import re
import datetime
from typing import TypeVar, Dict, List, Tuple
import time
from itertools import compress
import pandas as pd
import numpy as np
# Model packages
import torch
from threading import Thread
from transformers import pipeline, TextIteratorStreamer
# Alternative model sources
from dataclasses import asdict, dataclass
# Langchain functions
from langchain.prompts import PromptTemplate
from langchain.vectorstores import FAISS
from langchain.retrievers import SVMRetriever
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.docstore.document import Document
# For keyword extraction
import nltk
nltk.download('wordnet')
from nltk.corpus import stopwords
from nltk.tokenize import RegexpTokenizer
from nltk.stem import WordNetLemmatizer
import keybert
# For Name Entity Recognition model
from span_marker import SpanMarkerModel
# For BM25 retrieval
from gensim.corpora import Dictionary
from gensim.models import TfidfModel, OkapiBM25Model
from gensim.similarities import SparseMatrixSimilarity
import gradio as gr
torch.cuda.empty_cache()
PandasDataFrame = TypeVar('pd.core.frame.DataFrame')
embeddings = None # global variable setup
vectorstore = None # global variable setup
model_type = None # global variable setup
max_memory_length = 0 # How long should the memory of the conversation last?
full_text = "" # Define dummy source text (full text) just to enable highlight function to load
model = [] # Define empty list for model functions to run
tokenizer = [] # Define empty list for model functions to run
## Highlight text constants
hlt_chunk_size = 15
hlt_strat = [" ", ".", "!", "?", ":", "\n\n", "\n", ","]
hlt_overlap = 4
## Initialise NER model ##
ner_model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-mbert-base-multinerd")
## Initialise keyword model ##
# Used to pull out keywords from chat history to add to user queries behind the scenes
kw_model = pipeline("feature-extraction", model="sentence-transformers/all-MiniLM-L6-v2")
if torch.cuda.is_available():
torch_device = "cuda"
gpu_layers = 0
else:
torch_device = "cpu"
gpu_layers = 0
print("Running on device:", torch_device)
threads = 8 #torch.get_num_threads()
print("CPU threads:", threads)
# Flan Alpaca Model parameters
temperature: float = 0.1
top_k: int = 3
top_p: float = 1
repetition_penalty: float = 1.3
flan_alpaca_repetition_penalty: float = 1.3
tinyllama_repetition_penalty: float = 1.5
last_n_tokens: int = 64
max_new_tokens: int = 512
seed: int = 42
reset: bool = False
stream: bool = True
threads: int = threads
batch_size:int = 256
context_length:int = 2048
sample = True
class CtransInitConfig_gpu:
def __init__(self, temperature=temperature,
top_k=top_k,
top_p=top_p,
repetition_penalty=repetition_penalty,
last_n_tokens=last_n_tokens,
max_new_tokens=max_new_tokens,
seed=seed,
reset=reset,
stream=stream,
threads=threads,
batch_size=batch_size,
context_length=context_length,
gpu_layers=gpu_layers):
self.temperature = temperature
self.top_k = top_k
self.top_p = top_p
self.repetition_penalty = repetition_penalty# repetition_penalty
self.last_n_tokens = last_n_tokens
self.max_new_tokens = max_new_tokens
self.seed = seed
self.reset = reset
self.stream = stream
self.threads = threads
self.batch_size = batch_size
self.context_length = context_length
self.gpu_layers = gpu_layers
# self.stop: list[str] = field(default_factory=lambda: [stop_string])
def update_gpu(self, new_value):
self.gpu_layers = new_value
class CtransInitConfig_cpu(CtransInitConfig_gpu):
def __init__(self):
super().__init__()
self.gpu_layers = 0
gpu_config = CtransInitConfig_gpu()
cpu_config = CtransInitConfig_cpu()
#@dataclass
#class CtransGenGenerationConfig:
# top_k: int = top_k
# top_p: float = top_p
# temperature: float = temperature
# repetition_penalty: float = tinyllama_repetition_penalty
# last_n_tokens: int = last_n_tokens
# seed: int = seed
# batch_size:int = batch_size
# threads: int = threads
# reset: bool = True
class CtransGenGenerationConfig:
def __init__(self, temperature=temperature,
