from src.core.extractor import extractor

text = "Alice is a data scientist at TechCorp. She feels excited about the new AI project."

# Extract with default strategy
result = extractor.extract_for_neo4j(text)
print(f"Found {len(result['neo4j_data']['nodes'])} nodes and {len(result['neo4j_data']['relationships'])} relationships")

from src.core.visual_nodes import visual_nodes

# Generate interactive visualization
html_path = visual_nodes.visualize_text_extraction(
    text=text,
    strategy="literary",
    save_path="output/demo.html"
)
# Open the HTML file in your browser!

# Basic visualization
visual_nodes.visualize_text_extraction(text, strategy="scientific")

# Custom styling
custom_visual = VisualNodes(
    width="1200px", 
    height="800px",
    bgcolor="#f8f9fa"
)

# Comparison analysis  
visual_nodes.create_comparison_view(
    data_list=[result1, result2],
    titles=["Strategy A", "Strategy B"]
)

# Generate Cypher statements
result = extractor.extract_for_neo4j_merge(text, strategy="business")

# Get CREATE statements
nodes_cypher = result['merge_statements']['nodes']
relationships_cypher = result['merge_statements']['relationships']

# Execute in Neo4j
# driver.session().run(nodes_cypher)
# driver.session().run(relationships_cypher)

from src.core.extractor import extractor
from src.core.visual_nodes import visual_nodes

# Multi-step analysis pipeline
text = """
Dr. Sarah Chen, a machine learning researcher at Stanford University, 
published groundbreaking work on neural networks. She collaborates 
with Dr. Mike Johnson from MIT on deep learning applications.
"""

# 1. Extract with academic strategy
result = extractor.extract_for_neo4j(text, strategy="academic")

# 2. Visualize for quality check
visual_nodes.visualize_neo4j_data(
    result['neo4j_data'], 
    title="Academic Knowledge Graph"
)

# 3. Generate database import
cypher_statements = result['cypher_statements']
print("Ready for Neo4j import!")

# Compare different extraction approaches
strategies = ["literary", "business", "academic"]
results = []

for strategy in strategies:
    result = extractor.extract_for_neo4j(text, strategy=strategy)
    results.append(result['neo4j_data'])

# Generate comparison visualization
visual_nodes.create_comparison_view(
    data_list=results,
    titles=[f"Strategy: {s}" for s in strategies],
    save_dir="analysis/strategy_comparison"
)

# Create custom extraction with granular control
result = extractor.extract(
    text=text,
    entities=["person", "organization", "project"],
    relations=["works_at", "collaborates_with"],
    breadth="comprehensive",
    depth="inferential", 
    confidence="medium",
    context_scope="document"
)

strategy = strategy_manager.load_strategy("my_domain")
result = extractor.extract(text, strategy="my_domain")

# Custom node styles
visual_nodes.node_styles.update({
    'RESEARCHER': {
        'color': '#e74c3c',
        'shape': 'star', 
        'size': 30
    }
})

# Custom relationship styles  
visual_nodes.edge_styles.update({
    'COLLABORATES_WITH': {
        'color': '#3498db',
        'width': 4
    }
})