add main.py file

main.py

@@ -0,0 +1,103 @@
+
+
+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)
+
+# import joblib
+
+# Reload the model
+pipeline_filepath = "pipeline.joblib"
+pipeline = joblib.load(pipeline_filepath)
+
+# Save the model again
+joblib.dump(pipeline, 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
+
+import logging
+
+# Configure logging
+logging.basicConfig(level=logging.INFO)  # Set the desired logging level
+
+@app.post("/predict")
+def get_data_from_user(data: PatientData):
+    try:
+        logging.info("Received data: %s", data.dict())
+        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"
+
+        result = {
+            'predicted_sepsis': sepsis_status,
+            'probability': probability_of_positive_class,
+        }
+        return result
+    except Exception as e:
+        logging.error("Error: %s", e)
+        return {"error": "An error occurred during prediction."}