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Dockerfile

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+# use the official python 3.9 image
+FROM python:3.9
+
+# set the working directory in the container
+WORKDIR /code
+
+# copy the current directory contents in the container at /code
+COPY ./requirements.txt /code/requirements.txt
+
+# install dependencies
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+# set up a new user named "user"
+RUN useradd user
+
+# change the ownership of the /code directory to the 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
+COPY --chown=user . $HOME/app
+
+## Start the FASTAPI App on port 7860
+CMD ["uvicorn", "app:app", "--host", "8.0.0.0", "--port", "7860"]

app.py

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+from fastapi import FastAPI
+from transformers import pipeline #To call LLM models present in huggingface
+
+# create a new FASTAPI app instance
+app = FastAPI()
+
+# initialize the text generation pipeline
+pipe = pipeline("text2text-generation", model="google/flan-t5-small")
+
+
+@app.get("/")
+
+def home():
+    return {"message":"Hello World"}
+
+
+# Define a function to handle the GET request at "/generate"
+
+@app.get("/generate")
+
+def generate(text:str):
+
+    # use the pipeline to generate text from given input text
+
+    output = pipe(text)
+
+    # Return the generated text in Json format
+
+    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.*