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Dockerfile

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+## Use the official Python 3.9 image
+FROM python:3.10
+
+## set the working directory to /code
+WORKDIR /code
+
+## Copy the current directory contents in the container at /code
+COPY ./requirements.txt /code/requirements.txt
+
+## Install the requirements.txt
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+# Set up a new user named "user" 
+RUN useradd 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
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+from fastapi import FastAPI, HTTPException
+from transformers import pipeline
+from PIL import Image
+import io
+
+app = FastAPI()
+
+# Load the image classification pipeline
+pipe = pipeline("image-classification", model="mateoluksenberg/dit-base-Classifier_CM05")
+
+# Sample image path (for testing)
+image_path = 'cm5.jpg'
+
+# Async function to classify an image
+async def classify_image(image_path: str):
+    try:
+        image = Image.open(image_path).convert('RGB')
+
+        image_bytes = io.BytesIO()
+        image.save(image_bytes, format='JPEG')
+        image_bytes = image_bytes.getvalue()
+
+        # Perform image classification
+        result = pipe(image_bytes)
+
+        return result[0]  # Return the top prediction
+
+    except Exception as e:
+        # Handle exceptions, for example: file not found, image format issues, etc.
+        raise HTTPException(status_code=500, detail=f"Error processing image: {str(e)}")
+
+@app.get("/")
+async def home(image_path: str = image_path):
+    try:
+        result = await classify_image(image_path)
+        return {"message": "Hello World", "classification_result": result}
+
+    except HTTPException as e:
+        raise e
+
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Error classifying image: {str(e)}")
+