Generate example app from HF staff

Dockerfile

@@ -1,28 +1,27 @@
 # 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
- 
+
 # 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:$PATH
- 
+ENV HOME=/home/user \
+	PATH=/home/user/.local/bin:$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", "main:app", "--host", "0.0.0.0", "--port", "7860"]
+
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

@@ -0,0 +1,28 @@
+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"]}