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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)
history_string = "\n".join(data.history)
input_text = data.prompt
# Tokenize the input text
input_ids = tokenizer.encode(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 |