Update app.py

app.py

@@ -1,51 +1,28 @@
-## app.py ##
-import gradio as gr
-import spacy
 from transformers import pipeline
+from gradio import Interface
+import gradio as gr
 
-nlp = spacy.load('es_core_news_sm')
-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']
-pos_tags_es = ['ADJ', 'PREP', 'ADV', 'AUX', 'CONJ', 'DET', 'INTJ', 'SUST', 'NUM', 'PART', 'PRON', 'PROPN', 'PUNCT', 'SCONJ', 'SYM', 'VERBO', 'X']
-
-# Store the sentence state globally
-sentence_state = {'sentence': '', 'tagged_words': [], 'tagged_words_es': []}
-
-# Generate a sentence and analyze it
-def generate_and_analyze():
-    result = text_generator('', max_length=10, do_sample=True, language='es')[0]
-    sentence = result['generated_text']
-    doc = nlp(sentence)
-    tagged_words = [(token.text, token.pos_) for token in doc]
-    tagged_words_es = [(token.text, pos_tags_es[pos_tags.index(token.pos_)]) for token in doc]
-    sentence_state['sentence'] = sentence
-    sentence_state['tagged_words'] = tagged_words
-    sentence_state['tagged_words_es'] = tagged_words_es
-    return sentence, tagged_words, tagged_words_es
-
-# Initialize the game
-generate_and_analyze()
+# Create a dictionary of models
+MODELS = {
+    "GPT-2": "gpt2",
+    "mGPT": "ai-forever/mGPT"
+}
 
-# The game logic
-def game_flow(start_game, *args):
-    if start_game == 'Empezar':
-        sentence, _, _ = generate_and_analyze()
-        return sentence, '', '', ''
-    else:
-        correct_answer = [tag for _, tag in sentence_state['tagged_words_es']]
-        user_answer = list(args)
-        if user_answer == correct_answer:
-            return sentence_state['sentence'], ' '.join([f'{word} ({tag})' for word, tag in sentence_state['tagged_words_es']]), '¡Correcto!', ''
-        else:
-            correction = ' '.join([f'{word} es {tag}' for word, tag in sentence_state['tagged_words_es']])
-            return sentence_state['sentence'], ' '.join([f'{word} ({tag})' for word, tag in sentence_state['tagged_words_es']]), 'Incorrecto.', 'La respuesta correcta es: ' + correction
+# Define your function
+def generate_and_analyze(model_name, input_text):
+    # Load the model from the dictionary using the selected model name
+    model = MODELS[model_name]
+    text_generator = pipeline('text-generation', model=model)
+    result = text_generator(input_text, max_length=10, do_sample=True)[0]
+    return result['generated_text']
 
-iface = gr.Interface(fn=game_flow, 
-                     inputs=[gr.inputs.Textbox(label='Escribe "Empezar" para iniciar el juego')] + 
-                            [gr.inputs.Dropdown(choices=pos_tags_es, label=f'Función de la palabra {i+1}') for i in range(5)], 
-                     outputs=[gr.outputs.Textbox(label='Frase generada'),
-                              gr.outputs.Textbox(label='Funciones de las palabras'),
-                              gr.outputs.Textbox(label='Evaluación'),
-                              gr.outputs.Textbox(label='Corrección')])
+# Define your interface
+iface = gr.Interface(
+    fn=generate_and_analyze, 
+    inputs=[
+        gr.inputs.Dropdown(choices=list(MODELS.keys()), label="Model"),
+        gr.inputs.Textbox(lines=2, label="Input Text")
+    ], 
+    outputs="text"
+)
 iface.launch()