Update main.py

main.py

@@ -1,147 +1,11 @@
-# from fastapi import FastAPI,Form, Body,Path
-# from typing import Annotated
-# from pydantic import BaseModel, Field
-# import joblib
-# import pandas as pd
-# import numpy as np
-# import uvicorn
-# from fastapi.responses import JSONResponse
-
-
-# app = FastAPI()
-
-# # Load the numerical imputer, scaler, and model
-# num_imputer_filepath = "joblib_files/numerical_imputer.joblib"
-# scaler_filepath = "joblib_files/scaler.joblib"
-# model_filepath = "joblib_files/lr_model.joblib"
-
-# num_imputer = joblib.load(num_imputer_filepath)
-# scaler = joblib.load(scaler_filepath)
-# model = joblib.load(model_filepath)
-
-# class PatientData(BaseModel):
-#     PRG: float 
-#     PL: float
-#     PR: float
-#     SK: float
-#     TS: float
-#     M11: float
-#     BD2: float
-#     Age: float
-#     Insurance: int
-
-# def preprocess_input_data(user_input):
-#     input_data_df = pd.DataFrame([user_input])
-#     num_columns = [col for col in input_data_df.columns if input_data_df[col].dtype != 'object']
-#     input_data_imputed_num = num_imputer.transform(input_data_df[num_columns])
-#     input_scaled_df = pd.DataFrame(scaler.transform(input_data_imputed_num), columns=num_columns)
-#     return input_scaled_df
-
-# @app.get("/")
-# def read_root():
-#         return "Sepsis Prediction App"
-# @app.post("/sepsis/predict")
-# def get_data_from_user(data:PatientData):
-#     user_input = data.dict()
-#     input_scaled_df = preprocess_input_data(user_input)
-#     probabilities = model.predict_proba(input_scaled_df)[0]
-#     prediction = np.argmax(probabilities)
-
-#     sepsis_status = "Positive" if prediction == 1 else "Negative"
-#     probability = probabilities[1] if prediction == 1 else probabilities[0]
-
-#     if prediction == 1:
-#         sepsis_explanation = "A positive prediction suggests that the patient might be exhibiting sepsis symptoms and requires immediate medical attention."
-#     else:
-#         sepsis_explanation = "A negative prediction suggests that the patient is not currently exhibiting sepsis symptoms."
-
-#     statement = f"The patient's sepsis status is {sepsis_status} with a probability of {probability:.2f}. {sepsis_explanation}"
-
-#     user_input_statement = "user-inputted data: "
-#     output_df = pd.DataFrame([user_input])
-
-#     result = {'predicted_sepsis': sepsis_status, 'statement': statement, 'user_input_statement': user_input_statement, 'input_data_df': output_df.to_dict('records')}
-#     return result
-
-# from fastapi import FastAPI, Form
-# from pydantic import BaseModel
-# import joblib
-# import pandas as pd
-# import numpy as np
-# import uvicorn
-# from fastapi.responses import JSONResponse
-
-# app = FastAPI()
-
-# # Load the entire pipeline
-# pipeline_filepath = "pipeline.joblib"
-# pipeline = joblib.load(pipeline_filepath)
-
-# class PatientData(BaseModel):
-#     PRG: float
-#     PL: float
-#     PR: float
-#     SK: float
-#     TS: float
-#     M11: float
-#     BD2: float
-#     Age: float
-#     Insurance: int
-
-# @app.get("/")
-# def read_root():
-#     explanation = {
-#         'message': "Welcome to the Sepsis Prediction App",
-#         'description': "This API allows you to predict sepsis based on patient data.",
-#         'usage': "Submit a POST request to /predict with patient data to make predictions.",
-#         'input_fields': {
-#             'PRG': 'Plasma_glucose',
-#             'PL': 'Blood_Work_Result_1',
-#             'PR': 'Blood_Pressure',
-#             'SK': 'Blood_Work_Result_2',
-#             'TS': 'Blood_Work_Result_3',
-#             'M11': 'Body_mass_index',
-#             'BD2': 'Blood_Work_Result_4',
-#             'Insurance': 'Sepsis (Positive = 1, Negative = 0)'
-#         }
-#     }
-#     return explanation
-
-
-# @app.post("/predict")
-# def get_data_from_user(data: PatientData):
-#     user_input = data.model_dump()
-
-    
-#     input_df = pd.DataFrame([user_input])
-#      # Make predictions using the loaded pipeline
-#     # Make predictions using the loaded pipeline
-#     predictions = pipeline.predict(user_input)
-#     probabilities = pipeline.decision_function(user_input)
-
-#     # Assuming the pipeline uses a Logistic Regression model
-#     probability_of_positive_class = probabilities[0]
-
-#      # Calculate the prediction
-#     prediction = 1 if probability_of_positive_class >= 0.5 else 0
-
-#     sepsis_status = "Positive" if prediction == 1 else "Negative"
-#     sepsis_explanation = "A positive prediction suggests that the patient might be exhibiting sepsis symptoms and requires immediate medical attention." if prediction == 1 else "A negative prediction suggests that the patient is not currently exhibiting sepsis symptoms."
-
-   
-#     if prediction == 1:
-#         sepsis_status = "Positive"
-#         sepsis_explanation = "A positive prediction suggests that the patient might be exhibiting sepsis symptoms and requires immediate medical attention."
-#     else:
-#         sepsis_status = "Negative"
-#         sepsis_explanation = "A negative prediction suggests that the patient is not currently exhibiting sepsis symptoms."
-
-#     result = {
-#         'predicted_sepsis': sepsis_status,
-#         'sepsis_explanation': sepsis_explanation
-#     }
-#     return result
-
+from fastapi.openapi.models import APIKey, OAuthFlows as OAuthFlowsModel
+from fastapi.openapi.models import OAuthFlowAuthorizationCode as OAuthFlowAuthorizationCodeModel
+from fastapi.openapi.models import OAuthFlowAuthorizationCode as OAuthFlowAuthorizationCodeModel
+from fastapi.openapi.models import OAuthFlowsAuthorizationCode
+from fastapi.openapi.models import OAuthFlowAuthorizationCode as OAuthFlowAuthorizationCodeModel
+from fastapi.openapi.models import OAuthFlowAuthorizationCode as OAuthFlowAuthorizationCodeModel
+from fastapi.openapi.models import OAuthFlowAuthorizationCode
+from fastapi.openapi.models import OAuthFlowsAuthorizationCode
 from fastapi import FastAPI
 from pydantic import BaseModel
 import joblib
@@ -179,7 +43,11 @@ def read_root():
         
     }
     return explanation
-
+    #swagger ui
+@app.get("/docs")
+async def get_swagger_ui_html():
+    return get_swagger_ui_html(openapi_url="/openapi.json", title="API docs")
+    
 @app.post("/predict")
 def get_data_from_user(data: PatientData):
     user_input = data.dict()