Revert model in generator

generator.py

@@ -36,8 +36,8 @@ import streamlit as st
 def load_model():
   hfm = pickle.load(open('hfmodel.sav','rb'))
   hft = T5TokenizerFast.from_pretrained("t5-base")
-  model = pickle.load(open('electra_model.sav','rb'))
-  tok = et.from_pretrained("mrm8488/electra-small-finetuned-squadv2")
+  model = pickle.load(open('model.sav','rb'))
+  tok = et.from_pretrained("ahotrod/albert_xxlargev1_squad2_512 ")
   # return hfm, hft,tok, model
   return hfm, hft,tok, model
 
@@ -67,29 +67,29 @@ def run_model(input_string, **generator_args):
 # al_tokenizer = pickle.load(open('models/al_tokenizer.sav', 'rb'))
 def QA(question, context):
   # model_name="deepset/electra-base-squad2"
-  nlp = pipeline("question-answering",model=model,tokenizer = tok)
-  format = {
-      'question':question,
-      'context':context
-  }
-  res = nlp(format)
-  output = f"{question}\n{string.capwords(res['answer'])}\n"
-  return output
-  # inputs = tokenizer(question, context, return_tensors="pt")
-  # # Run the model, the deepset way
-  # with torch.no_grad():
-  #   output = model(**inputs)
-  # start_score = output.start_logits
-  # end_score = output.end_logits
-  # #Get the rel scores for the context, and calculate the most probable begginign using torch
-  # start = torch.argmax(start_score)
-  # end = torch.argmax(end_score)
-  # #cinvert tokens to strings
-  # # output = tokenizer.decode(input_ids[start:end+1], skip_special_tokens=True)
-  # predict_answer_tokens = inputs.input_ids[0, start : end + 1]
-  # output = tokenizer.decode(predict_answer_tokens, skip_special_tokens=True)
-  # output = string.capwords(output)
-  # return f"Q. {question} \n Ans. {output}"
+  # nlp = pipeline("question-answering",model=model,tokenizer = tok)
+  # format = {
+  #     'question':question,
+  #     'context':context
+  # }
+  # res = nlp(format)
+  # output = f"{question}\n{string.capwords(res['answer'])}\n"
+  # return output
+  inputs = tokenizer(question, context, return_tensors="pt")
+  # Run the model, the deepset way
+  with torch.no_grad():
+    output = model(**inputs)
+  start_score = output.start_logits
+  end_score = output.end_logits
+  #Get the rel scores for the context, and calculate the most probable begginign using torch
+  start = torch.argmax(start_score)
+  end = torch.argmax(end_score)
+  #cinvert tokens to strings
+  # output = tokenizer.decode(input_ids[start:end+1], skip_special_tokens=True)
+  predict_answer_tokens = inputs.input_ids[0, start : end + 1]
+  output = tokenizer.decode(predict_answer_tokens, skip_special_tokens=True)
+  output = string.capwords(output)
+  return f"Q. {question} \n Ans. {output}"
 # QA("What was the first C program","The first prgram written in C was Hello World")
 
 def gen_question(inputs):