Update app.py

app.py

@@ -1,16 +1,8 @@
-from fastapi import FastAPI
-from pydantic import BaseModel
 import pandas as pd
 from sklearn.linear_model import LinearRegression
 from sklearn.model_selection import train_test_split
 import gradio as gr
 
-app = FastAPI()
-
-@app.get("/")
-def read_root():
-    return {"message": "House Price Prediction API is running"}
-
 # ✅ Load inline dataset
 def load_data():
     csv_data = """area,bedrooms,age,price
@@ -38,22 +30,12 @@ def predict_price(model, area, bedrooms, age):
 
 model = train_model()
 
-# ✅ FastAPI POST endpoint
-class HouseFeatures(BaseModel):
-    area: float
-    bedrooms: int
-    age: int
-
-@app.post("/predict")
-def predict(data: HouseFeatures):
-    price = predict_price(model, data.area, data.bedrooms, data.age)
-    return {"predicted_price": round(price, 2)}
-
-# ✅ Gradio UI
+# ✅ Gradio UI function
 def gradio_predict(area, bedrooms, age):
     price = predict_price(model, area, bedrooms, age)
     return f"Predicted Price: ₹{round(price, 2):,.0f}"
 
+# ✅ Launch Gradio interface
 demo = gr.Interface(
     fn=gradio_predict,
     inputs=[