app.py

#!/usr/bin/env python3
"""
Hugging Face Streamlit App for LLM Field Analyzer
Upload a JSON file and analyze important fields with pattern generation.
"""

import streamlit as st
import json
from pathlib import Path
from typing import Dict, Any
import io

# Page configuration
st.set_page_config(
    page_title="Field Correlation Analyzer",
    page_icon="🤖",
    layout="wide"
)

# Import our modules
try:
    from structure_analysis import (
        detect_summary_fields,
        classify_data_structure,
        get_hierarchy_summary
    )
except ImportError:
    st.error("⚠️ structure_analysis.py not found. Make sure all files are uploaded.")
    st.stop()

# Session state
if 'analysis_result' not in st.session_state:
    st.session_state.analysis_result = None


def analyze_with_llm(data: Dict[str, Any], target_field: str = "rotation_enabled") -> Dict[str, Any]:
    """
    Analyze data and generate a prompt for LLM analysis.
    Returns structured analysis without requiring Ollama.
    """
    # Detect summary fields
    summary_fields = detect_summary_fields(data)
    classification = classify_data_structure(data)
    hierarchy_summary = get_hierarchy_summary(data)
    
    # Extract samples
    sample_object = {}
    if 'results' in data:
        for section in data['results'].values():
            if isinstance(section, list) and len(section) > 0:
                sample_object = section[0]
                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 {}
                        break
    
    summary_sample = data.get('results', {}).get('summary', {}) or data.get('summary', {})
    
    # 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
    
    total_objects = count_objects_with_field(data, target_field)
    
    # Generate 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": []
    }
    
    # Recommend fields based on priority
    if summary_fields:
        analysis["recommended_fields"].extend(summary_fields[:3])
    if classification.get('config_fields'):
        analysis["recommended_fields"].extend(classification['config_fields'][:2])
    if sample_object:
        analysis["recommended_fields"].extend([k for k in sample_object.keys() if target_field in k.lower()])
    
    return analysis


def generate_regex_patterns(field_names: list, data_sample: dict, summary_sample: dict) -> list:
    """Generate regex patterns for given fields."""
    patterns = []
    
    for field in field_names:
        # Try to find the field value type
        field_lower = field.lower()
        
        # Check in summary first
        if 'summary' in str(field):
            field_name = field.split('.')[-1]
            # Boolean pattern
            if field_name in summary_sample and isinstance(summary_sample.get(field_name), bool):
                patterns.append(f'"summary.{field_name}"\\s*:\\s*(true|false)')
            # Number pattern
            elif isinstance(summary_sample.get(field_name), (int, float)):
                patterns.append(f'"summary.{field_name}"\\s*:\\s*(\\d+)')
        # Check in object
        elif field in data_sample:
            value = data_sample[field]
            if isinstance(value, bool):
                patterns.append(f'"{field}"\\s*:\\s*(true|false)')
            elif isinstance(value, (int, float)):
                patterns.append(f'"{field}"\\s*:\\s*(\\d+)')
            elif isinstance(value, str):
                patterns.append(f'"{field}"\\s*:\\s*"([^"]*)"')
        else:
            # Generic pattern based on field name
            if 'percentage' in field_lower or 'count' in field_lower or 'total' in field_lower:
                patterns.append(f'"{field}"\\s*:\\s*(\\d+)')
            elif 'enabled' in field_lower or 'enforced' in field_lower:
                patterns.append(f'"{field}"\\s*:\\s*(true|false)')
            else:
                patterns.append(f'"{field}"\\s*:\\s*"([^"]*)"')
    
    return patterns


def main():
    """Main application."""
    st.title("🤖 Field Correlation Analyzer")
    st.markdown("Upload a JSON file to analyze important fields and generate regex patterns")
    
    # File upload
    uploaded_file = st.file_uploader(
        "Choose a JSON file",
        type=['json'],
        help="Upload a JSON file with structured data"
    )
    
    if uploaded_file is not None:
        # Read and parse JSON
        try:
            content = uploaded_file.read()
            data = json.loads(content)
            
            st.success("✅ File loaded successfully!")
            
            # Sidebar for settings
            with st.sidebar:
                st.header("⚙️ Settings")
                
                # Target field input
                target_field = st.text_input(
                    "Target Field",
                    value="rotation_enabled",
                    help="The field you want to analyze"
                )
                
                # Analyze button
                if st.button("🔍 Analyze", type="primary"):
                    with st.spinner("Analyzing data structure..."):
                        analysis_result = analyze_with_llm(data, target_field)
                        st.session_state.analysis_result = analysis_result
                        st.session_state.data = data
            
            # Display results if available
            if st.session_state.analysis_result:
                analysis = st.session_state.analysis_result
                
                # Summary metrics
                col1, col2, col3, col4 = st.columns(4)
                with col1:
                    st.metric("Summary Fields", len(analysis['summary_fields_detected']))
                with col2:
                    st.metric("Total Objects", analysis['total_objects'])
                with col3:
                    st.metric("Has Summary", "Yes" if analysis['hierarchy_summary']['has_summary'] else "No")
                with col4:
                    st.metric("Config Fields", len(analysis['classification'].get('config_fields', [])))
                
                st.markdown("---")
                
