Update app.py

app.py

@@ -2,6 +2,11 @@ import gradio as gr
 import pysbd
 from transformers import pipeline
 from sentence_transformers import CrossEncoder
+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)
 
 sentence_segmenter = pysbd.Segmenter(language='en',clean=False)
 passage_retreival_model = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
@@ -21,15 +26,26 @@ def fetch_answers(question, clincal_note ):
     for query, passage in top_5_query_paragraph_list:
      passage_sentences = sentence_segmenter.segment(passage)
      answer = qa_model(question = query, context = passage)['answer']
-     
+     evidence_sentence = None
      for i in range(len(passage_sentences)):
          if answer.startswith('.') or answer.startswith(':'):
              answer = answer[1:].strip()
          if answer in passage_sentences[i]:
-             passage_sentences[i] = "**"+passage_sentences[i].strip()+"**"
+             evidence_sentence = passage_sentences[i]
+             break
      
-     result_str = "# RESULT NO: "+str(count)+"\n"  
-     result_str = result_str + " ".join(passage_sentences) + "\n\n"
+         model_input = f"question: {query} context: {evidence_sentence}"
+         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 = "# RESULT NO: "+str(count)+ output_answer +"\n"  
+     result_str = result_str + "REFERENCE: "+ evidence_sentence + "\n\n"
      top_5_query_paragraph_answer_list += result_str
      count+=1