Update app.py

app.py

@@ -2,6 +2,8 @@ import gradio as gr
 import pandas as pd
 from huggingface_hub import hf_hub_download
 import joblib
+from fastapi import FastAPI
+from gradio import fastapi as gr_fastapi
 
 # Load the model
 repo_id = "rmaitest/mlmodel2"
@@ -11,6 +13,7 @@ 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({
@@ -23,7 +26,7 @@ def predict_price(size, bedrooms, age):
     prediction = model.predict(input_data)
     return prediction[0]
 
-# Define the Gradio interface (remove api_open parameter)
+# Define the Gradio interface
 iface = gr.Interface(
     fn=predict_price,
     inputs=[
@@ -36,8 +39,14 @@ iface = gr.Interface(
     description="Enter the size, number of bedrooms, and age of the house to get the predicted price."
 )
 
-# Launch the interface
+# Create FastAPI app
+app = FastAPI()
+
+# Mount Gradio as an API on the /predict endpoint
+app = gr_fastapi.mount_gradio_app(app, iface, path="/predict")
+
+# Launch Gradio app (optionally with API)
 if __name__ == "__main__":
-    iface.launch()
+    iface.launch(share=True)