initial app

.dockerignore

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+.git
+__pycache__
+venv
+.env*
+!.env.example

.env.example

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+API_KEY=

.gitignore

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+.DS_Store
+venv
+.env*
+!.env.example
+__pycache__

Dockerfile

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+FROM python:3.12-slim
+
+RUN useradd -m -u 1000 user
+USER user
+ENV PATH="/home/user/.local/bin:$PATH"
+
+WORKDIR /app
+
+COPY --chown=user ./requirements.txt requirements.txt
+RUN pip install --no-cache-dir --upgrade -r /app/requirements.txt
+
+COPY --chown=user . /app
+
+CMD [ "uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860", "--no-server-header" ]

Sentiment/__init__.py

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+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+
+app = FastAPI(title="Analyze sentiment of text")
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+from Sentiment import router

Sentiment/model.py

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+from functools import lru_cache
+from transformers import BertTokenizer, BertConfig, BertForSequenceClassification
+
+@lru_cache()
+def initialize_model():
+  tokenizer = BertTokenizer.from_pretrained('indobenchmark/indobert-base-p1')
+  config = BertConfig.from_pretrained('daphinokio/indobert-smsa-fine-tuned')
+  model = BertForSequenceClassification.from_pretrained('daphinokio/indobert-smsa-fine-tuned', config=config)
+  return model, tokenizer
+
+model, tokenizer = initialize_model()

Sentiment/router.py

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+import os
+import torch
+import torch.nn.functional as F
+from transformers import BertTokenizer, BertConfig, BertForSequenceClassification
+from fastapi import FastAPI, Depends, HTTPException, status
+from fastapi.security import HTTPBasic, HTTPBasicCredentials
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+from typing_extensions import Annotated
+from Sentiment import app
+from .model import model, tokenizer
+
+app_security = HTTPBasic()
+api_key = os.getenv("API_KEY")
+
+if not api_key:
+  raise ValueError("API_KEY is missing.")
+
+i2l = {0: 'positive', 1: 'neutral', 2: 'negative'}
+
+class AnalyzeRequest(BaseModel):
+  text: str
+
+class AnalyzeResponse(BaseModel):
+  text: str
+  label: str
+  score: float
+
+@app.get("/checkhealth", tags=["CheckHealth"])
+def checkhealth():
+  return  "Sentiment API is running."
+
+@app.post("/predict", tags=["Analyze"], summary="Analyze text from prompt", response_model=AnalyzeResponse)
+def predict(creds: Annotated[HTTPBasicCredentials, Depends(app_security)], data: AnalyzeRequest):
+  if creds.password != api_key:
+    print(creds.password, api_key)
+    raise HTTPException(
+      status_code=status.HTTP_401_UNAUTHORIZED,
+      detail="Incorrect Password",
+      headers={"WWW-Authenticate": "Basic"}
+    )
+
+  text = data.text
+  test_sample = tokenizer.encode(text)
+  test_sample = torch.LongTensor(test_sample).view(1, -1).to(model.device)
+  logits = model(test_sample)[0]
+  label_index = torch.topk(logits, k=1, dim=-1)[1].squeeze().item()
+  label = i2l[label_index]
+  score = f'{F.softmax(logits, dim=-1).squeeze()[label_index]:.3f}'
+  return {"text":text, "label": label, "score": score}

app.py

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+from Sentiment import app

docker-compose.yml

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+services:
+  server:
+    container_name: server
+    build:
+      context: .
+      dockerfile: Dockerfile
+    env_file: .env
+    ports:
+      - 7860:7860

requirements.txt

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+transformers
+torch
+uvicorn[standard]
+fastapi
+numpy