Upload app.py

app.py

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+from fastapi import FastAPI, HTTPException, Query
+from pydantic import BaseModel
+import pickle
+import pandas as pd
+
+app = FastAPI(
+    title="Sepsis Prediction API",
+    description="This FastAPI application provides sepsis predictions using a machine learning model.",
+    version="1.0"
+)
+
+# Load the model and key components
+with open('model_and_key_components.pkl', 'rb') as file:
+    loaded_components = pickle.load(file)
+
+loaded_model = loaded_components['model']
+loaded_encoder = loaded_components['encoder']
+loaded_scaler = loaded_components['scaler']
+
+# Define the input data structure using Pydantic BaseModel
+class InputData(BaseModel):
+    PRG: int = Query(..., title="Patient's Pregnancy Count", description="Enter the number of pregnancies.", example=2)
+    PL: float = Query(..., title="Platelet Count", description="Enter the platelet count.", example=150.0)
+    PR: float = Query(..., title="Pulse Rate", description="Enter the pulse rate.", example=75.0)
+    SK: float = Query(..., title="Skin Thickness", description="Enter the skin thickness.", example=25.0)
+    TS: int = Query(..., title="Triceps Skin Fold Thickness", description="Enter the triceps skin fold thickness.", example=30)
+    M11: float = Query(..., title="Insulin Level", description="Enter the insulin level.", example=120.0)
+    BD2: float = Query(..., title="BMI", description="Enter the Body Mass Index (BMI).", example=32.0)
+    Age: int = Query(..., title="Age", description="Enter the patient's age.", example=35)
+
+# Define the output data structure using Pydantic BaseModel
+class OutputData(BaseModel):
+    Sepsis: str
+
+# Define a function to preprocess input data
+def preprocess_input_data(input_data: InputData):
+    # Encode Categorical Variables (if needed)
+    # All columns are numerical. No need for encoding
+
+    # Apply scaling to numerical data
+    numerical_cols = ['PRG', 'PL', 'PR', 'SK', 'TS', 'M11', 'BD2', 'Age']
+    input_data_scaled = loaded_scaler.transform([list(input_data.dict().values())])
+
+    return pd.DataFrame(input_data_scaled, columns=numerical_cols)
+
+# Define a function to make predictions
+def make_predictions(input_data_scaled_df: pd.DataFrame):
+    y_pred = loaded_model.predict(input_data_scaled_df)
+    sepsis_mapping = {0: 'Negative', 1: 'Positive'}
+    return sepsis_mapping[y_pred[0]]
+
+@app.get("/")
+async def root():
+    # Endpoint at the root URL ("/") returns a welcome message with a clickable link
+    message = "Welcome to your Sepsis Classification API! Click [here](/docs) to access the API documentation."
+    return {"message": message}
+
+@app.post("/predict/", response_model=OutputData)
+async def predict_sepsis(input_data: InputData):
+    try:
+        input_data_scaled_df = preprocess_input_data(input_data)
+        sepsis_status = make_predictions(input_data_scaled_df)
+        return {"Sepsis": sepsis_status}
+    except Exception as e:
+        # Handle exceptions and return an error response
+        raise HTTPException(status_code=500, detail=str(e))
+
+if __name__ == "__main__":
+    import uvicorn
+    # Run the FastAPI application on the local host and port 8000
+    uvicorn.run(app, host="127.0.0.1", port=8000)