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from pydantic import BaseModel, validator |
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from peft import PeftModel, PeftConfig |
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from transformers import T5ForConditionalGeneration, AutoTokenizer |
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from fastapi import FastAPI, Request |
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from fastapi.middleware.cors import CORSMiddleware |
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app = FastAPI() |
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origins = ["*"] |
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app.add_middleware( |
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CORSMiddleware, |
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allow_origins=origins, |
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allow_credentials=True, |
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allow_methods=["*"], |
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allow_headers=["*"] |
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) |
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peft_model_id = "deutsche-welle/t5_large_peft_wnc_debiaser" |
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config = PeftConfig.from_pretrained(peft_model_id) |
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model = T5ForConditionalGeneration.from_pretrained(config.base_model_name_or_path) |
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tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path) |
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model = PeftModel.from_pretrained(model, peft_model_id) |
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model.eval() |
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def prepare_input(sentence: str): |
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input_ids = tokenizer(sentence, max_length=256, return_tensors="pt").input_ids |
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return input_ids |
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def inference(sentence: str) -> str: |
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input_data = prepare_input(sentence=sentence) |
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input_data = input_data.to(model.device) |
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outputs = model.generate(inputs=input_data, max_length=256) |
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result = tokenizer.decode(token_ids=outputs[0], skip_special_tokens=True) |
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return result |
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class Response(BaseModel): |
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generated_text: str |
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@app.get("/debias", response_model=Response) |
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def predict_subjectivity(sentence: str): |
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result = inference(f"debias: {sentence} </s>") |
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return {"generated_text": result} |
