Add application file

Dockerfile

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+FROM python:3.10
+
+# Set the working directory inside the container
+WORKDIR /app
+
+# Copy the requirements.txt file into our working directory (/app) in the container
+COPY requirements.txt .
+
+# Install dependencies
+RUN pip install --default-timeout=600 -r requirements.txt
+
+
+# Copy the app.py file into our working directory (/app) in the container
+COPY . /app
+
+
+# Make port 80 available to the world outside this container
+EXPOSE 80
+# Command to run when the container starts
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "80", "--reload"]
+
+

RandomForestClassifier_pipeline.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:eb22a9265c6176ff84d7df1d1cf571ff70f3641841a35d63ece52b2e681bc5cc
+size 1226431

encoder.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b7ac692cc8ee984ec176b119b746ee624096f88535f71bca8bcb69736d4f1cd8
+size 542

main.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+import joblib
+import pandas as pd
+
+# Create a FastAPI instance
+app = FastAPI()
+
+# Load the entire pipeline
+sep_pipeline = joblib.load('./RandomForestClassifier_pipeline.joblib')
+encoder = joblib.load('./encoder.joblib')
+
+# Define a FastAPI instance ML model input schema
+class PredictionInput(BaseModel):
+    PRG: int
+    PL: int
+    PR: int
+    SK: int
+    TS: int
+    M11: float
+    BD2: float
+    Age: int
+    Insurance: int
+    
+# Defining the root endpoint for the API
+@app.get("/")
+def index():
+    explanation = {
+        'message': "Welcome to the Sepsis Prediction App",
+        'description': "This API allows you to predict sepsis based on patient data.",
+    }
+    return explanation
+
+@app.post("/predict")
+def predict(PredictionInput: PredictionInput):
+    df = pd.DataFrame([PredictionInput.model_dump()])
+    # Make predictions using the pipeline
+    prediction = sep_pipeline.predict(df)
+    
+    encode = encoder.inverse_transform([prediction])[0]
+    
+    # Return the prediction
+    return {'prediction': encode }

requirements.txt

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+annotated-types==0.6.0
+anyio==3.7.1
+attrs==23.1.0
+blinker==1.6.2
+click==8.1.6
+colorama==0.4.6
+contourpy==1.1.1
+cycler==0.12.1
+exceptiongroup==1.2.0
+fastapi==0.104.1
+fastjsonschema==2.19.0
+Flask==2.3.2
+fonttools==4.45.1
+h11==0.14.0
+idna==3.6
+imbalanced-learn==0.11.0
+importlib-metadata==6.9.0
+importlib-resources==6.1.1
+itsdangerous==2.1.2
+Jinja2==3.1.2
+joblib==1.3.2
+jsonschema==4.20.0
+jsonschema-specifications==2023.11.2
+jupyter_core==5.5.0
+kiwisolver==1.4.5
+MarkupSafe==2.1.3
+matplotlib==3.7.4
+nbformat==5.9.2
+numpy==1.24.4
+packaging==23.2
+pandas==2.0.3
+Pillow==10.1.0
+pkgutil_resolve_name==1.3.10
+platformdirs==4.0.0
+plotly==5.18.0
+pydantic==2.5.2
+pydantic_core==2.14.5
+pyparsing==3.1.1
+python-dateutil==2.8.2
+pytz==2023.3.post1
+referencing==0.31.1
+rpds-py==0.13.2
+scikit-learn==1.3.2
+scipy==1.10.1
+seaborn==0.13.0
+six==1.16.0
+sniffio==1.3.0
+starlette==0.27.0
+tenacity==8.2.3
+threadpoolctl==3.2.0
+traitlets==5.14.0
+typing_extensions==4.8.0
+tzdata==2023.3
+uvicorn==0.24.0.post1
+Werkzeug==2.3.6
+xgboost==2.0.2
+zipp==3.17.0