Update main.py

main.py

@@ -2,7 +2,7 @@
 from fastapi import FastAPI, Query, Request, HTTPException
 from fastapi.responses import JSONResponse, HTMLResponse
 from fastapi.templating import Jinja2Templates
-import xgboost as xgb
+#import xgboost as xgb
 import joblib
 import pandas as pd
 from pydantic import BaseModel  # Import Pydantic's BaseModel
@@ -23,7 +23,20 @@ class InputFeatures(BaseModel):
     age: int
 
 # Load the pickled XGBoost model
-xgb_model = joblib.load("model.joblib")
+model_input = joblib.load("model_1.joblib")
+
+@app.post("/sepsis_prediction")
+    async def predicts(input:model_input):
+
+    XGB= Pipeline([
+    ("col_trans", full_pipeline),
+    ("feature_selection", SelectKBest(score_func=f_classif, k='all')),
+    ("model", BaggingClassifier(base_estimator=XGBClassifier(random_state=42)))
+])
+        return prediction
+
+
+
 
 
 # @app.get("/")
@@ -33,35 +46,35 @@ xgb_model = joblib.load("model.joblib")
 # @app.get("/form/")
 # async def show_form():
 
-@app.post("/predict/")
-async def predict_sepsis(
-    request: Request,
-    input_data: InputFeatures  # Use the Pydantic model for input validation
-):
-    try:
-        # Convert Pydantic model to a DataFrame for prediction
-        input_df = pd.DataFrame([input_data.dict()])
-
-        # Make predictions using the loaded XGBoost model
-        prediction = xgb_model.predict_proba(xgb.DMatrix(input_df))
-
-        # Create a JSON response
-        response = {
-            "input_features": input_data,
-            "prediction": {
-                "class_0_probability": prediction[0],
-                "class_1_probability": prediction[1]
-            }
-        }
-
-        return templates.TemplateResponse(
-            "display_params.html",
-            {
-                "request": request,
-                "input_features": response["input_features"],
-                "prediction": response["prediction"]
-            }
-        )
-    except Exception as e:
-        #raise HTTPException(status_code=500, detail="An error occurred while processing the request.")
-        raise HTTPException(status_code=500, detail=str(e))
+# @app.post("/predict/")
+# async def predict_sepsis(
+#     request: Request,
+#     input_data: InputFeatures  # Use the Pydantic model for input validation
+# ):
+#     try:
+#         # Convert Pydantic model to a DataFrame for prediction
+#         input_df = pd.DataFrame([input_data.dict()])
+
+#         # Make predictions using the loaded XGBoost model
+#         prediction = xgb_model.predict_proba(xgb.DMatrix(input_df))
+
+#         # Create a JSON response
+#         response = {
+#             "input_features": input_data,
+#             "prediction": {
+#                 "class_0_probability": prediction[0],
+#                 "class_1_probability": prediction[1]
+#             }
+#         }
+
+#         return templates.TemplateResponse(
+#             "display_params.html",
+#             {
+#                 "request": request,
+#                 "input_features": response["input_features"],
+#                 "prediction": response["prediction"]
+#             }
+#         )
+#     except Exception as e:
+#         #raise HTTPException(status_code=500, detail="An error occurred while processing the request.")
+#         raise HTTPException(status_code=500, detail=str(e))