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import logging
import os
from fastapi import FastAPI, Request
from contextlib import asynccontextmanager
from transformers import pipeline
from langdetect import detect
from huggingface_hub import login
import socket
# Global variables
HF_HUB_TOKEN = os.getenv("HUGGINGFACEHUB_API_TOKEN")
# Verify Hugging Face token
if not HF_HUB_TOKEN:
raise ValueError("Missing Hugging Face API token. Please set HUGGINGFACEHUB_API_TOKEN in environment variables.")
login(token=HF_HUB_TOKEN)
# Load Hebrew and English text generation models
hebrew_generator = pipeline("text-generation", model="Norod78/hebrew-gpt_neo-small")
english_generator = pipeline("text-generation", model="distilgpt2")
# Function to detect language
def detect_language(user_input):
try:
lang = detect(user_input)
return "hebrew" if lang == "he" else "english" if lang == "en" else "unsupported"
except:
return "unsupported"
# Function to generate a response
def generate_response(text):
language = detect_language(text)
if language == "hebrew":
return hebrew_generator(text, max_length=100)[0]["generated_text"]
elif language == "english":
return english_generator(text, max_length=100)[0]["generated_text"]
return "Sorry, I only support Hebrew and English."
# FastAPI lifespan event
@asynccontextmanager
async def lifespan(app: FastAPI):
print("Starting application...")
yield # Wait until app closes
print("Shutting down application...")
# Create FastAPI app
app = FastAPI(lifespan=lifespan)
@app.get("/")
async def root():
return {"message": "Decision Helper API is running!"}
@app.post("/generate_response")
async def generate_text(request: Request):
data = await request.json()
text = data.get("text", "")
if not text:
return {"error": "No text provided"}
response = generate_response(text)
return {"response": response}
# Run the server
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=7860)
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