
    """
    Analyze data and generate a prompt for LLM analysis.
    Returns structured analysis without requiring Ollama.
    """
    print(f"DEBUG: Starting analysis with target_field: {target_field}")
    print(f"DEBUG: Data type: {type(data)}")
    print(f"DEBUG: Data keys: {list(data.keys()) if isinstance(data, dict) else 'Not a dict'}")
    
    # Detect summary fields
    print("DEBUG: Detecting summary fields...")
    summary_fields = detect_summary_fields(data)
    print(f"DEBUG: Found summary fields: {summary_fields}")
    
    print("DEBUG: Classifying data structure...")
    classification = classify_data_structure(data)
    print(f"DEBUG: Classification result: {classification}")
    
    print("DEBUG: Getting hierarchy summary...")
    hierarchy_summary = get_hierarchy_summary(data)
    print(f"DEBUG: Hierarchy summary: {hierarchy_summary}")
    
    # Extract samples
    print("DEBUG: Extracting samples...")
    sample_object = {}
    if 'results' in data:
        print("DEBUG: Found 'results' key in data")
        for section_name, section in data['results'].items():
            print(f"DEBUG: Processing section '{section_name}': {type(section)}")
            if isinstance(section, list) and len(section) > 0:
                sample_object = section[0]
                print(f"DEBUG: Found sample object from list: {sample_object}")
                break
            elif isinstance(section, dict):
                for key, value in section.items():
                    if isinstance(value, list) and len(value) > 0:
                        sample_object = value[0] if isinstance(value[0], dict) else {}
                        print(f"DEBUG: Found sample object from dict list: {sample_object}")
                        break
    else:
        print("DEBUG: No 'results' key found in data")
    
    summary_sample = data.get('results', {}).get('summary', {}) or data.get('summary', {})
    print(f"DEBUG: Summary sample: {summary_sample}")
    
    # Count objects with target field
    def count_objects_with_field(obj, field_name):
        count = 0
        if isinstance(obj, dict):
            if field_name in obj:
                count += 1
            for v in obj.values():
                count += count_objects_with_field(v, field_name)
        elif isinstance(obj, list):
            for item in obj:
                count += count_objects_with_field(item, field_name)
        return count
    
    print("DEBUG: Counting objects with target field...")
    total_objects = count_objects_with_field(data, target_field)
    print(f"DEBUG: Total objects with '{target_field}': {total_objects}")
    
    # Generate analysis
    print("DEBUG: Generating analysis...")
    analysis = {
        "summary_fields_detected": summary_fields[:10],
        "classification": classification,
        "hierarchy_summary": hierarchy_summary,
        "total_objects": total_objects,
        "sample_object": sample_object,
        "summary_sample": summary_sample,
        "recommended_fields": []
    }
    print(f"DEBUG: Initial analysis: {analysis}")
    
    # Recommend fields based on priority
    print("DEBUG: Generating field recommendations...")
    if summary_fields:
        analysis["recommended_fields"].extend(summary_fields[:3])
        print(f"DEBUG: Added summary fields: {summary_fields[:3]}")
    if classification.get('config_fields'):
        analysis["recommended_fields"].extend(classification['config_fields'][:2])
        print(f"DEBUG: Added config fields: {classification['config_fields'][:2]}")
    if sample_object:
        target_related = [k for k in sample_object.keys() if target_field in k.lower()]
        analysis["recommended_fields"].extend(target_related)
        print(f"DEBUG: Added target-related fields: {target_related}")
    
    print(f"DEBUG: Final recommended fields: {analysis['recommended_fields']}")
    print("DEBUG: Analysis completed successfully")
    return analysis

def test_analysis_functions():
    """Test the analysis functions with sample data."""
    
    # Load test data
    with open('test_data.json', 'r') as f:
        data = json.load(f)
    
    print("=== Testing Analysis Functions ===")
    print(f"Data loaded: {type(data)}")
    print(f"Data keys: {list(data.keys())}")
    print()
    
    # Test detect_summary_fields
    print("Testing detect_summary_fields...")
    summary_fields = detect_summary_fields(data)
    print(f"Summary fields found: {summary_fields}")
    print()
    
    # Test classify_data_structure
    print("Testing classify_data_structure...")
    classification = classify_data_structure(data)
    print(f"Classification result: {classification}")
    print()
    
    # Test get_hierarchy_summary
    print("Testing get_hierarchy_summary...")
    hierarchy_summary = get_hierarchy_summary(data)
    print(f"Hierarchy summary: {hierarchy_summary}")
    print()
    
    # Test the full analysis function
    print("Testing full analysis...")
    try:
        analysis_result = analyze_with_llm(data, "rotation_enabled")
        print("✅ Analysis completed successfully!")
        print(f"Analysis keys: {list(analysis_result.keys())}")
        print(f"Recommended fields: {analysis_result.get('recommended_fields', [])}")
        print(f"Total objects with 'rotation_enabled': {analysis_result.get('total_objects', 0)}")
    except Exception as e:
        print(f"❌ Analysis failed: {e}")
        import traceback
        traceback.print_exc()

if __name__ == "__main__":
    test_analysis_functions()