Create app.py

app.py

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+import gradio as gr
+import pickle
+import json
+import numpy as np
+
+# Load model and columns
+with open("../model/banglore_home_prices_model.pickle", "rb") as f:
+    model = pickle.load(f)
+
+with open("../model/columns.json", "r") as f:
+    data_columns = json.load(f)["data_columns"]
+
+locations = data_columns[3:]  # Extract location columns (FROM FORTH COLUMN TO END FOUND IN LOCATION)
+
+def predict_price(total_sqft, bath, bhk, location):
+    # Prepare the input array
+    x = np.zeros(len(data_columns))
+    x[0] = total_sqft
+    x[1] = bath
+    x[2] = bhk
+    if location in locations:
+        loc_index = data_columns.index(location)
+        x[loc_index] = 1
+    
+    # Make prediction
+    return model.predict([x])[0]
+
+# Create the Gradio interface
+inputs = [
+    gr.Number(label="Total Square Feet"),
+    gr.Number(label="Bath"),
+    gr.Number(label="BHK"),
+    gr.Dropdown(choices=locations, label="Location")
+]
+
+outputs = gr.Textbox(label="Predicted Price (Lakh)")
+
+# Launch the interface
+gr.Interface(fn=predict_price, inputs=inputs, outputs=outputs, title="Real Estate Price Prediction").launch()