Delete app.py

app.py

@@ -1,71 +0,0 @@
-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)