main.py

from fastapi.staticfiles import StaticFiles
from fastapi.responses import FileResponse
from pydantic import BaseModel
from fastapi import FastAPI

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_name = "facebook/blenderbot-1B-distill"

# https://huggingface.co/models?sort=trending&search=facebook%2Fblenderbot
# facebook/blenderbot-3B
# facebook/blenderbot-1B-distill
# facebook/blenderbot-400M-distill
# facebook/blenderbot-90M
# facebook/blenderbot_small-90M

# https://www.youtube.com/watch?v=irjYqV6EebU

app = FastAPI()
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

class req(BaseModel):
  prompt: str
  history: list

@app.get("/")
def read_root():
  return FileResponse(path="templates/index.html", media_type="text/html")

@app.post("/api")
def read_root(data: req):
  print("Prompt:", data.prompt)
  print("History:", data.history)

  history_string = "\n".join(data.history)
  
  input_text = data.prompt
  
  # Tokenize the input text
  input_ids = tokenizer.encode_plus(history_string, input_text, return_tensors="pt")
  
  # Generate output using the model
  output_ids = model.generate(input_ids, num_beams=5, no_repeat_ngram_size=2)
  generated_text = tokenizer.decode(output_ids[0], skip_special_tokens=True).strip()
  
  answer_data = { "answer": generated_text }
  print("Answer:", generated_text)
  
  return answer_data