Upload model.py

add in API deployment file

model.py

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+import numpy as np
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense
+from tensorflow.keras.optimizers import Adam
+
+import torch
+from fastapi import FastAPI
+from pydantic import BaseModel
+from flask import Flask, request, jsonify
+
+# Load model and tokenizer from Hugging Face Hub
+model_name = "model.joblib"
+import os
+import joblib
+# Define the file paths to load the model and scaler
+model_filename =  'model.joblib'
+scaler_filename =  'scaler.joblib'
+
+# Load the model and scaler using joblib
+loaded_model = joblib.load(model_filename)
+loaded_scaler = joblib.load(scaler_filename)
+
+# Initialize Flask app
+app = Flask(__name__)
+
+# Define the prediction route
+@app.route('/predict', methods=['POST'])
+def predict():
+    # Get the input JSON data
+    data = request.get_json()
+    new_df = pd.DataFrame(data)
+    # Assuming you used pd.get_dummies during training
+    new_df_encoded = pd.get_dummies(new_df, columns=['Creative Set', 'Game Type', 'Partner', 'Country'])
+
+
+    new_data_scaled = loaded_scaler.transform(new_df_encoded)
+    #print(new_data_scaled)
+
+    # Step 4: Make predictions for the new data
+    predictions = loaded_model.predict(new_data_scaled)
+
+    # Return the prediction as a JSON response
+    return jsonify({"prediction": predictions.tolist()})
+
+if __name__ == '__main__':
+    # Run the Flask app
+    app.run(debug=True, host='0.0.0.0', port=5000)