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Dockerfile

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+# read the doc: https://huggingface.co/docs/hub/spaces-sdks-docker
+# you will also find guides on how best to write your Dockerfile
+
+FROM python:3.9.7
+
+WORKDIR /code
+
+COPY ./requirements.txt /code/requirements.txt
+
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+COPY . .
+
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
+from dotenv import load_dotenv
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+import os
+
+
+load_dotenv()
+
+
+os.environ["HF_TOKEN"] = os.getenv('HF_TOKEN')
+
+
+app = FastAPI()
+
+
+origins = ["*"]
+
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=origins,
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+
+class LanguageTextModel(BaseModel):
+    languageText: str
+    sourceLanguageCode: str
+    targetLanguageCode: str
+
+
+@app.get('/')
+def welcome():
+    return {
+        'success': True,
+        'message': 'server of "nllb language translator" is up and running successfully '
+    }
+
+
+@app.post('/translate')
+async def translate_text(input: LanguageTextModel):
+    
+    model = AutoModelForSeq2SeqLM.from_pretrained("facebook/nllb-200-distilled-600M")
+
+    tokenizer = AutoTokenizer.from_pretrained("facebook/nllb-200-distilled-600M")
+
+    translator = pipeline('translation', model=model, tokenizer=tokenizer, src_lang=input.sourceLanguageCode, tgt_lang=input.targetLanguageCode, max_length=400)
+
+    response = translator('I am not feeling well')
+
+    return {
+        "success": True,
+        "translated_text": response[0]['translation_text']
+    }

requirements.txt

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+fastapi
+pydantic
+python-dotenv
+transformers[torch]