Update app.py

app.py

@@ -8,20 +8,16 @@ 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, ohe, scaler):
     # Convert input into DataFrame for processing
-    input_data = pd.DataFrame([data], columns=['genre', 'target_platform', 'total_sales', 'initial_price', 'avg_market_price', 'market_saturation', 'user_rating'])
+    input_data = pd.DataFrame([data], columns=['genre', 'targetPlatform', 'gamePlays', 'competitorPricing', 'currencyFluctuations'])
     
     # Apply OneHotEncoder for categorical features
-    input_data_transformed = ohe.transform(input_data[['genre', 'target_platform']])
+    input_data_transformed = ohe.transform(input_data[['genre', 'targetPlatform']])
     
     # Ensure numerical features are 2D
-    numerical_features = input_data[['total_sales', 'initial_price', 'avg_market_price', 'market_saturation', 'user_rating']].values
+    numerical_features = input_data[['gamePlays', 'competitorPricing', 'currencyFluctuations']].values.reshape(1, -1)
     
     # Merge with numerical features
     input_data = np.hstack((input_data_transformed.toarray(), numerical_features))
@@ -41,73 +37,49 @@ def make_prediction(input_data):
     
     return prediction[0][0]
 
+# 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
+
 # Streamlit application
-st.title("Dynamic Game Price Prediction App")
+st.title("Game Price Prediction App")
 
 st.write("""
-### Enter the game details below to predict its optimal price.
-This model considers actual sales data and market trends for more accurate pricing.
+### Enter the game details below to predict its price.
 """)
 
 # Game details form
 with st.form("game_details_form"):
     genre = st.selectbox('Genre', ['Action', 'RPG', 'Puzzle', 'Adventure', 'Simulation', 'Strategy', 'Horror', 'Fighting', 'Sports', 'Racing', 'Casual', 'MOBA', 'Sandbox'])
     target_platform = st.selectbox('Platform', ['PC', 'PlayStation', 'Xbox', 'Mobile', 'Switch', 'Nintendo 3DS', 'VR', 'Web'])
-    total_sales = st.number_input('Total Sales (units)', min_value=0, value=10000)
-    initial_price = st.number_input('Initial Price Offering ($)', min_value=0.0, value=29.99, format="%.2f")
-    revenue = total_sales * initial_price
-    st.write(f"Current Revenue: ${revenue:.2f}")
-    
-    avg_market_price = st.number_input('Average Market Price for Similar Games ($)', min_value=0.0, value=24.99, format="%.2f")
-    market_saturation = st.slider('Market Saturation (0-100%)', min_value=0, max_value=100, value=50)
-    user_rating = st.slider('User Rating (0-5 stars)', min_value=0.0, max_value=5.0, value=4.0, step=0.1)
-    
+    game_plays = st.number_input('Total Sales (units)', min_value=0, value=50000)
+    competitor_pricing = st.number_input('Competitor Pricing', min_value=0.0, value=30.0, format="%.2f")
+    currency_fluctuations = st.number_input('Currency Fluctuations', min_value=0.5, max_value=1.5, value=1.0, format="%.2f")
+
     # Submit button
-    submitted = st.form_submit_button("Predict Optimal Price")
+    submitted = st.form_submit_button("Predict Price")
 
 # Prediction logic
 if submitted:
     # Prepare input data
     input_data = {
         'genre': genre,
-        'target_platform': target_platform,
-        'total_sales': total_sales,
-        'initial_price': initial_price,
-        'avg_market_price': avg_market_price,
-        'market_saturation': market_saturation / 100,  # Convert to 0-1 scale
-        'user_rating': user_rating
+        'targetPlatform': target_platform,
+        'gamePlays': game_plays,
+        'competitorPricing': competitor_pricing,
+        'currencyFluctuations': currency_fluctuations
     }
     
     # Make prediction
     predicted_price = make_prediction(input_data)
     
     # Display results
-    st.write(f"### Predicted Optimal Price: ${predicted_price:.2f}")
+    st.write(f"### Predicted Game Price: ${predicted_price:.2f}")
     
     # Show the input details for reference
     st.write("#### Input Details:")
     st.write(f"- **Genre**: {genre}")
     st.write(f"- **Platform**: {target_platform}")
-    st.write(f"- **Total Sales**: {total_sales}")
-    st.write(f"- **Initial Price**: ${initial_price:.2f}")
-    st.write(f"- **Current Revenue**: ${revenue:.2f}")
-    st.write(f"- **Avg. Market Price**: ${avg_market_price:.2f}")
-    st.write(f"- **Market Saturation**: {market_saturation}%")
-    st.write(f"- **User Rating**: {user_rating} stars")
-    
-    # Provide some insights
-    price_difference = predicted_price - initial_price
-    if abs(price_difference) < 1:
-        st.write("The predicted optimal price is close to your initial price. Your pricing strategy seems to be aligned with the market.")
-    elif price_difference > 0:
-        st.write(f"The model suggests increasing your price by ${price_difference:.2f}. This might help maximize revenue, but consider the potential impact on sales volume.")
-    else:
-        st.write(f"The model suggests decreasing your price by ${-price_difference:.2f}. This might help increase sales volume and overall revenue.")
-
-    st.write("Remember, this is a prediction based on the provided data. Always consider other factors like marketing strategies, seasonal trends, and your specific game's unique value proposition when making pricing decisions.")
-
-# Add a note about the dynamic nature of the marketplace
-st.write("""
-#### Note on Marketplace Dynamics
-This prediction model takes into account current market trends and your game's performance. As the marketplace is dynamic, it's recommended to periodically reassess your pricing strategy using updated sales data and market information.
-""")
+    st.write(f"- **Game Plays**: {game_plays}")
+    st.write(f"- **Competitor Pricing**: ${competitor_pricing:.2f}")
+    st.write(f"- **Currency Fluctuations**: {currency_fluctuations}")