Hope this is final commit

.gitignore

@@ -1,3 +1 @@
-venv
-app.py
-Dockerfile
+venv

Dockerfile

@@ -0,0 +1,20 @@
+# Use the official Python base image
+FROM python:3.10-slim
+
+# Set the working directory in the container
+WORKDIR /app
+
+# Copy the requirements file into the container
+COPY requirements.txt .
+
+# Install the dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy the rest of the application files
+COPY . .
+
+# Expose the port that Uvicorn will run on
+EXPOSE 7860
+
+# Command to run the application
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+import numpy as np
+from fastapi import FastAPI
+from pydantic import BaseModel
+import joblib
+import pickle
+
+app = FastAPI()
+
+def load_model():
+    try:
+        model = joblib.load("model.joblib")
+        print("Model Load Successfully")
+        return model
+    except Exception as e:
+        print(f"Error loading model: {e}")
+        model = None
+        return model
+
+def load_scaler():
+    try:
+        with open("scaler.pkl", 'rb') as file:
+            scaler = pickle.load(file)
+        print("Scaler loaded successfully")
+        return scaler
+    except:
+        return ("Error while loading scaler")
+
+def load_encoder():
+    try:
+        with open("encoder.pkl", 'rb') as file:
+            encoder = pickle.load(file)
+        print("Encoder loaded successfully")
+        return encoder
+    except:
+        print("Error while loading encoder")
+
+model = load_model()
+encoder = load_encoder()
+scaler = load_scaler()
+
+def load_data(data):
+    gender = 1 if data.Gender == 'Male' else 0
+    age = data.Age
+
+    neighbourhood = encoder.transform([data.Neighbourhood])[0]
+    
+    scholarship = 1 if data.Scholarship == 'Yes' else 0
+    hipertension = 1 if data.Hipertension == 'Yes' else 0
+    diabetes = 1 if data.Diabetes == 'Yes' else 0
+    alcoholism = 1 if data.Alcoholism == 'Yes' else 0
+    handcap = 1 if data.Handcap == 'Yes' else 0
+    smsreceived = 1 if data.SMSreceived == "Yes" else 0
+    waitingtime = data.WaitingTime
+    appointmentDayWeek = data.AppointmentDayOfWeek
+
+    lst = ['SameDay', 'Short', 'Mid', 'Long', 'VeryLong']
+    waitingGroup = lst.index(data.WaitingGroup)
+
+    cronic_count = hipertension + diabetes + alcoholism
+    chronicGroup = 0
+    if cronic_count >= -1 or cronic_count < 0:
+        chronicGroup = 0
+    elif cronic_count == 0 or cronic_count <= 1:
+        chronicGroup = 1
+    elif cronic_count > 1 or cronic_count <=3:
+        chronicGroup = 2
+
+    return np.array([gender, 
+                    age, 
+                    neighbourhood, 
+                    scholarship, 
+                    hipertension, 
+                    diabetes, 
+                    alcoholism, 
+                    handcap, 
+                    smsreceived, 
+                    waitingtime, 
+                    appointmentDayWeek,
+                    waitingGroup,
+                    chronicGroup])
+
+class InputData(BaseModel):
+    Gender: str
+    Age: int
+    Neighbourhood: str
+    Scholarship: str
+    Hipertension: str
+    Diabetes: str
+    Alcoholism: str
+    Handcap: str
+    SMSreceived: str
+    WaitingTime: int
+    AppointmentDayOfWeek: int
+    WaitingGroup: str
+
+@app.get("/")
+def home():
+    return {"Message": "This is an API for no-show prediction"}
+
+@app.post("/predict")
+def prediction(data: InputData):
+    print("Data -> ", data)
+    print("-"*40)
+    raw_data = load_data(data)
+    print("Row Data -> ", raw_data)
+    print("-"*40)
+    scaled_data = scaler.transform(raw_data.reshape(1, -1))
+    prediction = model.predict(scaled_data)
+
+    response = {"message": "Data received successfully!", "prediction": "Patient Will No-Show" if int(prediction[0]) == 1 else "Patient Will Show"}
+    return response