from fastapi import FastAPI, Request, Form
from fastapi.responses import HTMLResponse
from transformers import GPT2LMHeadModel, GPT2Config
import torch
app = FastAPI()
quantized_model_path = "gpt3_mini_quantized_2x_16bits.pth"
config = GPT2Config.from_pretrained("Deniskin/gpt3_medium") # Load the config to initialize the model architecture
quantized_model = GPT2LMHeadModel(config=config) # Initialize the model
# Set the model to evaluation mode
model.eval()
# Function to generate text using the model
def generate_text(prompt):
input_ids = tokenizer.encode(prompt, return_tensors="pt")
output = model.generate(input_ids, max_length=50, num_return_sequences=1)
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
return generated_text
@app.get("/", response_class=HTMLResponse)
async def home(request: Request):
html_content = """
LSTM Text Generation
LSTM Text Generation
"""
return HTMLResponse(content=html_content, status_code=200)
@app.post("/generate")
async def generate(request: Request, input_text: str = Form(...)):
generated_text = generate_text(input_text)
return {"generated_text": generated_text}
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=8000)