                # Create tabs
                tab1, tab2, tab3, tab4 = st.tabs([
                    "📊 Structure Analysis",
                    "🎯 Field Recommendations", 
                    "📝 Generated Patterns",
                    "📄 Raw Data"
                ])
                
                with tab1:
                    st.subheader("Data Hierarchy")
                    
                    # Summary fields
                    if analysis['summary_fields_detected']:
                        st.markdown("#### Level 1: Summary/Aggregate Fields (Highest Priority)")
                        for field in analysis['summary_fields_detected'][:10]:
                            st.write(f"✓ `{field}`")
                    
                    # Config fields
                    config_fields = analysis['classification'].get('config_fields', [])
                    if config_fields:
                        st.markdown("#### Level 2: Configuration/Compliance Fields")
                        for field in config_fields[:10]:
                            st.write(f"✓ `{field}`")
                    
                    # Object arrays
                    object_arrays = analysis['classification'].get('object_arrays', [])
                    if object_arrays:
                        st.markdown("#### Level 3: Object Arrays")
                        for field in object_arrays[:5]:
                            st.write(f"✓ `{field}`")
                    
                    # Show sample data
                    with st.expander("📋 View Summary Data Sample"):
                        st.json(analysis['summary_sample'])
                    
                    with st.expander("📋 View Object Data Sample"):
                        st.json(analysis['sample_object'])
                
                with tab2:
                    st.subheader("Recommended Fields for Analysis")
                    
                    if analysis['recommended_fields']:
                        st.info("These fields are recommended based on the data hierarchy and target field.")
                        
                        # Let user select fields
                        selected_fields = st.multiselect(
                            "Select fields to generate patterns for:",
                            analysis['recommended_fields'],
                            default=analysis['recommended_fields'][:3]
                        )
                        
                        if selected_fields and st.button("Generate Patterns"):
                            patterns = generate_regex_patterns(
                                selected_fields,
                                analysis['sample_object'],
                                analysis['summary_sample']
                            )
                            
                            st.session_state.generated_patterns = {
                                'fields': selected_fields,
                                'patterns': patterns
                            }
                    else:
                        st.warning("No recommended fields found.")
                
                with tab3:
                    if 'generated_patterns' in st.session_state:
                        patterns_data = st.session_state.generated_patterns
                        
                        st.subheader("Generated Regex Patterns")
                        
                        # Show patterns
                        for i, (field, pattern) in enumerate(zip(patterns_data['fields'], patterns_data['patterns']), 1):
                            st.markdown(f"**Pattern {i}: {field}**")
                            st.code(pattern, language="regex", line_numbers=False)
                            st.markdown("---")
                        
                        # Copy to clipboard
                        all_patterns = "\n".join(patterns_data['patterns'])
                        st.text_area(
                            "All Patterns (copy this):",
                            all_patterns,
                            height=100
                        )
                        
                        # JSON export
                        export_data = {
                            "test_name": "Field Analysis",
                            "important_fields": patterns_data['fields'],
                            "reasoning": "Fields identified using hierarchical analysis prioritizing summary/aggregate fields",
                            "generated_regex": patterns_data['patterns']
                        }
                        
                        st.download_button(
                            label="📥 Download as JSON",
                            data=json.dumps(export_data, indent=2),
                            file_name="analysis_result.json",
                            mime="application/json"
                        )
                    else:
                        st.info("👆 Go to 'Field Recommendations' tab to select fields and generate patterns.")
                
                with tab4:
                    st.subheader("Raw Data Structure")
                    
                    # Full data viewer
                    st.json(data)
                    
                    # Download raw data
                    st.download_button(
                        label="📥 Download Raw Data",
                        data=json.dumps(data, indent=2),
                        file_name="raw_data.json",
                        mime="application/json"
                    )
        
        except json.JSONDecodeError as e:
            st.error(f"❌ Invalid JSON file: {e}")
        except Exception as e:
            st.error(f"❌ Error processing file: {e}")
    
    else:
        # Show example when no file uploaded
        st.info("👆 Please upload a JSON file to begin analysis")
        
        with st.expander("📖 How to use"):
            st.markdown("""
            **Steps:**
            1. Upload a JSON file with structured data
            2. Set the target field you want to analyze (e.g., `rotation_enabled`)
            3. Click "Analyze" to process the data
            4. Review the structure analysis and field recommendations
            5. Select fields and generate regex patterns
            6. Download the results as JSON
            
            **What this tool does:**
            - Detects summary/aggregate fields automatically
            - Classifies data structure by hierarchy levels
            - Recommends important fields for validation
            - Generates regex patterns for field extraction
            """)
        
        with st.expander("📋 Example JSON Structure"):
            example = {
                "results": {
                    "summary": {
                        "total_keys": 13,
                        "rotated_keys": 6,
                        "rotation_percentage": 46
                    },
                    "kms_keys": {
                        "object": [
                            {
                                "key_id": "12345",
                                "rotation_enabled": True,
                                "key_state": "Enabled"
                            }
                        ]
                    }
                }
            }
            st.json(example)


if __name__ == "__main__":
    main()