Create app.py

app.py

@@ -0,0 +1,34 @@
+import pickle
+import numpy as np
+import gym
+from flask import Flask, render_template, request
+
+app = Flask(__name__)
+
+# Load the Q-learning model
+with open("q-learning.pkl", "rb") as f:
+    model = pickle.load(f)
+
+# Initialize the Taxi-v3 environment
+env = gym.make("Taxi-v3")
+
+@app.route('/')
+def home():
+    return render_template('index.html')
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    try:
+        # Get the state from the user input
+        state = int(request.form['state'])
+
+        # Get the action from the model
+        action = model.predict(np.array([state]))  # This assumes model.predict can work with the state as input
+
+        # Return the result
+        return render_template('index.html', state=state, action=action)
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+if __name__ == '__main__':
+    app.run(debug=True)