initial commit

Dockerfile

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+# 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
+ 
+# 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"]
+
+CMD gunicorn -k uvicorn.workers.UvicornWorker --workers 2 --threads=2 --max-requests 512 --bind 0.0.0.0:7860 app:app

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.
+summarizer = pipeline("summarization", model="philschmid/bart-large-cnn-samsum")
+ 
+# 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 = summarizer(conversation)
+     
+    # Return the generated text in a JSON response
+    return {"output": output[0]["summary_text"]}

requirements.txt

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+fastapi==0.74.*
+requests==2.27.*
+# uvicorn[standard]==0.17.*
+uvloop==0.15.2
+uvicorn==0.13.4
+httptools==0.2.0
+sentencepiece==0.1.*
+torch==1.11.*
+transformers==4.*
+gunicorn==20.1.0