main.py

from fastapi import FastAPI
from fastapi.staticfiles import StaticFiles
from fastapi.responses import FileResponse
from transformers import AutoTokenizer, AutoModel
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
from pydantic import BaseModel

# load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained('allenai/specter')
model = AutoModel.from_pretrained('allenai/specter')

# papers = [{'title': 'BERT', 'abstract': 'We introduce a new language representation model called BERT'},
#           {'title': 'Attention is all you need', 'abstract': ' The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'}]

# concatenate title and abstract


class Input(BaseModel):
    papers: list = []


app = FastAPI()



@app.post('/similarity')
def similarity(input: Input):
    papers = input.papers
    title_abs = [d['title'] + tokenizer.sep_token + (d.get('abstract') or '') for d in papers]
    # preprocess the input
    inputs = tokenizer(title_abs, padding=True, truncation=True, return_tensors="pt", max_length=512)
    result = model(**inputs)
    # take the first token in the batch as the embedding
    embeddings = result.last_hidden_state[:, 0, :].detach().numpy()
    res = cosine_similarity(embeddings, embeddings).tolist()
    return {"output": res}


app.mount("/", StaticFiles(directory="static", html=True), name="static")

@app.get("/")
def index() -> FileResponse:
    return FileResponse(path="/app/static/index.html", media_type="text/html")