Update app.py

app.py

@@ -1,30 +1,56 @@
-from flask import Flask, request, render_template
-from transformers import pipeline
+import gradio as gr
+from gradio import Interface
+from gradio.components import Textbox, Dropdown
 
-app = Flask(__name__)
+import spacy
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
 
-nlp = pipeline('sentiment-analysis')
-
-@app.route('/')
-def home():
-    return render_template('index.html')
+nlp = spacy.load('es_core_news_sm')
+tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
+model = GPT2LMHeadModel.from_pretrained("gpt2")
 
-@app.route('/predict',methods=['POST'])
-def predict():
-    if request.method == 'POST':
-        message = request.form['message']
-        prediction = nlp(message)
-        return render_template('index.html', prediction_text=prediction)
+pos_map = {
+    'sustantivo': 'NOUN',
+    'verbo': 'VERB',
+    'adjetivo': 'ADJ',
+    'artículo': 'DET'
+}
 
-if __name__ == "__main__":
-    app.run(debug=True)
-from transformers import GPT3LMHeadModel, GPT2Tokenizer
+def identify_pos(sentence):
+    doc = nlp(sentence)
+    pos_tags = [(token.text, token.pos_) for token in doc]
+    return pos_tags
 
-tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
-model = GPT3LMHeadModel.from_pretrained("gpt3")
+def game_logic(sentence, user_word, user_pos):
+    correct_answers = identify_pos(sentence)
+    user_pos = pos_map[user_pos.lower()]
+    for word, pos in correct_answers:
+        if word == user_word:
+            if pos.lower() == user_pos.lower():
+                return True, f'¡Correcto! "{user_word}" es un {user_pos}.'
+            else:
+                return False, f'Incorrecto. "{user_word}" no es un {user_pos}, es un {pos}.'
+    return False, f'La palabra "{user_word}" no se encuentra en la frase.'
 
-def get_response(prompt):
-    inputs = tokenizer.encode(prompt, return_tensors="pt")
+def generate_hint(sentence, user_word, user_pos):
+    inputs = tokenizer.encode(f'Give a hint about the {user_pos} in the sentence "{sentence}"', return_tensors='pt')
     outputs = model.generate(inputs, max_length=150, num_return_sequences=1, no_repeat_ngram_size=2)
-    response = tokenizer.decode(outputs[:, inputs.shape[-1]:][0], skip_special_tokens=True)
-    return response
+    hint = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return hint
+
+def main(sentence, user_word, user_pos):
+    if sentence and user_word and user_pos and user_pos != 'Selecciona una función gramatical...':
+        correct, message = game_logic(sentence, user_word, user_pos)
+        hint = generate_hint(sentence, user_word, user_pos)
+        return message, hint
+    else:
+        return 'Por favor, introduce una frase, una palabra y selecciona una función gramatical válida (sustantivo, verbo, adjetivo, artículo).', ''
+
+iface = Interface(fn=main, 
+                  inputs=[
+                      Textbox(lines=2, placeholder='Introduce una frase aquí...'),
+                      Textbox(lines=1, placeholder='Introduce una palabra aquí...'),
+                      Dropdown(choices=['Selecciona una función gramatical...', 'sustantivo', 'verbo', 'adjetivo', 'artículo'])
+                  ], 
+                  outputs=[Textbox(), Textbox()])
+iface.launch()