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from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
import torch
import logging
# Initialize FastAPI app
app = FastAPI()
# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# Load the Google Gemma 7B model and tokenizer
model_id = "google/gemma-7b" # Use Google Gemma 7B
tokenizer = AutoTokenizer.from_pretrained(model_id)
# Load the model with 4-bit quantization to reduce VRAM usage
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.float16, # Use half-precision for faster inference
device_map="auto", # Automatically offload to available GPUs
load_in_4bit=True # Enable 4-bit quantization
)
# Create a text generation pipeline
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
device="cuda" if torch.cuda.is_available() else "cpu"
)
# 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!"} |