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Dockerfile

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+from python:3.8-slim
+
+WORKDIR /app
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+
+CMD ["uvicorn","api:app","--host","0.0.0.0","--port","8000"]

api.py

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+from fastapi import FastAPI, Request
+from pydantic import BaseModel
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
+import torch
+
+app = FastAPI()
+
+model_path = './fine-tuned-gpt2'
+model = GPT2LMHeadModel.from_pretrained(model_path)
+tokenizer = GPT2Tokenizer.from_pretrained(model_path)
+
+if tokenizer.pad_token is None:
+    tokenizer.add_special_tokens({'pad_token':'[PAD]'})
+    model.resize_token_embeddings(len(tokenizer))
+
+class TextRequest(BaseModel):
+    prompt: str
+
+def generate_response(prompt, max_length=100):
+    input_ids = tokenizer.encode(prompt, return_tensors='pt')
+    output = model.generate(input_ids, max_length=max_length,pad_token_id = tokenizer.eos_token_id)
+    response = tokenizer.decode(output[0], skip_special_tokens=True)
+    return response
+
+@app.post("/generate")
+async def generate(request: TextRequest):
+    response = generate_response(request.prompt)
+    return {"response":response}

requirements.txt

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+fastapi
+transformers
+torch
+uvicorn