Upload 5 files

lr_model.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b4e6945f0cda3e51575762750345b994c1a0e00422f495f6e20140075b3754dc
+size 1193

main.py

@@ -0,0 +1,82 @@
+import pandas as pd
+import joblib
+from fastapi import FastAPI
+import uvicorn
+import numpy as np
+import os
+
+app = FastAPI()
+
+def load_model():
+    cwd = os.getcwd()
+    destination = os.path.join(cwd, "Assets")
+
+    imputer_filepath = os.path.join(destination, "numerical_imputer.joblib")
+    scaler_filepath = os.path.join(destination, "scaler.joblib")
+    model_filepath = os.path.join(destination, "lr_model.joblib")
+
+    num_imputer = joblib.load(imputer_filepath)
+    scaler = joblib.load(scaler_filepath)
+    model = joblib.load(model_filepath)
+
+    return num_imputer, scaler, model
+
+
+def preprocess_input_data(input_data, num_imputer, scaler):
+    input_data_df = pd.DataFrame([input_data])
+    num_columns = [col for col in input_data_df.columns if input_data_df[col].dtype != 'object']
+    input_data_imputed_num = num_imputer.transform(input_data_df[num_columns])
+    input_scaled_df = pd.DataFrame(scaler.transform(input_data_imputed_num), columns=num_columns)
+    return input_scaled_df
+
+@app.get("/")
+def read_root():
+    return "Sepsis Prediction App"
+
+@app.get("/sepsis/predict")
+def predict_sepsis_endpoint(PRG: float, PL: float, PR: float, SK: float, TS: float,
+                            M11: float, BD2: float, Age: float, Insurance: int):
+    num_imputer, scaler, model = load_model()
+
+    input_data = {
+        'PRG': [PRG],
+        'PL': [PL],
+        'PR': [PR],
+        'SK': [SK],
+        'TS': [TS],
+        'M11': [M11],
+        'BD2': [BD2],
+        'Age': [Age],
+        'Insurance': [Insurance]
+    }
+
+    input_scaled_df = preprocess_input_data(input_data, num_imputer, scaler)
+
+    probabilities = model.predict_proba(input_scaled_df)[0]
+    prediction = np.argmax(probabilities)
+
+    sepsis_status = "Positive" if prediction == 1 else "Negative"
+    
+    probability = probabilities[1] if prediction == 1 else probabilities[0]
+
+    #statement = f"The patient is {sepsis_status}. There is a {'high' if prediction == 1 else 'low'} probability ({probability:.2f}) that the patient is susceptible to developing sepsis."
+
+    if prediction == 1:
+        status_icon = "✔"  # Red 'X' icon for positive sepsis prediction
+        sepsis_explanation = "Sepsis is a life-threatening condition caused by an infection. A positive prediction suggests that the patient might be exhibiting sepsis symptoms and requires immediate medical attention."
+    else:
+        status_icon = "✘"  # Green checkmark icon for negative sepsis prediction
+        sepsis_explanation = "Sepsis is a life-threatening condition caused by an infection. A negative prediction suggests that the patient is not currently exhibiting sepsis symptoms."
+
+    statement = f"The patient's sepsis status is {sepsis_status} {status_icon} with a probability of {probability:.2f}. {sepsis_explanation}"
+
+    user_input_statement = "Please note this is the user-inputted data: "
+
+    output_df = pd.DataFrame([input_data])
+
+    result = {'predicted_sepsis': sepsis_status, 'statement': statement, 'user_input_statement': user_input_statement, 'input_data_df': output_df.to_dict('records')}
+
+    return result
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=8080, reload=True)

numerical_imputer.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:345add991409813d7429e1a0ad969dfc0c770aab4c6029dabb2231c450ba0cc1
+size 913

requirements.txt

@@ -0,0 +1,8 @@
+joblib==1.2.0
+matplotlib==3.7.1
+numpy==1.24.2
+pandas==1.5.3
+scikit-learn==1.2.2
+fastapi==0.95.1
+uvicorn==0.22.0
+pydantic==1.10.7

scaler.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d2e124383ce5c05bbb777499908c299b05fda0c87445d602b601794d6261f209
+size 752