Create app.py

app.py

@@ -0,0 +1,77 @@
+from transformers import *
+import gradio as gr
+tokenizer_s = AutoTokenizer.from_pretrained("oskrmiguel/mt5-simplification-spanish")
+model_s = AutoModelForSeq2SeqLM.from_pretrained("oskrmiguel/mt5-simplification-spanish")
+tokenizer = BertTokenizerFast.from_pretrained('mrm8488/bert2bert_shared-spanish-finetuned-summarization')
+model = EncoderDecoderModel.from_pretrained('mrm8488/bert2bert_shared-spanish-finetuned-summarization')
+model_q = AutoModelForSeq2SeqLM.from_pretrained("mrm8488/bert2bert-spanish-question-generation")
+tokenizer_q = AutoTokenizer.from_pretrained("mrm8488/bert2bert-spanish-question-generation")
+tokenizer_a = AutoTokenizer.from_pretrained("mrm8488/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es")
+model_a = AutoModelForQuestionAnswering.from_pretrained("mrm8488/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es")
+nlp_a = pipeline('question-answering', model='mrm8488/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es',
+    tokenizer=(
+        'mrm8488/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es',  
+        {"use_fast": False}
+    ))
+def generate_summary(text):
+   inputs = tokenizer([text], padding="max_length", truncation=True, max_length=64, return_tensors="pt")
+   input_ids = inputs.input_ids
+   attention_mask = inputs.attention_mask
+   output = model.generate(input_ids, attention_mask=attention_mask)
+   return tokenizer.decode(output[0], skip_special_tokens=True)
+def generate_simple_text(data):
+    outputs = []
+    for text in data.split("."):
+        inputs = tokenizer_s(text, max_length=1024, padding=True, truncation=True, return_tensors='pt')
+        output = model_s.generate(inputs['input_ids'], max_length=100)
+        outputs.append(['\n'.join([tokenizer_s.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in output])])
+    return outputs
+def generate_questions(data):
+  outputs = []
+  for text in data.split("."):
+    inputs_q = tokenizer_q(text, return_tensors="pt")
+    outputs_q = model_q.generate(inputs_q['input_ids'], max_length=100)
+    question = tokenizer_q.decode(outputs_q[0], skip_special_tokens=True)
+    outputs.append(question)
+  return outputs
+def generate_answer(question_texts, context_text):
+  return nlp_a({'question': question_texts,'context': context_text           }
+  )['answer']
+def generate_paragraph(data):
+  return list(filter(lambda x : x != '', data.split('\n')))
+contexto = gr.inputs.Textbox(lines=10, placeholder="Ingresa un cuento de niños")
+resultado = gr.outputs.HTML(label="Resultado")
+opciones = gr.inputs.CheckboxGroup(["Resumir", "Facil Lectura", "Generar Preguntas", "Ver Respuestas"])
+parrafo_longitud = gr.inputs.Slider(50, 500)
+def generate_question(contexto,opciones, parrafo_longitud):
+  parrafos = generate_paragraph(contexto)
+  resultado=""
+  resumen=[]
+  preguntas=[]
+  simples=[]
+  respuestas=[]
+  for i, text in enumerate(parrafos):
+    if len(text) < parrafo_longitud:
+      resumen.append(text)
+      if "Facil Lectura" in opciones:
+        simples.append(text)
+    else:
+      sumarize = generate_summary(text)
+      resumen.append(sumarize)
+      if "Generar Preguntas" in opciones:
+        questions = generate_questions(sumarize)
+        preguntas.append(str(i+1)+"-> "+questions[0])
+        if "Ver Respuestas" in opciones:
+          respuestas.append(str(i+1)+"-> "+generate_answer(questions[0], sumarize))
+      if "Facil Lectura" in opciones:
+        simples.append(generate_simple_text(sumarize)[0][0])
+  resultado += "<p><b>Resumen:</b> "+'<br/>'.join(resumen)+"</p>" 
+  resultado += "<p><b>Texto Simple:</b> "+'<br/>'.join(simples)+"</p>" 
+  resultado += "<p><b>Preguntas:</b> "+'<br/>'.join(preguntas)+"</p>" 
+  resultado += "<p><b>Respuestas:</b> "+'<br/>'.join(respuestas)+"</p>"
+  return resultado
+iface = gr.Interface(
+  fn=generate_question, 
+  inputs=[contexto, opciones, parrafo_longitud], 
+  outputs=resultado)
+iface.launch(debug=True)