dada

main.py

@@ -4,60 +4,14 @@ import uvicorn
 import pandas as pd
 
 app = FastAPI()
-
-# @app.get("/")
-# def read_root():
-#     return {"Hello": "World!"}
-
-
-# Function to load pickle file
-def load_pickle(filename):
-    with open(filename, 'rb') as file:
-        data = pickle.load(file)
-        return data
-    
-# Load pickle file
-ml_components = load_pickle('ml_sepsis.pkl') 
-
-# Components in the pickle file
-ml_model = ml_components['model']
-pipeline_processing = ml_components['pipeline']    
-
+ 
 #Endpoints 
 #Root endpoints
 @app.get("/")
-def root():
-    return {"API": "An API for Sepsis Prediction."}
-    
-@app.get('/Predict_Sepsis')
-async def predict(Plasma_glucose: int, Blood_Work_Result_1: int,
-                  Blood_Pressure: int, Blood_Work_Result_2: int,
-                    Blood_Work_Result_3: int, Body_mass_index: float, 
-                    Blood_Work_Result_4: float,Age: int, Insurance:float):
-    
-    data = pd.DataFrame({'Plasma glucose': [Plasma_glucose], 'Blood Work Result-1':	[Blood_Work_Result_1],
-                         'Blood Pressure': [Blood_Pressure], 'Blood Work Result-2': [Blood_Work_Result_2],
-                        'Blood Work Result-3': [Blood_Work_Result_3], 'Body mass index': [Body_mass_index],
-                        'Blood Work Result-4':	[Blood_Work_Result_4], 'Age': [Age], 'Insurance':[Insurance]})
-    
-    data_prepared = pipeline_processing.transform(data)
-
-    model_output = ml_model.predict(data_prepared).tolist()
+async def root():
+    # Dapatkan URL publik dari ngrok
+    ngrok_url = "Tidak Ada URL Publik (ngrok belum selesai memulai)"
 
-    prediction = make_prediction(model_output)
-
-    return prediction
-    
+    return {"message": "Hello, World!", "ngrok_url": ngrok_url}
     
 
-
-def make_prediction(data_prepared):
-
-    output_pred = data_prepared
-
-    if output_pred == 0:
-        output_pred = "Sepsis status is Negative"
-    else:
-        output_pred = "Sepsis status is Positive"
-        
-    return output_pred