Upload files to 'Backend'
update backend
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Backend/Dockerfile
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55
Backend/Dockerfile
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FROM python:3.10-slim-bullseye AS base
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WORKDIR /app
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# Set utf-8 encoding for Python et al
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ENV LANG=C.UTF-8 \
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# Turn off writing .pyc files
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PYTHONDONTWRITEBYTECODE=1 \
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# Reduce the OS system calls for this tool it makes a difference
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PYTHONUNBUFFERED=1 \
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# Disables cache dir in pip
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PIP_NO_CACHE_DIR=1 \
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# Virtual environment
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VENV="/opt/venv" \
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# Add new user
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APPUSER=appuser \
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# Ensure that the python and pip executables used in the image
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PATH="${VENV}/bin:$PATH"
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FROM base as builder
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COPY requirements.txt .
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RUN apt-get update \
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&& apt-get install -y git build-essential
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RUN python -m venv ${VENV} \
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&& . ${VENV}/bin/activate \
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&& pip install --upgrade pip \
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&& pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu \
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&& pip install -r requirements.txt
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FROM base as runner
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COPY api.py .
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COPY --from=builder ${VENV} ${VENV}
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ENV PATH="${VENV}/bin:$PATH"
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# Update permissions & change user to not run as root
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RUN chgrp -R 0 /app \
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&& chmod -R g=u /app \
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&& groupadd -r ${APPUSER} \
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&& useradd -r -g ${APPUSER} ${APPUSER} \
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&& chown -R ${APPUSER}:${APPUSER} /app \
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&& usermod -d /app ${APPUSER}
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CMD ["/opt/venv/bin/uvicorn", "api:app", "--host", "0.0.0.0"]
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Backend/api.py
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Backend/api.py
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import torch
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import uvicorn
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from fastapi import FastAPI
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from pydantic import BaseModel
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from transformers import MT5Tokenizer,AutoTokenizer, AutoModel ,T5ForConditionalGeneration
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import warnings
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import json
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import random
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import torch.nn.functional as F
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import os
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from dotenv import load_dotenv
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#from ece import compute_ECE
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from torch.utils.data import DataLoader
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from functools import reduce
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warnings.filterwarnings("ignore")
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DEVICE ='cpu'
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load_dotenv()
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host = os.getenv("HOST")
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port = os.getenv("PORT")
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model_dir = os.getenv("QA_MODEL")
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#model_dir = "C:/Users/david/Desktop/T5_JUPYTER/qa_model"
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tokenizer_dir = os.getenv("QA_TOKENIZER")
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#tokenizer_dir = "C:/Users/david/Desktop/T5_JUPYTER/qa_tokenizer"
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MODEL = T5ForConditionalGeneration.from_pretrained(model_dir, from_tf=False, return_dict=True).to(DEVICE)
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print("Model succesfully loaded!")
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TOKENIZER = AutoTokenizer.from_pretrained(tokenizer_dir, use_fast=True)
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print("Tokenizer succesfully loaded!")
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Q_LEN = 512
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TOKENIZER.add_tokens('<sep>')
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print('model loaded')
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app = FastAPI()
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# BASE MODEL
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class InputData(BaseModel):
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context: str
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question: str
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@app.post("/predict")
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async def predict(input_data: InputData):
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inputs = TOKENIZER(input_data.question, input_data.context, max_length=512, padding="max_length", truncation=True, add_special_tokens=True)
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input_ids = torch.tensor(inputs["input_ids"], dtype=torch.long).to(DEVICE).unsqueeze(0)
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attention_mask = torch.tensor(inputs["attention_mask"], dtype=torch.long).to(DEVICE).unsqueeze(0)
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outputs = MODEL.generate(input_ids=input_ids, attention_mask=attention_mask, return_dict_in_generate=True,output_scores=True,max_length=512)
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predicted_ids = outputs.sequences.numpy()
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predicted_text = TOKENIZER.decode(predicted_ids[0], skip_special_tokens=True)
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return {'prediction':predicted_text}
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if __name__ == "__main__":
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uvicorn.run(app, host=host, port=port)
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5
Backend/requirements.txt
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5
Backend/requirements.txt
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uvicorn==0.23.2
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fastapi==0.103.2
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transformers==4.34.0
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rank_bm25==0.2.2
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python-dotenv
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