Delete app.py

app.py

@@ -1,49 +0,0 @@
-import gradio as gr
-import joblib
-import numpy as np
-import pandas as pd
-
-# Load the model and scaler
-xg = joblib.load('xgb.joblib')
-scaler = joblib.load('scaler.joblib')
-
-# Manual encoders (replace joblib-loaded encoders)
-fuel_type_encoder = {'Petrol': 0, 'Diesel': 1, 'CNG': 2}
-seller_type_encoder = {'Dealer': 0, 'Individual': 1}
-transmission_encoder = {'Manual': 0, 'Automatic': 1}
-
-def Pred_func(Kms_Driven, Present_Price, Fuel_Type, Seller_Type, Transmission, Age):
-    try:
-        Fuel_Type = fuel_type_encoder[Fuel_Type]
-        Seller_Type = seller_type_encoder[Seller_Type]
-        Transmission = transmission_encoder[Transmission]
-
-        x_new = np.array([[Kms_Driven, Present_Price, Fuel_Type, Seller_Type, Transmission, Age]])
-        x_new = scaler.transform(x_new)
-        y_pred = xg.predict(x_new)
-        y_pred = round(y_pred[0], 2)
-        return str(y_pred) + 'k$'
-    except Exception as e:
-        return f"Error: {e}"
-
-def Pred_func_csv(file):
-    try:
-        df = pd.read_csv(file)
-        predictions = []
-
-        for row in df.iloc[:, :].values:
-            fuel = fuel_type_encoder.get(row[2], -1)
-            seller = seller_type_encoder.get(row[3], -1)
-            trans = transmission_encoder.get(row[4], -1)
-
-            new_row = np.array([[row[0], row[1], fuel, seller, trans, row[5]]])
-            new_row = scaler.transform(new_row)
-            y_pred = xg.predict(new_row)
-            y_pred = round(y_pred[0], 2)
-            predictions.append(y_pred)
-
-        df['Selling_Price'] = predictions
-        df.to_csv('predictions.csv', index=False)
-        return 'predictions.csv'
-    except Exception as e:
-        return f"Error processing file: {