add qa model

app.py

@@ -7,6 +7,8 @@ from transformers import BartForConditionalGeneration
 from transformers import BartTokenizer
 from transformers import PreTrainedModel
 from transformers import PreTrainedTokenizer
+from transformers import AutoTokenizer
+from transformers import AutoModelForSeq2SeqLM
 
 tokenizer = BartTokenizer.from_pretrained('facebook/bart-large-cnn')
 model = BartForConditionalGeneration.from_pretrained('facebook/bart-large-cnn')
@@ -94,9 +96,22 @@ def generate_summary(text):
     summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
     return summary
 
+# QA
+# Cargar el modelo para preguntas y respuestas
+qa_model_name = "MaRiOrOsSi/t5-base-finetuned-question-answering"
+qa_tokenizer = AutoTokenizer.from_pretrained(qa_model_name)
+qa_model = AutoModelForSeq2SeqLM.from_pretrained(qa_model_name)
 
-def generate_question_response(question):
-    return f'response: {question}'
+
+def generate_question_response(question, context):
+    # Formar el input para el modelo de preguntas y respuestas
+    input_text = f"question: {question} context: {context}"
+    encoded_input = qa_tokenizer(input_text, return_tensors='pt', max_length=1024, truncation=True)
+    output = qa_model.generate(input_ids=encoded_input['input_ids'], attention_mask=encoded_input['attention_mask'])
+    response_en = qa_tokenizer.decode(output[0], skip_special_tokens=True)
+    translator = Translator()
+    translated_response = translator.translate(response_en, dest='es').text
+    return f'Respuesta: {translated_response}'
 
 
 class SummarizerAndQA:
@@ -114,9 +129,9 @@ class SummarizerAndQA:
             self.summary = generate_summary(text)
             self.study_generated_questions = generate_questions(text)
 
-        if question != self.question:
+        if question != self.question and text != '':
             self.question = question
-            self.question_response = generate_question_response(question)
+            self.question_response = generate_question_response(question, text)
 
         return self.summary, self.study_generated_questions, self.question_response
 

requirements.txt

@@ -5,4 +5,5 @@ accelerate
 # We are using this fork since the orignal google library uses an old package version of httpx
 # which is not compatible with the last version of gradio
 googletrans-py
-sentencepiece
+sentencepiece
+protobuf