Creation of an api module with a rest endpoint /suggest which receives a list of words and states and return a suggestion

a3c/play.py

@@ -1,7 +1,16 @@
+import os
 import torch
+from dotenv import load_dotenv
+from wordle_env.state import update_from_mask
 from .net import GreedyNet
 from .utils import v_wrap
-from wordle_env.state import update_from_mask
+
+
+def get_play_model_path():
+    load_dotenv()
+    model_name = os.getenv('RS_WORDLE_MODEL_NAME')
+    model_checkpoint_dir = os.path.join('checkpoints', 'best_models')
+    return os.path.join(model_checkpoint_dir, model_name)
 
 
 def suggest(

api_rest/api.py

@@ -0,0 +1,39 @@
+from a3c.play import get_play_model_path, suggest
+from flask import Flask, request, jsonify
+from wordle_env.words import target_vocabulary
+from wordle_env.wordle import get_env
+
+app = Flask(__name__)
+
+
+def validate_params(words, states):
+    # Check if the input lists are valid (i.e. all elements have length 5 and numbers are between 0 and 2 inclusive)
+    if not all(len(w) == 5 and w in target_vocabulary for w in words):
+        return True, 'Invalid input, words must be 5 characters long and must be an eligible word'
+
+    if not all(len(n) == 5 and all(c.isdigit() and 0 <= int(c) <= 2 for c in n) for n in states):
+        return True, 'Invalid input, states must be 5 characters long and the numbers between 0 and 2 inclusive'
+
+    return False, ''
+
+
+@app.route('/suggest', methods=['GET'])
+def get_suggestion():
+    # Get the list of words and list of number strings from the request
+    words = [word.strip().upper()
+             for word in request.args.get('words').split(',')]
+    states = [state.strip() for state in request.args.get('states').split(',')]
+    print(states)
+    error, msge = validate_params(words, states)
+    if error:
+        return jsonify({'error': msge}), 400
+
+    env = get_env()
+    model_path = get_play_model_path()
+    # Call the suggest function with the input lists and return the result
+    suggestion = suggest(env, words, states, model_path)
+    return jsonify({'suggestion': suggestion})
+
+
+if __name__ == '__main__':
+    app.run(debug=True)

main.py

@@ -1,15 +1,13 @@
 #!/usr/bin/env python3
 
 import argparse
-import gym
 import os
-import sys
 import time
 import matplotlib.pyplot as plt
 from a3c.train import train
 from a3c.eval import evaluate, evaluate_checkpoints
 from a3c.play import suggest
-from wordle_env.wordle import WordleEnvBase
+from wordle_env.wordle import get_env
 
 
 def training_mode(args, env, model_checkpoint_dir):
@@ -88,6 +86,6 @@ if __name__ == "__main__":
 
     args = parser.parse_args()
     env_id = args.enviroment
-    env = gym.make(env_id)
+    env = get_env(env_id)
     model_checkpoint_dir = os.path.join(args.models_dir, env.unwrapped.spec.id)
     args.func(args, env, model_checkpoint_dir)

requirements.txt

@@ -4,3 +4,4 @@ gym
 matplotlib
 selenium
 torch
+flask

rs_wordle_player/rs_wordle_player.py

@@ -1,23 +1,9 @@
-import gym
-import os
-from a3c.play import suggest
-from dotenv import load_dotenv
+from a3c.play import get_play_model_path, suggest
+from wordle_env.wordle import get_env
 from .firebase_connector import FirebaseConnector
 from .selenium_player import SeleniumPlayer
 
 
-def get_model_path():
-    load_dotenv()
-    model_name = os.getenv('RS_WORDLE_MODEL_NAME')
-    model_checkpoint_dir = os.path.join('checkpoints', 'best_models')
-    return os.path.join(model_checkpoint_dir, model_name)
-
-
-def get_env():
-    env_id = 'WordleEnvFull-v0'
-    return gym.make(env_id)
-
-
 def get_attempts(fb_connector):
     attempts = fb_connector.today_user_attempts()
     words = []
@@ -51,7 +37,7 @@ def play_game(player, fb_connector, env, model_path):
 def play():
     fb = FirebaseConnector()
     player = SeleniumPlayer()
-    model_path = get_model_path()
+    model_path = get_play_model_path()
     env = get_env()
     goal_word = fb.today_word()
     if goal_word and len(goal_word) == 5:

wordle_env/wordle.py

@@ -18,6 +18,10 @@ def _load_words(limit: Optional[int] = None, complete: Optional[bool] = False) -
     return words if not limit else words[:limit]
 
 
+def get_env(env_id='WordleEnvFull-v0'):
+    return gym.make(env_id)
+
+
 class WordleEnvBase(gym.Env):
     """
     Actions: