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main.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+import joblib
+import pandas as pd
+import numpy as np
+from sklearn.preprocessing import StandardScaler
+from sklearn.impute import SimpleImputer
+from sklearn.compose import ColumnTransformer
+from sklearn.pipeline import Pipeline
+from sklearn.linear_model import LogisticRegression
+
+app = FastAPI()
+
+# Load the entire pipeline
+pipeline_filepath = "pipeline.joblib"
+pipeline = joblib.load(pipeline_filepath)
+
+class PatientData(BaseModel):
+    Plasma_glucose : float
+    Blood_Work_Result_1: float
+    Blood_Pressure : float
+    Blood_Work_Result_2 : float
+    Blood_Work_Result_3 : float
+    Body_mass_index  : float
+    Blood_Work_Result_4: float
+    Age: float
+    Insurance: int
+
+@app.get("/")
+def read_root():
+    explanation = {
+        'message': "Welcome to the Sepsis Prediction App",
+        'description': "This API allows you to predict sepsis based on patient data.",
+        'usage': "Submit a POST request to /predict with patient data to make predictions.",
+        
+    }
+    return explanation
+
+@app.post("/predict")
+def get_data_from_user(data: PatientData):
+    user_input = data.dict()
+
+    input_df = pd.DataFrame([user_input])
+
+    # Make predictions using the loaded pipeline
+    prediction = pipeline.predict(input_df)
+    probabilities = pipeline.predict_proba(input_df)
+
+    
+    probability_of_positive_class = probabilities[0][1]
+
+    # Calculate the prediction
+    sepsis_status = "Positive" if prediction[0] == 1 else "Negative"
+    sepsis_explanation = "A positive prediction suggests that the patient might be exhibiting sepsis symptoms and requires immediate medical attention." if prediction[0] == 1 else "A negative prediction suggests that the patient is not currently exhibiting sepsis symptoms."
+
+    result = {
+        'predicted_sepsis': sepsis_status,
+        'probability': probability_of_positive_class,
+        'sepsis_explanation': sepsis_explanation
+    }
+    return result

pipeline.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:74f69ea3c16f9dfa66dd5738523b19ebb4db2c17f0af741730f7a5b24e16a0be
+size 27955

requirements.txt

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+pytest
+scikit-learn
+fastapi[all]
+pydantic
+uvicorn
+pandas
+numpy
+joblib