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dockerfile

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+# Use an official Python runtime as a parent image
+FROM python:3.10-slim
+
+# Set the working directory in the container
+WORKDIR /sentiment
+
+# Copy the requirements.txt file into the root
+COPY requirements.txt .
+
+# Copy the current directory contents into the container at /app as well
+COPY ./app ./app
+
+# Install any needed packages specified in requirements.txt
+RUN pip install -r requirements.txt
+
+# Make port 8000 available to the world outside this container
+EXPOSE 8000
+
+# Run main.py when the container launches, as it is contained under the app folder, we define app.main
+CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000"]

main.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+from transformers import pipeline
+
+# You can check any other model in the Hugging Face Hub. In my case I chose this one to classify text by positive and negative sentiment. 
+pipe = pipeline(model="distilbert/distilbert-base-uncased-finetuned-sst-2-english")
+
+# We define the app
+app = FastAPI()
+
+# We define that we expect our input to be a string
+class RequestModel(BaseModel):
+    input: str
+
+# Now we define that we accept post requests
+# ->  In APIs, requests are made to ask the API to perform a certain task — in this case to analyze a piece of text. 
+@app.post("/sentiment")
+def get_response(request: RequestModel):
+    # We get the input prompt
+    prompt = request.input
+
+    # We use the hf model to classify the prompt
+    response = pipe(prompt)
+
+    # We get both the label and the score from the input
+    label = response[0]["label"]
+    score = response[0]["score"]
+    return f"The '{prompt}' input is {label} with a score of {score}"

requirements.txt

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+fastapi
+pydantic
+transformers
+uvicorn
+requests
+torch