Update app.py

app.py

@@ -2,15 +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
+#from transformers import pipeline
 
-text2text_generator = pipeline("text2text-generation")
+#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')
@@ -37,18 +37,18 @@ def fetch_answers(question, clincal_note ):
          if answer in passage_sentences[i]:
              evidence_sentence = evidence_sentence + " " + passage_sentences[i]
      
-         
-         #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)
-         
+                  
      model_input = f"question: {query} context: {evidence_sentence}"
-     output_answer = text2text_generator(model_input)[0]['generated_text']
+     #output_answer = text2text_generator(model_input)[0]['generated_text']
+     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)
+
      result_str = "# ANSWER "+str(count)+": "+ output_answer +"\n"  
      result_str = result_str + "REFERENCE: "+ evidence_sentence + "\n\n"
      top_5_query_paragraph_answer_list += result_str