Update app.py

app.py

@@ -2,11 +2,15 @@ import gradio as gr
 import pysbd
 from transformers import pipeline
 from sentence_transformers import CrossEncoder
-from  transformers  import  AutoTokenizer, AutoModelWithLMHead, pipeline
+#from  transformers  import  AutoTokenizer, AutoModelWithLMHead, pipeline
 
-model_name = "MaRiOrOsSi/t5-base-finetuned-question-answering"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelWithLMHead.from_pretrained(model_name)
+#model_name = "MaRiOrOsSi/t5-base-finetuned-question-answering"
+#tokenizer = AutoTokenizer.from_pretrained(model_name)
+#model = AutoModelWithLMHead.from_pretrained(model_name)
+
+from transformers import pipeline
+
+text2text_generator = pipeline("text2text-generation")
 
 sentence_segmenter = pysbd.Segmenter(language='en',clean=False)
 passage_retreival_model = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
@@ -34,14 +38,15 @@ def fetch_answers(question, clincal_note ):
              evidence_sentence = passage_sentences[i]
      
          model_input = f"question: {query} context: {evidence_sentence}"
-         encoded_input = tokenizer([model_input],
-                                     return_tensors='pt',
-                                     max_length=512,
-                                     truncation=True)
+         #encoded_input = tokenizer([model_input],
+         #                            return_tensors='pt',
+         #                            max_length=512,
+         #                            truncation=True)
                                      
-         output = model.generate(input_ids = encoded_input.input_ids,
-                                    attention_mask = encoded_input.attention_mask)
-         output_answer = tokenizer.decode(output[0], skip_special_tokens=True)
+         #output = model.generate(input_ids = encoded_input.input_ids,
+         #                           attention_mask = encoded_input.attention_mask)
+         #output_answer = tokenizer.decode(output[0], skip_special_tokens=True)
+         output_answer = text2text_generator(model_input)['generated_text']
          break
          
      result_str = "# ANSWER "+str(count)+": "+ output_answer +"\n"