Update app.py

app.py

@@ -1,7 +1,85 @@
 from fastapi import FastAPI
+from pydantic import BaseModel
+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')
+
+# Load pre-trained OneHotEncoder and StandardScaler
+ohe = joblib.load('ohe.pkl')
+scaler = joblib.load('scaler.pkl')
+
+# FastAPI app
 app = FastAPI()
 
+# Pydantic model for input validation
+class GameDetails(BaseModel):
+    genre: str
+    targetPlatform: str
+    gamePlays: int
+    competitorPricing: float
+    currencyFluctuations: float
+
+# 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]
+
+# API endpoint for price prediction
+@app.post("/predict_price/")
+def predict_price(game_details: GameDetails):
+    # Prepare input data for prediction
+    input_data = {
+        'genre': game_details.genre,
+        'targetPlatform': game_details.targetPlatform,
+        'gamePlays': game_details.gamePlays,
+        'competitorPricing': game_details.competitorPricing,
+        'currencyFluctuations': game_details.currencyFluctuations
+    }
+    
+    # Make prediction
+    predicted_price = make_prediction(input_data)
+    
+    # Return the predicted price
+    return {
+        "predicted_price": f"${predicted_price:.2f}",
+        "input_details": {
+            "genre": game_details.genre,
+            "platform": game_details.targetPlatform,
+            "game_plays": game_details.gamePlays,
+            "competitor_pricing": game_details.competitorPricing,
+            "currency_fluctuations": game_details.currencyFluctuations
+        }
+    }
+
 @app.get("/")
 def greet_json():
     return {"Hello": "World!"}