Upload main.py

main.py

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+# 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 import FastAPI
+from pydantic import BaseModel
+import joblib
+import pandas as pd
+import numpy as np
+from sklearn.preprocessing import StandardScaler
+from sklearn.impute import SimpleImputer
+from sklearn.compose import ColumnTransformer
+from sklearn.pipeline import Pipeline
+from sklearn.linear_model import LogisticRegression
+
+app = FastAPI()
+
+# Load the entire pipeline
+pipeline_filepath = "pipeline.joblib"
+pipeline = joblib.load(pipeline_filepath)
+
+class PatientData(BaseModel):
+    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
+    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.",
+        
+    }
+    return explanation
+
+@app.post("/predict")
+def get_data_from_user(data: PatientData):
+    user_input = data.dict()
+
+    input_df = pd.DataFrame([user_input])
+
+    # Make predictions using the loaded pipeline
+    prediction = pipeline.predict(input_df)
+    probabilities = pipeline.predict_proba(input_df)
+
+    
+    probability_of_positive_class = probabilities[0][1]
+
+    # Calculate the prediction
+    sepsis_status = "Positive" if prediction[0] == 1 else "Negative"
+    sepsis_explanation = "A positive prediction suggests that the patient might be exhibiting sepsis symptoms and requires immediate medical attention." if prediction[0] == 1 else "A negative prediction suggests that the patient is not currently exhibiting sepsis symptoms."
+
+    result = {
+        'predicted_sepsis': sepsis_status,
+        'probability': probability_of_positive_class,
+        'sepsis_explanation': sepsis_explanation
+    }
+    return result