Added all my project file and deployed my App

Dockerfile.txt

@@ -0,0 +1,19 @@
+# Use the official Python image as the base image
+FROM python:3.9
+
+# Set the working directory in the container
+WORKDIR /code
+
+# Copy requirements.txt file
+COPY requirements.txt .
+
+# Install the Python dependencies
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+# copy all the files in the app folder
+COPY app/ .
+
+EXPOSE 7860
+
+# Start the FastAPI app with Uvicorn when the container starts
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

main.py

@@ -0,0 +1,86 @@
+from fastapi import FastAPI
+from pydantic import BaseModel
+import pickle
+import pandas as pd
+import numpy as np
+import uvicorn
+
+# call the app
+app = FastAPI(title="API")
+
+# Load the model and scaler
+def load_model_and_scaler():
+    with open("model.pkl", "rb") as f1, open("scaler.pkl", "rb") as f2:
+        return pickle.load(f1), pickle.load(f2)
+
+model, scaler = load_model_and_scaler()
+
+def predict(df, endpoint="simple"):
+    # Scaling
+    scaled_df = scaler.transform(df)  # Scale the input data using a pre-defined scaler
+
+    # Prediction
+    prediction = model.predict_proba(scaled_df)  # Make predictions using a pre-trained model
+
+    highest_proba = prediction.max(axis=1)  # Get the highest probability for each prediction
+
+    # Assign predicted labels based on the highest probabilities
+    predicted_labels = ["Patient does not have sepsis" if i == 0 else "Patient has sepsis" for i in highest_proba]
+    print(f"Predicted labels: {predicted_labels}")  # Print the predicted labels to the terminal
+    print(highest_proba)  # Print the highest probabilities to the terminal
+
+    response = []
+    for label, proba in zip(predicted_labels, highest_proba):
+        # Create a response for each prediction with the predicted label and probability
+        output = {
+            "prediction": label,
+            "probability of prediction": str(round(proba * 100)) + '%'  # Convert the probability to a percentage
+        }
+        response.append(output)  # Add the response to the list of responses
+
+    return response  # Return the list of responses
+
+
+class Patient(BaseModel):
+    Blood_Work_R1: int
+    Blood_Pressure: int
+    Blood_Work_R3: int
+    BMI: float
+    Blood_Work_R4: float
+    Patient_age: int
+
+class Patients(BaseModel):
+    all_patients: list[Patient]
+
+    @classmethod
+    def return_list_of_dict(cls, patients: "Patients"):
+        patient_list = []
+        for patient in patients.all_patients: #for each item in all_patients,
+            patient_dict = patient.dict() #convert to a dictionary
+            patient_list.append(patient_dict) #add it to the empty list called patient_list
+        return patient_list
+    
+# Endpoints
+# Root Endpoint
+@app.get("/")
+def root():
+    return {"Welcome to the Sepsis Prediction API! This API provides endpoints for predicting sepsis based on patient data."}    
+
+# Prediction endpoint
+@app.post("/predict")
+def predict_sepsis(patient: Patient):
+    # Make prediction
+    data = pd.DataFrame(patient.dict(), index=[0])
+    parsed = predict(df=data)
+    return {"output": parsed}
+
+# Multiple Prediction Endpoint
+@app.post("/predict_multiple")
+def predict_sepsis_for_multiple_patients(patients: Patients):
+    """Make prediction with the passed data"""
+    data = pd.DataFrame(Patients.return_list_of_dict(patients))
+    parsed = predict(df=data, endpoint="multi")
+    return {"output": parsed}
+
+if __name__ == "__main__":
+    uvicorn.run("main:app", reload=True)

model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e03b9e36a896ad1fdce4008c8f0bbaf734571a4a757197fadcb36821bc00f215
+size 937816

requirements.txt

@@ -0,0 +1,5 @@
+fastapi[all]
+scikit-learn==1.2.1
+numpy==1.21.5
+pandas==1.3.1
+pydantic

scaler.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:aeb4a535138b890f96a1f33737c29b408c1f673a6ed4500882a3ed382f65126b
+size 814