initial commit

app.py

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+# -*- coding: utf-8 -*-
+"""GradioUI.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/13606Sv5nfECx_rbwG8DGuXDHspZ6t4kA
+"""
+
+import gradio as gr
+import joblib
+import numpy as np
+import pandas as pd
+from sklearn.preprocessing import MinMaxScaler
+
+# Load the model and scaler
+model = joblib.load('model.joblib')
+scaler = joblib.load('scaler.joblib')
+
+# Prediction function
+def predict_rent(BHK, Size, Furnishing_Status, Bathroom, floor_number,
+                 Area_Type, City):
+
+    try:
+        # Prepare the input DataFrame with dummy variables
+        input_data = pd.DataFrame({
+            'BHK': [BHK],
+            'Size': [Size],
+            'Furnishing Status': [Furnishing_Status],
+            'Bathroom': [Bathroom],
+            'floor_number': [floor_number],
+            'Area Type_Carpet Area': [0],
+            'Area Type_Super Area': [0],
+            'City_Chennai': [0],
+            'City_Delhi': [0],
+            'City_Hyderabad': [0],
+            'City_Kolkata': [0],
+            'City_Mumbai': [0],
+        })
+
+        # Update one-hot encoded fields
+        if Area_Type == "Carpet Area":
+            input_data['Area Type_Carpet Area'] = [1]
+        elif Area_Type == "Super Area":
+            input_data['Area Type_Super Area'] = [1]
+
+        if City == "Chennai":
+            input_data['City_Chennai'] = [1]
+        elif City == "Delhi":
+            input_data['City_Delhi'] = [1]
+        elif City == "Hyderabad":
+            input_data['City_Hyderabad'] = [1]
+        elif City == "Kolkata":
+            input_data['City_Kolkata'] = [1]
+        elif City == "Mumbai":
+            input_data['City_Mumbai'] = [1]
+
+        rent = model.predict(input_data)[0]
+        return f"💰 Predicted Rent: ${rent:,.2f} per month"
+
+    except Exception as e:
+        return f"❌ Error: {e}"
+
+# Inputs
+inputs = [
+    gr.Number(label="BHK", minimum=1, maximum=6),
+    gr.Number(label="Size (in sqft)", minimum=100, maximum=80000),
+    gr.Number(label="Furnishing Status (0: Unfurnished, 1: Semi-Furnished, 2: Furnished)", minimum=0, maximum=2),
+    gr.Number(label="Bathrooms", minimum=1, maximum=3),
+    gr.Number(label="Floor Number", minimum=0, maximum=10),
+    gr.Dropdown(choices=["Carpet Area", "Super Area"], label="Area Type"),
+    gr.Dropdown(choices=["Chennai", "Delhi", "Hyderabad", "Kolkata", "Mumbai"], label="City"),
+]
+
+# Gradio UI
+gr.Interface(
+    fn=predict_rent,
+    inputs=inputs,
+    outputs=gr.Textbox(label="Prediction Result"),
+    title="🏡 Rent Prediction App",
+    description="Welcome to Leo's RealEstate! Our valued customer, please fill your preferences to get the predicted monthly rent."
+).launch()