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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 requirements.txt into the container
+COPY ./requirements.txt /code/requirements.txt
+
+# Install dependencies
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+# Switch to a new user: 'user'
+RUN useradd -m user
+USER user
+
+# Set home and cache directories for the 'user'
+ENV HOME=/home/user \
+    PATH=/home/user/.local/bin/:$PATH \
+    TRANSFORMERS_CACHE=/home/user/.cache/huggingface
+
+# Set working directory to the user's home directory
+WORKDIR $HOME/app
+
+# Copy current directory into the container and set ownership to 'user'
+COPY --chown=user . $HOME/app
+
+# Start the FastAPI 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
+
+# Creating a new FastAPI app instance
+app = FastAPI()
+
+# Calling the Hugging Face Model from the pipeline
+# Here I am using the Facebook/bart-large-cnn model for summarization
+summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+
+# Creating routes
+@app.get("/")
+def home():
+    return {"message": "Your FastAPI and Model is Running"}
+
+@app.get("/chat")
+def chat(text: str):
+    # Use the summarizer pipeline to generate a summary from the given input text
+    output = summarizer(text, max_length=130, min_length=30, do_sample=False)
+    # Return the summary from the output
+    return {"summary": output[0]['summary_text']}