Update app.py

app.py

@@ -1,7 +1,57 @@
+import json
+import numpy as np
 import gradio as gr
+import pickle
+from sklearn.linear_model import LinearRegression
 
-def greet(name):
-    return "Hello " + name + "!!"
+# Load the model
+with open('HYD_Rent_Predictor.pkl', 'rb') as model_file:
+    best_model = pickle.load(model_file)
+    if isinstance(best_model, LinearRegression):
+        best_model.check_input = False
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+# Load the columns
+with open('columns.json', 'r') as f:
+    data_columns = json.load(f)['data_columns']
+
+def predict_price(locality, balconies, bathroom, furnishingDesc, parking, property_size, type_bhk, floor):
+    loc_index = np.where(np.array(data_columns) == locality.lower())[0][0]
+
+    x = np.zeros(len(data_columns))
+    x[0] = balconies
+    x[1] = bathroom
+    x[2] = furnishingDesc
+    x[3] = parking
+    x[4] = property_size
+    x[5] = type_bhk
+    x[6] = floor
+
+    if loc_index >= 0:
+        x[loc_index] = 1
+
+    return best_model.predict([x])[0]
+
+# Gradio interface
+def interface(locality, balconies, bathroom, furnishingDesc, parking, property_size, type_bhk, floor):
+    result = predict_price(locality, balconies, bathroom, furnishingDesc, parking, property_size, type_bhk, floor)
+    return f"Predicted Rent: {result:.2f} INR"
+
+furnishing_options = [0.5, 0, 1]  # Replace with actual options
+parking_options = [0, 1, 2, 3]  # Replace with actual options
+type_bhk_options = [0.5, 1, 2, 3, 4, 5]  # Replace with actual options
+
+inputs = [
+    gr.inputs.Textbox(label="Locality"),
+    gr.inputs.Slider(0, 10, step=1, default=1, label="Balconies"),
+    gr.inputs.Slider(1, 5, step=1, default=1, label="Bathrooms"),
+    gr.inputs.Dropdown(furnishing_options, label="Furnishing Description"),
+    gr.inputs.Dropdown(parking_options, label="Parking"),
+    gr.inputs.Number(default=1000, label="Property Size (in sqft)"),
+    gr.inputs.Dropdown(type_bhk_options, label="Type BHK"),
+    gr.inputs.Number(default=1, label="Floor"),
+]
+
+outputs = gr.outputs.Textbox()
+
+# Create Gradio interface
+gr.Interface(fn=interface, inputs=inputs, outputs=outputs, title="Hyderabad House Rent Prediction").launch()