init

Dockerfile

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+FROM python:3.9
+WORKDIR /code
+COPY ./requirements.txt /code/requirements.txt
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+RUN useradd -m -u 1000 user
+USER user
+ENV HOME=/home/user \\
+    PATH=/home/user/.local/bin:$PATH
+WORKDIR $HOME/app
+COPY --chown=user . $HOME/app
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]
+
+

app.py

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+
+from fastapi import FastAPI
+from transformers import pipeline
+ 
+ # Create a new FastAPI app instance
+ app = FastAPI()
+  
+  # Initialize the text generation pipeline
+  # This function will be able to generate text
+  # given an input.
+  pipe = pipeline("text2text-generation", 
+          model="google/flan-t5-small")
+   
+  # Define a function to handle the GET request at `/generate`
+  # The generate() function is defined as a FastAPI route that takes a 
+  # string parameter called text. The function generates text based on the # input using the pipeline() object, and returns a JSON response 
+  # containing the generated text under the key "output"
+  @app.get("/generate")
+  def generate(text: str):
+          """
+              Using the text2text-generation pipeline from `transformers`, generate text
+                  from the given input text. The model used is `google/flan-t5-small`, which
+                      can be found [here](<https://huggingface.co/google/flan-t5-small>).
+                          """
+                              # Use the pipeline to generate text from the given input text
+                                  output = pipe(text)
+                                       
+                                           # Return the generated text in a JSON response
+                                               return {"output": output[0]["generated_text"]}

requirements.txt

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+fastapi==0.74.*
+requests==2.27.*
+uvicorn[standard]==0.17.*
+sentencepiece==0.1.*
+torch==1.11.*
+transformers==4.*