import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import logging
import re
import json
import gzip
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel, validator
import uvicorn
import requests
import time
import threading

Set up logging

logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')

Create a FastAPI application

app = FastAPI()

class DataFormat:
"""DSL for encoding/decoding data for efficient transmission."""
def init(self, name: str, fields: list):
self.name = name
self.fields = fields

def encode(self, data: dict) -> bytes:
    """Encode data as compressed JSON."""
    try:
        json_data = json.dumps({k: data[k] for k in self.fields if k in data})
        return gzip.compress(json_data.encode('utf-8'))
    except Exception as e:
        logging.error(f"Encoding error: {str(e)}")
        raise ValueError(f"Failed to encode data: {str(e)}")

def decode(self, encoded_data: bytes) -> dict:
    """Decode compressed JSON data."""
    try:
        return json.loads(gzip.decompress(encoded_data).decode('utf-8'))
    except Exception as e:
        logging.error(f"Decoding error: {str(e)}")
        raise ValueError(f"Failed to decode data: {str(e)}")

class MultilingualGenerator:
"""Class to generate and transmit text in multiple languages."""
def init(self, model_name: str = "google/mt5-small"):
"""Initialize with a multilingual Seq2Seq model."""
self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
self.data_format = DataFormat("TextGeneration", ["prompt", "language", "response"])
try:
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
self.model = AutoModelForSeq2SeqLM.from_pretrained(model_name).to(self.device)
except Exception as e:
logging.error(f"Failed to load model or tokenizer: {str(e)}")
raise ValueError(f"Failed to load model or tokenizer: {str(e)}")

def preprocess_text(self, text: str, language: str) -> str:
    """Apply language-specific preprocessing."""
    try:
        if language == "Persian":
            text = re.sub(r'[\u0643]', '\u06A9', text)  # Arabic kaf to Persian kaf
            text = re.sub(r'[\u064A]', '\u06CC', text)  # Arabic yeh to Persian yeh
            text = re.sub(r'\s+','', text.strip())
        elif language == "Hebrew":
            text = re.sub(r'\s+','', text.strip())
        elif language == "Arabic":
            text = re.sub(r'[\u0622\u0623\u0625]', '\u0627', text)  # Unify alef
            text = re.sub(r'\s+','', text.strip())
        elif language == "English":
            text = text.strip()
        elif language == "Turkish":
            text = re.sub(r'\s+','', text.strip())
        return text
    except Exception as e:
        logging.error(f"Preprocessing error for {language}: {str(e)}")
        return text

def generate_text(self, prompt: str, language: str, max_new_tokens: int = 100) -> dict:
    """Generate text for a given prompt and language."""
    if not prompt:
        raise ValueError("Prompt cannot be empty")
    prompt = self.preprocess_text(prompt, language)
    try:
        inputs = self.tokenizer(
            f"{language}: {prompt}",
            return_tensors="pt",
            padding=True,
            truncation=True,
            max_length=512
        ).to(self.device)
        with torch.no_grad():
            outputs = self.model.generate(
                **inputs,
                max_new_tokens=max_new_tokens,
                num_beams=5,
                early_stopping=True,
                temperature=0.7,
                no_repeat_ngram_size=2
            )
        response = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
        return {"prompt": prompt, "language": language, "response": response}
    except RuntimeError as e:
        logging.error(f"Model inference error for {language}: {str(e)}")
        return {"prompt": prompt, "language": language, "response": f"Error: {str(e)}"}
    except Exception as e:
        logging.error(f"Unexpected error generating text for {language}: {str(e)}")
        return {"prompt": prompt, "language": language, "response": f"Error: {str(e)}"}

def transmit_response(self