Update app.py

app.py

@@ -2,16 +2,29 @@ import streamlit as st
 import numpy as np
 import pandas as pd
 import tensorflow as tf
-from sklearn.preprocessing import StandardScaler
+from sklearn.preprocessing import OneHotEncoder, StandardScaler
 import joblib
 
 # Load the trained model
 model = tf.keras.models.load_model('trained_game_price_model.h5')
 
+# Load pre-trained OneHotEncoder and StandardScaler (assuming you have these saved)
+ohe = joblib.load('ohe.pkl')  # Load the OneHotEncoder
+scaler = joblib.load('scaler.pkl')  # Load the StandardScaler
+
 # Function to preprocess the input data
-def preprocess_input(data, scaler):
+def preprocess_input(data, ohe, scaler):
     # Convert input into DataFrame for processing
-    input_data = pd.DataFrame([data])
+    input_data = pd.DataFrame([data], columns=['genre', 'target_platform', 'total_sales', 'initial_price', 'avg_market_price', 'market_saturation', 'user_rating'])
+    
+    # Apply OneHotEncoder for categorical features
+    input_data_transformed = ohe.transform(input_data[['genre', 'target_platform']])
+    
+    # Ensure numerical features are 2D
+    numerical_features = input_data[['total_sales', 'initial_price', 'avg_market_price', 'market_saturation', 'user_rating']].values
+    
+    # Merge with numerical features
+    input_data = np.hstack((input_data_transformed.toarray(), numerical_features))
     
     # Scale the features
     input_data_scaled = scaler.transform(input_data)
@@ -21,16 +34,13 @@ def preprocess_input(data, scaler):
 # Function to make a prediction
 def make_prediction(input_data):
     # Preprocess the data for the model
-    input_data_scaled = preprocess_input(input_data, scaler)
+    input_data_scaled = preprocess_input(input_data, ohe, scaler)
     
     # Make prediction
     prediction = model.predict(input_data_scaled)
     
     return prediction[0][0]
 
-# Load pre-trained StandardScaler
-scaler = joblib.load('scaler.pkl')
-
 # Streamlit application
 st.title("Dynamic Game Price Prediction App")
 
@@ -63,7 +73,6 @@ if submitted:
         'target_platform': target_platform,
         'total_sales': total_sales,
         'initial_price': initial_price,
-        'revenue': revenue,
         'avg_market_price': avg_market_price,
         'market_saturation': market_saturation / 100,  # Convert to 0-1 scale
         'user_rating': user_rating