Delete app.py.py

app.py.py

@@ -1,82 +0,0 @@
-# -*- 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()