app.py

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
from huggingface_hub import login
import os
import torch
import logging

# Read the Hugging Face token from the environment variable
token = os.getenv("HUGGING_FACE_HUB_TOKEN")
if token is None:
    raise ValueError("Hugging Face token not found in environment variables. Please set the HUGGING_FACE_HUB_TOKEN secret in Hugging Face Spaces.")

# Log in with the token
login(token=token)

# Initialize FastAPI app
app = FastAPI()

# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Load a smaller model (GPT-Neo-125M) and tokenizer
model_id = "EleutherAI/gpt-neo-125M"  # Use GPT-Neo-125M for faster performance
tokenizer = AutoTokenizer.from_pretrained(model_id)

# Load the model without quantization for CPU
logger.info("Loading model...")
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float32,  # Use FP32 for CPU compatibility
    device_map="auto"           # Automatically offload to available devices
)

# Create a text generation pipeline
pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer
)

# Define request body schema
class TextGenerationRequest(BaseModel):
    prompt: str
    max_new_tokens: int = 50  # Reduce this for faster responses
    temperature: float = 0.7  # Lower for more deterministic outputs
    top_k: int = 50
    top_p: float = 0.9
    do_sample: bool = True

# Define API endpoint
@app.post("/generate-text")
async def generate_text(request: TextGenerationRequest):
    try:
        logger.info("Generating text...")
        
        # Generate text using the pipeline with the user's prompt
        outputs = pipe(
            request.prompt,  # Use the user's prompt directly
            max_new_tokens=request.max_new_tokens,
            temperature=request.temperature,
            top_k=request.top_k,
            top_p=request.top_p,
            do_sample=request.do_sample,
            return_full_text=False  # Exclude the input prompt from the output
        )
        return {"generated_text": outputs[0]["generated_text"]}
    except Exception as e:
        logger.error(f"Error generating text: {e}")
        raise HTTPException(status_code=500, detail=str(e))

# Add a root endpoint for health checks
@app.get("/test")
async def root():
    return {"message": "API is running!"}