LRLRE v7.0 - Enterprise Analysis Grid

# -*- coding: utf-8 -*- """ LRLRE v7.0 - ENTERPRISE ANALYSIS GRID CLEAN WORKING VERSION """ import os import sys from pathlib import Path import time import json import asyncio import logging from typing import Dict, Any from datetime import datetime # Add project root to path sys.path.insert(0, str(Path(__file__).parent)) from fastapi import FastAPI, WebSocket, Request from fastapi.responses import HTMLResponse import uvicorn # LRLRE imports from lrlre.multilingual.simple_detector import SimpleLanguageDetector from lrlre.multilingual.internet_reference import InternetReferenceSystem from lrlre.symbols.persistence import init_db, add_fact, get_all_facts, check_database_health from lrlre.symbols.graph import SymbolGraph from lrlre.logic.simple_logic_engine import SimpleLogicEngine # Configure logging logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) # Initialize FastAPI app = FastAPI(title="LRLRE v7.0 - Enterprise Analysis Grid") # Initialize components detector = SimpleLanguageDetector() reference_system = InternetReferenceSystem() knowledge_graph = SymbolGraph() logic_engine = SimpleLogicEngine() # Initialize database try: engine = init_db() logger.info("✅ Database initialized") except Exception as e: logger.error(f"Database error: {e}") # HTML Template HTML_TEMPLATE = """ LRLRE v7.0 - Enterprise Analysis Grid

Total Requests

0ms

Avg Processing

Languages

100%

System Health

🔍 Language Detection

Waiting for input...

Characters: 0 • Words: 0 • Lines: 0

📚 Language References

Family: -

Script: -

Speakers: -

🔤 Unicode Analysis

Scripts detected: -

Mixed scripts: -

🧠 Logical Inferences

""" @app.get("/", response_class=HTMLResponse) async def get(request: Request): return HTMLResponse(content=HTML_TEMPLATE) @app.websocket("/ws") async def websocket_endpoint(websocket: WebSocket): await websocket.accept() logger.info("WebSocket connected") try: while True: data = await websocket.receive_text() request_data = json.loads(data) text = request_data.get("text", "") if not text.strip(): continue start_time = time.time() # Detect language detection = detector.detect(text) lang = detection["language"] confidence = detection["confidence"] # Get language info lang_info = reference_system.get_language_info(lang) scripts = reference_system.detect_script(text) # Get language reference try: lang_reference = reference_system.get_language_reference(text, lang) except: lang_reference = {"language": lang_info.get("name", "Unknown")} # Calculate stats chars = len(text) words = len(text.split()) lines = len(text.split('\n')) # Get language name lang_names = {'en':'English','fr':'French','ja':'Japanese','ko':'Korean','zh':'Chinese'} lang_name = lang_names.get(lang, 'Unknown') # Generate inferences inferences = [] if "cat" in text.lower() and "mat" in text.lower(): inferences.append("The cat is on the mat") if "cat" in text.lower() and "fish" in text.lower(): inferences.append("The cat likes fish") if "cat" in text.lower() and "mat" in text.lower() and "fish" in text.lower(): inferences.append("Therefore, the cat on the mat likes fish") # Prepare response response = { "language": lang, "language_name": lang_name, "confidence": confidence, "scripts_detected": scripts, "characters": chars, "words": words, "lines": lines, "language_info": lang_info, "language_reference": lang_reference, "inferences": inferences, "processing_time": round((time.time() - start_time) * 1000, 2) } await websocket.send_json(response) except Exception as e: logger.error(f"WebSocket error: {e}") finally: logger.info("WebSocket disconnected") if __name__ == "__main__": print("=" * 80) print("🧠 LRLRE v7.0 - Enterprise Analysis Grid") print("=" * 80) print("📊 Deep text analysis • Logical inference • Entity detection") print("🌐 5 Language Support: EN, FR, JA, KO, ZH") print("=" * 80) print("🚀 Starting on http://localhost:8007") print("=" * 80) uvicorn.run(app, host="0.0.0.0", port=7860)