'1

Dockerfile

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+# Use an official Python runtime as a parent image
+FROM python:3.9-slim
+
+# Set environment variables
+ENV PYTHONDONTWRITEBYTECODE=1 \
+    PYTHONUNBUFFERED=1
+
+# Create a non-root user
+RUN useradd --create-home user
+# Set the working directory to the user's home directory
+WORKDIR /home/user
+
+# Copy and install dependencies
+COPY --chown=user:user requirements.txt ./
+RUN pip install --no-cache-dir --upgrade pip && \
+    pip install --no-cache-dir -r requirements.txt
+
+# Copy the application code
+COPY --chown=user:user app.py ./
+COPY --chown=user:user rf_model.pkl ./
+
+# Switch to the non-root user
+USER user
+
+# Set environment variables for the application
+ENV HOME=/home/user \
+    PATH=/home/user/.local/bin:$PATH
+
+# Expose the port uvicorn will run on
+EXPOSE 8501
+
+# Define the default command to run the application
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "8501"]

app.py

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+import json
+import numpy as np
+import joblib
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+import uvicorn
+
+# Load model from the local storage (ensure the model file is in the same directory)
+model_path = "rf_model.pkl"
+gb_model_loaded = joblib.load(model_path)
+
+# Create FastAPI app
+app = FastAPI()
+
+# Define class labels
+class_names = [
+    'Emergency & Accident Unit', 'Heart Clinic',
+    'Neuro Med Center', 'OPD:EYE', 'Dental',
+    'OPD:MED', 'OPD:ENT', 'OPD:OBG',
+    'OPD:Surgery + Uro.', 'Orthopedic Surgery',
+    'GI Clinic', 'Breast Clinic', 'Skin & Dermatology'
+]
+
+# Define the input format for FastAPI using Pydantic BaseModel
+class InputData(BaseModel):
+    features: list[float]  # List of 32 feature inputs
+
+@app.post("/predict")
+def predict(data: InputData):
+    try:
+        # Validate input length
+        if len(data.features) != 32:
+            raise HTTPException(status_code=400, detail=f"Expected 32 features, but got {len(data.features)}")
+
+        # Convert list to numpy array and reshape
+        input_array = np.array(data.features).reshape(1, -1)
+
+        # Get predictions
+        prediction = gb_model_loaded.predict_proba(input_array)
+
+        # Convert probabilities to percentage and format
+        probabilities = (prediction[0] * 100).round(2)
+        result_pro = {class_name: f"{prob:.2f}%" for class_name, prob in zip(class_names, probabilities)}
+
+        # Return result as JSON
+        return {'result': result_pro}
+
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+# Run the application with the following command if needed
+# if __name__ == "__main__":
+#     uvicorn.run(app, host="0.0.0.0", port=8501)

model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:eefc4721e5f6d31845e925a604e34ca3e42107762c8d8ea7ffb68e5c5f3fb108
+size 173541345

requirements.txt

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+fastapi
+uvicorn
+numpy
+joblib
+pydantic
+requests