fastapi

Dockerfile

@@ -1,15 +1,28 @@
-# read the doc: https://huggingface.co/docs/hub/spaces-sdks-docker
-# you will also find guides on how best to write your Dockerfile
-
+# Use the official Python 3.9 image
 FROM python:3.9
-
+ 
+# Set the working directory to /code
 WORKDIR /code
-
+ 
+# Copy the current directory contents into the container at /code
 COPY ./requirements.txt /code/requirements.txt
-
+ 
+# Install requirements.txt 
 RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
-
-COPY . .
-
-CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "7860"]
-
+ 
+# Set up a new user named "user" with user ID 1000
+RUN useradd -m -u 1000 user
+# Switch to the "user" user
+USER user
+# Set home to the user's home directory
+ENV HOME=/home/user \\
+    PATH=/home/user/.local/bin:$PATH
+ 
+# Set the working directory to the user's home directory
+WORKDIR $HOME/app
+ 
+# Copy the current directory contents into the container at $HOME/app setting the owner to the user
+COPY --chown=user . $HOME/app
+ 
+# Start the FastAPI app on port 7860, the default port expected by Spaces
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

main.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.*