Create app.py

app.py

@@ -0,0 +1,31 @@
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+import joblib
+import numpy as np
+
+# Load the saved model (make sure it's in the same directory or provide the correct path)
+model = joblib.load("linear_regression_model.pkl")
+
+# Initialize the FastAPI app
+app = FastAPI()
+
+# Define the input schema for predictions
+class PredictionInput(BaseModel):
+    feature1: float
+    feature2: float
+
+# Define the prediction endpoint
+@app.post("/predict")
+def predict(input_data: PredictionInput):
+    try:
+        # Convert input into model-compatible format (as a 2D array)
+        input_features = np.array([[input_data.feature1, input_data.feature2]])
+        prediction = model.predict(input_features)
+        return {"prediction": prediction.tolist()}  # Return prediction as a list
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+# Basic greeting endpoint (optional)
+@app.get("/")
+def greet_json():
+    return {"message": "Welcome to the Linear Regression API!"}