Update main.py

main.py

@@ -1,63 +1,56 @@
-# main.py
 from fastapi import FastAPI, Query, Request, HTTPException
 from fastapi.responses import JSONResponse, HTMLResponse
 from fastapi.templating import Jinja2Templates
-#import xgboost as xgb
 import joblib
 import pandas as pd
-from pydantic import BaseModel  # Import Pydantic's BaseModel
+from pydantic import BaseModel
+from sklearn.pipeline import Pipeline
+from sklearn.feature_selection import SelectKBest
+from sklearn.ensemble import BaggingClassifier
+from xgboost import XGBClassifier
+from sklearn.preprocessing import StandardScaler
+from sklearn.compose import ColumnTransformer
+from sklearn.feature_selection import f_classif
 
 app = FastAPI()
 templates = Jinja2Templates(directory="templates")
 
-
-
 class InputFeatures(BaseModel):
-    # prg: float
-    # pl: float
-    # pr: float
-    # sk: float
-    # ts: float
-    # m11: float
-    # bd2: float
-    # age: int
-
     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
-    patients_age :int
+    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
+    patients_age: int
 
 # Load the pickled XGBoost model
 model_input = joblib.load("model_1.joblib")
 
-
 @app.post("/sepsis_prediction")
-async def predict(input:model_input):  
-     #Numeric Features 
+async def predict(input: InputFeatures):  
+    # Numeric Features 
+    num_features = [
+        ['Plasma_glucose', 'Blood_Work_Result_1', 'Blood_Pressure',
+         'Blood_Work_Result_2', 'Blood_Work_Result_3', 'Body_mass_index',
+         'Blood_Work_Result_4', 'patients_age']
+    ]
+
+    XGB = Pipeline([
+        ("col_trans", full_pipeline),  # You need to define full_pipeline
+        ("feature_selection", SelectKBest(score_func=f_classif, k='all')),
+        ("model", BaggingClassifier(base_estimator=XGBClassifier(random_state=42)))
+    ])
 
-     num_features = [['Plasma_glucose','Blood_Work_Result_1','Blood_Pressure',
-       'Blood_Work_Result_2',' Blood_Work_Result_3', 'Body_mass_index',
-       'Blood_Work_Result_4', 'Age']]
-
-    XGB= Pipeline([
-    ("col_trans", full_pipeline),
-    ("feature_selection", SelectKBest(score_func=f_classif, k='all')),
-    ("model", BaggingClassifier(base_estimator=XGBClassifier(random_state=42)))])
-
-#print(model_input)
     df = pd.DataFrame([input])
-    final_input = np.array(predict_input.fit_transform(df), dtype = np.str)
-    prediction = model.predict(np.array([[final_input]]).reshape(1, 1))
+    final_input = np.array(predict_input.fit_transform(df), dtype=np.str)  # Check predict_input, maybe it should be XGB
+    prediction = model_input.predict(np.array([final_input]).reshape(1, -1))
 
     return prediction
 
 if __name__ == '__main__':
-    uvicorn.run("Main:app", reload = True)
-
+    uvicorn.run("Main:app", reload=True)