Create app.py

app.py

@@ -1,69 +0,0 @@
-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)
-
-#from transformers import pipeline
-
-#text2text_generator = pipeline("text2text-generation", model = "gpt2")
-
-sentence_segmenter = pysbd.Segmenter(language='en',clean=False)
-passage_retreival_model = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
-qa_model = pipeline("question-answering",'a-ware/bart-squadv2')
-
-def fetch_answers(question, clincal_note ):
-    clincal_note_paragraphs = clincal_note.splitlines()
-    query_paragraph_list = [(question, para) for para in clincal_note_paragraphs if len(para.strip()) > 0 ]
-    
-    scores = passage_retreival_model.predict(query_paragraph_list)
-    top_5_indices = scores.argsort()[-5:]
-    top_5_query_paragraph_list = [query_paragraph_list[i] for i in top_5_indices ]
-    top_5_query_paragraph_list.reverse()
-    
-    top_5_query_paragraph_answer_list = ""
-    count = 1
-    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 = ""
-     for i in range(len(passage_sentences)):
-         if answer.startswith('.') or answer.startswith(':'):
-             answer = answer[1:].strip()
-         if answer in passage_sentences[i]:
-             evidence_sentence = evidence_sentence + " " + passage_sentences[i]
-     
-                  
-     model_input = f"question: {query} context: {evidence_sentence}"
-     #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
-     count+=1
-     
-    return top_5_query_paragraph_answer_list
-
-demo = gr.Interface(
-    fn=fetch_answers,
-    #take input as real time audio and use OPENAPI whisper for S2T
-    #clinical note upload as file (.This is an example of simple text. or doc/docx file)
-    inputs=[gr.Textbox(lines=2, label='Question', show_label=True),
-            gr.Textbox(lines=10, label='Document Text', show_label=True)],
-    outputs="markdown",
-    examples='.',
-    title='Document Question Answering System with Evidence from document'
-)
-demo.launch()