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Dockerfile

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+FROM python:3.9
+
+# Create a new user 'user' with user id 1000
+RUN useradd -m -u 1000 user
+
+# Set the working directory to /app
+WORKDIR /app
+
+# Copy the requirements.txt file into the container at /app/requirements.txt
+COPY --chown=user ./requirements.txt requirements.txt
+
+# Install the Python dependencies from requirements.txt
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+# Install wget to ensure the next RUN command works
+RUN apt-get update && apt-get install -y wget && rm -rf /var/lib/apt/lists/*
+
+# Download the model file using wget
+RUN wget https://huggingface.co/Ahmed007/chest_xray/resolve/main/model.h5 -O model.h5 && chown user:user model.h5
+
+# Copy the current directory into the container at /app
+COPY --chown=user . /app
+
+# Command to run the application
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+from fastapi import FastAPI, File, UploadFile, HTTPException
+from fastapi.responses import JSONResponse
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing import image
+import numpy as np
+import logging
+from PIL import Image
+import io
+
+# Configure logging
+logging.basicConfig(level=logging.DEBUG)
+
+# Initialize FastAPI app
+app = FastAPI()
+
+# Load your trained model
+model = load_model('model.h5')
+class_names = ['Normal', 'bacteria', 'virus']
+
+def preprocess_image(img, target_size):
+    """Resize and preprocess the image for the model."""
+    if img.mode != "RGB":
+        img = img.convert("RGB")
+    img = img.resize(target_size)
+    img_array = image.img_to_array(img)
+    img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
+    return img_array
+
+@app.post("/predict")
+async def predict(file: UploadFile = File(...)):
+    if not file:
+        raise HTTPException(status_code=400, detail="No file provided")
+    try:
+        # Read the file's content into a BytesIO object
+        img_bytes = io.BytesIO(await file.read())
+        
+        # Use PIL to open the image
+        img = Image.open(img_bytes)
+        img_array = preprocess_image(img, (224, 224))
+        
+        # Make prediction
+        predictions = model.predict(img_array)
+
+        predicted_class = np.argmax(predictions, axis=1)
+        
+        # Return the prediction
+        predictions = {
+            'class': class_names[predicted_class[0]],
+            'confidence': float(predictions[0][predicted_class[0]])
+        }
+        return JSONResponse(content=predictions)
+    except Exception as e:
+        logging.debug(f"Error processing the file: {str(e)}")
+        raise HTTPException(status_code=500, detail=f"Error processing the file: {str(e)}")
+        

requirements.txt

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+keras
+tensorflow
+numpy
+pandas
+matplotlib
+flask
+gensim
+huggingface-hub
+gunicorn
+fastapi
+uvicorn[standard]