first push

Author: arijitx

README.md

@@ -1,2 +1,17 @@
 # QnA-Bot
 QnA bot powered by CoQA + BERT
+
+## Installation
+
+	pip3 install torch
+	pip3 install flask
+	pip3 install git+https://github.com/huggingface/pytorch-pretrained-BERT.git
+
+	Download pretrained model from : https://drive.google.com/file/d/15HOJmRizBrgoPPVDHKpvSO2tNf0k-d8f/view?usp=sharing
+
+	python3 server.py
+
+
+## Running
+
+![alt text](https://raw.githubusercontent.com/username/projectname/branch/path/to/img.png)

infer.py

@@ -0,0 +1,157 @@
+import argparse
+import collections
+import json
+import logging
+import math
+import os
+import random
+import sys
+from io import open
+import time
+
+import numpy as np
+import torch
+from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,TensorDataset)
+from torch.utils.data.distributed import DistributedSampler
+from tqdm import tqdm, trange
+
+from pytorch_pretrained_bert.file_utils import PYTORCH_PRETRAINED_BERT_CACHE
+from modeling import BertForQuestionAnswering, BertConfig
+from pytorch_pretrained_bert.optimization import BertAdam, warmup_linear
+from pytorch_pretrained_bert.tokenization import (BasicTokenizer,BertTokenizer,whitespace_tokenize)
+
+if sys.version_info[0] == 2:
+	import cPickle as pickle
+else:
+	import pickle
+
+from infer_utils import *
+import spacy
+nlp = spacy.load('en_core_web_md')
+
+def is_whitespace(c):
+		if c == " " or c == "\t" or c == "\r" or c == "\n" or ord(c) == 0x202F:
+			return True
+		return False
+
+def is_punc(c):
+	if c in '?,.!()[]-_\'"':
+		return True
+	return False
+
+def punc_sep(s):
+	tokens = []
+	is_prev_white = True
+	for c in s:
+		if is_whitespace(c):
+			is_prev_white = True
+		else:	
+			if is_punc(c):
+				tokens.append(c)
+				is_prev_white = True
+			else:
+				if is_prev_white:
+					is_prev_white = False
+					tokens.append(c)
+				else:
+					tokens[-1]+=c
+	return ' '.join(tokens)
+
+def str_to_coqa_example(contenxt, question, prev_ques, prev_answ):
+	paragraph_text = contenxt
+	doc_tokens = []
+	char_to_word_offset = []
+	prev_is_whitespace = True
+	for c in paragraph_text:
+		if is_whitespace(c):
+			prev_is_whitespace = True
+		else:
+			if prev_is_whitespace:
+				doc_tokens.append(c)
+				prev_is_whitespace = False
+			else:
+				doc_tokens[-1] += c
+
+		char_to_word_offset.append(len(doc_tokens) - 1)
+
+	question_text = question
+
+	example = CoQAExample(
+				qas_id='random',
+				question_text=question_text,
+				doc_tokens=doc_tokens,
+				orig_answer_text="",
+				start_position=0,
+				end_position=0,
+				is_impossible=False,
+				is_yes= False,
+				is_no=False,
+				answer_span="",
+				prev_ques=prev_ques,
+				prev_answ=prev_answ)
+	return example
+
+class InferCoQA():
+	def __init__(self, model_path, lower_case = True):
+		self.model_path = model_path
+		self.tokenizer = BertTokenizer.from_pretrained(model_path, do_lower_case=lower_case)
+		self.model = BertForQuestionAnswering.from_pretrained(model_path)
+		self.model.cuda()
+		self.model.eval()
+
+	def predict(self, contenxt, question, prev_ques, prev_answ):
+		t = time.time()
+		coqa_example = str_to_coqa_example(contenxt, question, prev_ques, prev_answ)
+		coqa_features = convert_examples_to_features([coqa_example], self.tokenizer, max_seq_length=512,doc_stride=128, max_query_length=100, is_training=False)
+		
+		all_input_ids = torch.tensor([f.input_ids for f in coqa_features], dtype=torch.long)
+		all_input_mask = torch.tensor([f.input_mask for f in coqa_features], dtype=torch.long)
+		all_segment_ids = torch.tensor([f.segment_ids for f in coqa_features], dtype=torch.long)
+		all_example_index = torch.arange(all_input_ids.size(0), dtype=torch.long)
+		coqa_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_example_index)
+		
+		coqa_sampler = SequentialSampler(coqa_data)
+		coqa_dataloader = DataLoader(coqa_data, sampler=coqa_sampler, batch_size=1)
+		all_results = []
+		for input_ids, input_mask, segment_ids, example_indices in coqa_dataloader:
+			input_ids = input_ids.cuda()
+			input_mask = input_mask.cuda()
+			segment_ids = segment_ids.cuda()
+
+			
+			with torch.no_grad():
+				score = self.model(input_ids, segment_ids, input_mask)
+
+			coqa_feature = coqa_features[example_indices[0].item()]
+			unique_id = int(coqa_feature.unique_id)
+			all_results.append(RawResult(unique_id=unique_id,score=score[0].cpu(),length=input_ids.size(1)))
+
+		output_prediction_file = "predictions.json"
+		output_nbest_file = "nbest_predictions.json"
+		output_null_log_odds_file = "null_odds.json"
+		write_predictions([coqa_example], coqa_features, all_results,
+					1, 100,
+					True, output_prediction_file,
+					output_nbest_file, output_null_log_odds_file, False,
+					False, 0.0)
+		os.remove(output_nbest_file)
+		res = json.loads(open(output_prediction_file).read())['random']
+		os.remove(output_prediction_file)
+		print('inference time :',time.time() - t )
+		return res
+
+# iq = InferCoQA('coqa_ynu_history_1')
+# print('done loading model ..')
+# context = input("Context : ")
+
+
+# prev_q = ""
+# prev_a = ""
+# while True:
+# 	q = input("Question : ")
+# 	a = iq.predict(context,q,prev_q,prev_a)
+# 	print("Answer :",a)
+# 	prev_q = q
+# 	prev_a = a
+
+