Update app.py

app.py

@@ -1,10 +1,7 @@
-
 import gradio as gr
 import pandas as pd
 from huggingface_hub import hf_hub_download
 import joblib
-from fastapi import FastAPI
-from gradio.routes import Route
 
 # Load the model
 repo_id = "rmaitest/mlmodel2"
@@ -14,7 +11,6 @@ model_file = "house_price_model.pkl"  # Adjust as necessary
 model_path = hf_hub_download(repo_id, model_file)
 model = joblib.load(model_path)
 
-# Define the prediction function
 def predict_price(size, bedrooms, age):
     # Create a DataFrame from the input
     input_data = pd.DataFrame({
@@ -40,18 +36,5 @@ iface = gr.Interface(
     description="Enter the size, number of bedrooms, and age of the house to get the predicted price."
 )
 
-# Create FastAPI app
-app = FastAPI()
-
-# Create a route for the /predict API endpoint
-@app.post("/predict")
-async def predict(size: float, bedrooms: int, age: int):
-    # Call the Gradio function manually for the API route
-    return iface.fn(size, bedrooms, age)
-
-# Launch Gradio interface (only for UI purposes, if needed)
-if __name__ == "__main__":
-    iface.launch(share=True)
-
-
-
+# Launch the interface with a public URL
+iface.launch(share=True)