Update app.py

app.py

@@ -1,26 +1,27 @@
 import gradio as gr
-import requests
+import pandas as pd
+from huggingface_hub import hf_hub_download
+import joblib
 
-# Define the Gradio app URL for API access
-API_URL = "https://huggingface.co/spaces/rmaitest/demoforml22oct/run/predict"
+# Load the model
+repo_id = "rmaitest/mlmodel2"
+model_file = "house_price_model.pkl"  # Adjust as necessary
+
+# Download and load the model
+model_path = hf_hub_download(repo_id, model_file)
+model = joblib.load(model_path)
 
 def predict_price(size, bedrooms, age):
-    # Prepare the data in the required format for the API request
-    payload = {
-        "data": [size, bedrooms, age]
-    }
-    
-    # Send request to the Gradio app API
-    response = requests.post(API_URL, json=payload)
+    # Create a DataFrame from the input
+    input_data = pd.DataFrame({
+        'Size (sq ft)': [size],
+        'Number of Bedrooms': [bedrooms],
+        'Age of House (years)': [age]
+    })
     
-    # Handle the response
-    if response.status_code == 200:
-        result = response.json()
-        # Extract the prediction from the response
-        prediction = result["data"][0]
-        return prediction
-    else:
-        return "Error: Unable to get prediction."
+    # Make prediction
+    prediction = model.predict(input_data)
+    return prediction[0]
 
 # Define the Gradio interface
 iface = gr.Interface(
@@ -32,9 +33,12 @@ iface = gr.Interface(
     ],
     outputs=gr.Number(label="Predicted Price ($)"),
     title="House Price Prediction",
-    description="Enter the size, number of bedrooms, and age of the house to get the predicted price."
+    description="Enter the size, number of bedrooms, and age of the house to get the predicted price.",
+    # Enable API mode for the Space
+    api_open=True
 )
 
 # Launch the interface
 if __name__ == "__main__":
     iface.launch()
+