Create main.py

main.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+from typing import Dict
+import joblib
+import numpy as np
+
+app = FastAPI()
+
+# Define the input schema
+class UsageInput(BaseModel):
+    days_until_cycle_end: int
+    voice_total_allowance: float
+    voice_remaining: float
+    data_total_allowance: float
+    data_remaining: float
+    plan_price: float
+
+# Load the trained model
+model = joblib.load("model.pkl")
+
+@app.post("/predict")
+def predict(input_data: UsageInput) -> Dict[str, float]:
+    # Convert input to model format
+    input_array = np.array([[
+        input_data.days_until_cycle_end,
+        input_data.voice_total_allowance,
+        input_data.voice_remaining,
+        input_data.data_total_allowance,
+        input_data.data_remaining,
+        input_data.plan_price
+    ]])
+
+    # Predict using the model
+    predicted_voice, predicted_data = model.predict(input_array)[0]
+
+    return {
+        "predicted_total_voice_usage": round(predicted_voice, 2),
+        "predicted_total_data_usage": round(predicted_data, 2)
+    }