Beleza

api/DataProcesser.py

@@ -53,13 +53,13 @@ def handle_classify(self, df, classifier):
 
  def get_pipeline(self, model_name):
  if model_name=="emotion_pipeline.pkl":
- df = pd.read_csv('src/assets/tweet_emotions.csv')
+ df = pd.read_csv('assets/tweet_emotions.csv')
  train_data, test_data, train_target, test_target = train_test_split(df['content'], df['sentiment'], test_size=0.2, shuffle=True)
  elif model_name=="hate_speech.pkl":
- df = pd.read_csv('src/assets/nb_hatespeech.csv', sep=';')
+ df = pd.read_csv('assets/nb_hatespeech.csv', sep=';')
  train_data, test_data, train_target, test_target = train_test_split(df['comment'], df['isHate'], test_size=0.2, shuffle=True)
  elif model_name=="text_classification_pipeline.pkl":
- df = pd.read_csv('src/assets/nb_news.csv')
+ df = pd.read_csv('assets/nb_news.csv')
  train_data, test_data, train_target, test_target = train_test_split(df['short_description'], df['category'], test_size=0.2, shuffle=True)
  else:
  with open(f'api/models/{model_name}', 'rb') as file:

api/app.py

@@ -79,15 +79,15 @@ def upload_file():
 
 @app.route('/get-classifiers', methods=["GET"])
 def get_classifiers():
- print(build_tree('..'))
+ print(build_tree('.'))
  classifiers = get_available_classifiers()
- return jsonify(build_tree('../..')) # should be classifiers but testing for now
+ return jsonify(classifiers)
 
 def build_tree(directory, indent='', d=0):
  """
  Recursively build directory tree structure as a string.
  """
- if d == 3:
+ if d == 6:
  return ''
 
  tree = indent + os.path.basename(directory) + '/' + '\n'

api/available_classifiers.py

@@ -5,11 +5,11 @@ def get_available_classifiers():
 
  # Verifica se o diretório 'models' existe
  if not model_folder:
- return []
+ return {}
 
  # Obtém a lista de arquivos no diretório 'models'
  model_files = os.listdir(model_folder)
- classifiers = {}
+ classifiers = {0 : "emotion_pipeline.pkl", 1 : "hate_speech.pkl", 2 : "text_classification_pipeline.pkl"}
 
  for file in model_files:
  classifiers[len(classifiers)] = file