import streamlit as st
import numpy as np
import pandas as pd
import tensorflow as tf
from sklearn.preprocessing import OneHotEncoder, StandardScaler
import joblib

# Load the trained model
model = tf.keras.models.load_model('trained_game_price_model.h5')

# 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', 'targetPlatform', 'gamePlays', 'competitorPricing', 'currencyFluctuations'])
    
    # Apply OneHotEncoder for categorical features
    input_data_transformed = ohe.transform(input_data[['genre', 'targetPlatform']])
    
    # Ensure numerical features are 2D
    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))
    
    # Scale the features
    input_data_scaled = scaler.transform(input_data)
    
    return input_data_scaled

# Function to make a prediction
def make_prediction(input_data):
    # Preprocess the data for the model
    input_data_scaled = preprocess_input(input_data, ohe, scaler)
    
    # Make prediction
    prediction = model.predict(input_data_scaled)
    
    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("Game Price Prediction App")

st.write("""
### 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=50000)
    initial_price = st.number_input('Initial Price Offering ($)', min_value=0.0, value=29.99, format="%.2f")
    revenue = total_sales * initial_price
    competitor_pricing = st.number_input('Average Market Price for Similar Games ($)', 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 Price")

# Prediction logic
if submitted:
    # Prepare input data
    input_data = {
        'genre': genre,
        'targetPlatform': target_platform,
        'gamePlays': revenue,
        'competitorPricing': competitor_pricing,
        'currencyFluctuations': currency_fluctuations
    }
    
    # Make prediction
    predicted_price = make_prediction(input_data)
    
    # Display results
    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"- **Competitor Pricing**: ${competitor_pricing:.2f}")
    st.write(f"- **Currency Fluctuations**: {currency_fluctuations}")