Update app.py

app.py

@@ -1,42 +1,44 @@
+## app.py ##
 import gradio as gr
 import spacy
-from transformers import GPT2Tokenizer, GPT2LMHeadModel
+from transformers import pipeline
 
 nlp = spacy.load('es_core_news_sm')
-
-# Load pre-trained model tokenizer (vocabulary)
-tokenizer = GPT2Tokenizer.from_pretrained('datificate/gpt-2-small-spanish')
-
-# Load pre-trained model (weights)
-model = GPT2LMHeadModel.from_pretrained('datificate/gpt-2-small-spanish')
+text_generator = pipeline('text-generation', model='gpt2')
 
 pos_tags = ['ADJ', 'ADP', 'ADV', 'AUX', 'CONJ', 'DET', 'INTJ', 'NOUN', 'NUM', 'PART', 'PRON', 'PROPN', 'PUNCT', 'SCONJ', 'SYM', 'VERB', 'X']
 
-sentence = ""
-tagged_words = []
+sentence_state = {'sentence': '', 'tagged_words': []}
 
 def generate_sentence():
-    global sentence, tagged_words
-    # We will generate the text manually to control the special tokens
-    input_ids = tokenizer.encode('', return_tensors='pt')
-    output = model.generate(input_ids, max_length=50)
-    sentence = tokenizer.decode(output[0], skip_special_tokens=True)
+    result = text_generator('')[0]
+    sentence = result['generated_text']
     tagged_words = analyze_sentence(sentence)
+    sentence_state['sentence'] = sentence
+    sentence_state['tagged_words'] = tagged_words
     return sentence, [word for word, _ in tagged_words]
 
 def analyze_sentence(sentence):
     doc = nlp(sentence)
-    tagged_words = [(token.text, token.pos_) for token in doc]
-    return tagged_words
+    return [(token.text, token.pos_) for token in doc]
 
 def check_answer(*args):
-    correct_answer = [tag for word, tag in tagged_words]
+    correct_answer = [tag for word, tag in sentence_state['tagged_words']]
     user_answer = list(args)
     if user_answer == correct_answer:
         return 'Correcto!'
     else:
         return 'Incorrecto. La respuesta correcta es: ' + str(correct_answer)
 
-iface = gr.Interface(fn=generate_sentence, inputs='button', outputs=['textbox', 'dynamic'])
-iface.add_interface(fn=check_answer, inputs=gr.inputs.Dynamic(type="dropdown", choices=pos_tags, label='Word Tags'), outputs='textbox')
+def game_flow():
+    sentence, words = generate_sentence()
+    answer = check_answer(*gr.inputs)
+    return sentence, words, answer
+
+iface = gr.Interface(fn=game_flow, 
+                     inputs=[gr.inputs.Button(label='Generate Sentence')] + 
+                            [gr.inputs.Dropdown(choices=pos_tags, label=f'Word {i+1}') for i in range(len(sentence_state['tagged_words']))],
+                     outputs=[gr.outputs.Textbox(label='Sentence'),
+                              gr.outputs.Textbox(label='Words'),
+                              gr.outputs.Textbox(label='Result')])
 iface.launch()