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Dockerfile

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+# FROM python:3.9
+
+# WORKDIR /code
+
+# COPY ./requirements.txt /code/requirements.txt
+
+# RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+# COPY . .
+
+# CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "7860"]
+
+
+# Use the official Python image as the base image
+#FROM python:3.9-slim
+ FROM python:3.10.6
+
+# Set the working directory in the container
+WORKDIR /app
+
+# Copy the requirements file into the container
+COPY requirements.txt .
+
+# Install dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy the  xgb_model.joblib files to the container
+COPY xgb.joblib .
+
+# Copy the current directory contents into the container at /app
+COPY . /app
+
+# Expose the port that the FastAPI application will run on
+EXPOSE 7860
+
+# Command to run the FastAPI application when the container starts
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

datasets/Paitients_Files_Test.csv

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+ID,PRG,PL,PR,SK,TS,M11,BD2,Age,Insurance

datasets/Paitients_Files_Train.csv

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+ID,PRG,PL,PR,SK,TS,M11,BD2,Age,Insurance,Sepssis

main.py

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+from fastapi import FastAPI
+import uvicorn
+import json
+from pydantic import BaseModel
+import joblib
+import json
+import imblearn
+import pandas as pd
+from xgboost import XGBClassifier
+from fastapi import FastAPI, Query, Request, HTTPException
+
+
+
+app = FastAPI()
+
+# loading my best model with joblib 
+model = joblib.load("./xgb.joblib")
+
+###
+@app.get("/")
+async def read_root():
+    return {"message": "Welcome to the Sepsis Prediction using FastAPI"}
+
+def classify(prediction):
+    if prediction == 0:
+        return "Patient does not have sepsis"
+    else:
+        return "Patient has sepsis"
+        
+@app.post("/predict/")
+async def predict_sepsis(
+    request: Request,
+    prg: float = Query(..., description="Plasma_glucose"),
+    pl: float = Query(..., description="Blood_Work_R1"),
+    pr: float = Query(..., description="Blood_Pressure"),
+    sk: float = Query(..., description="Blood_Work_R2"),
+    ts: float = Query(..., description="Blood_Work_R3"),
+    m11: float = Query(..., description="BMI"),
+    bd2: float = Query(..., description="Blood_Work_R4"),
+    age: int = Query(..., description="Age")
+    # ... (other input parameters)
+):
+    input_data = [prg, pl, pr, sk, ts, m11, bd2, age]
+
+    input_df = pd.DataFrame([input_data], columns=[
+        "Plasma_glucose", "Blood_Work_R1", "Blood_Pressure",
+        "Blood_Work_R2", "Blood_Work_R3",
+        "BMI", "Blood_Work_R4", "Age"
+    ])
+
+    pred = model.predict(input_df)
+    output = classify(pred[0])
+
+    response = {
+        "prediction": output
+    }
+
+    return response
+
+    # Run the app using Uvicorn
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="127.0.0.1", port=7860)
+
+
+
+
+
+
+

xgb.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c8f67e1c70c753b04cb7b68256898ba9c22b30d0e63cc58cdde831b93326bf8b
+size 3837923