top_k=top_k,
top_p=top_p,
repetition_penalty=repetition_penalty,
last_n_tokens=last_n_tokens,
seed=seed,
threads=threads,
batch_size=batch_size,
reset=True
):
self.temperature = temperature
self.top_k = top_k
self.top_p = top_p
self.repetition_penalty = repetition_penalty# repetition_penalty
self.last_n_tokens = last_n_tokens
self.seed = seed
self.threads = threads
self.batch_size = batch_size
self.reset = reset
# Vectorstore funcs
def docs_to_faiss_save(docs_out:PandasDataFrame, embeddings=embeddings):
print(f"> Total split documents: {len(docs_out)}")
vectorstore_func = FAISS.from_documents(documents=docs_out, embedding=embeddings)
'''
#with open("vectorstore.pkl", "wb") as f:
#pickle.dump(vectorstore, f)
'''
#if Path(save_to).exists():
# vectorstore_func.save_local(folder_path=save_to)
#else:
# os.mkdir(save_to)
# vectorstore_func.save_local(folder_path=save_to)
global vectorstore
vectorstore = vectorstore_func
out_message = "Document processing complete"
#print(out_message)
#print(f"> Saved to: {save_to}")
return out_message
# Prompt functions
def base_prompt_templates(model_type = "Flan Alpaca"):
#EXAMPLE_PROMPT = PromptTemplate(
# template="\nCONTENT:\n\n{page_content}\n\nSOURCE: {source}\n\n",
# input_variables=["page_content", "source"],
#)
CONTENT_PROMPT = PromptTemplate(
template="{page_content}\n\n",#\n\nSOURCE: {source}\n\n",
input_variables=["page_content"]
)
# The main prompt:
instruction_prompt_template_alpaca_quote = """### Instruction:
Quote directly from the SOURCE below that best answers the QUESTION. Only quote full sentences in the correct order. If you cannot find an answer, start your response with "My best guess is: ".
CONTENT: {summaries}
QUESTION: {question}
Response:"""
instruction_prompt_template_alpaca = """### Instruction:
### User:
Answer the QUESTION using information from the following CONTENT.
CONTENT: {summaries}
QUESTION: {question}
Response:"""
instruction_prompt_template_sheared_llama = """Answer the QUESTION using information from the following CONTENT.
CONTENT: {summaries}
QUESTION: {question}
Answer:"""
instruction_prompt_template_orca = """
### System:
You are an AI assistant that follows instruction extremely well. Help as much as you can.
### User:
Answer the QUESTION with a short response using information from the following CONTENT.
CONTENT: {summaries}
QUESTION: {question}
### Response:"""
instruction_prompt_mistral_orca = """<|im_start|>system\n
You are an AI assistant that follows instruction extremely well. Help as much as you can.
<|im_start|>user\n
Answer the QUESTION using information from the following CONTENT. Respond with short answers that directly answer the question.
CONTENT: {summaries}
QUESTION: {question}\n
Answer:<|im_end|>"""
instruction_prompt_tinyllama_orca = """<|im_start|>system\n
You are an AI assistant that follows instruction extremely well. Help as much as you can.
<|im_start|>user\n
Answer the QUESTION using information from the following CONTENT. Only quote text that directly answers the question and nothing more. If you can't find an answer to the question, respond with "Sorry, I can't find an answer to that question.".
CONTENT: {summaries}
QUESTION: {question}\n
Answer:<|im_end|>"""
if model_type == "Flan Alpaca":
INSTRUCTION_PROMPT=PromptTemplate(template=instruction_prompt_template_alpaca, input_variables=['question', 'summaries'])
elif model_type == "Orca Mini":
INSTRUCTION_PROMPT=PromptTemplate(template=instruction_prompt_template_orca, input_variables=['question', 'summaries'])
return INSTRUCTION_PROMPT, CONTENT_PROMPT
def generate_expanded_prompt(inputs: Dict[str, str], instruction_prompt, content_prompt, extracted_memory, vectorstore, embeddings): # ,
question = inputs["question"]
chat_history = inputs["chat_history"]
new_question_kworded = adapt_q_from_chat_history(question, chat_history, extracted_memory) # new_question_keywords,
docs_keep_as_doc, doc_df, docs_keep_out = hybrid_retrieval(new_question_kworded, vectorstore, embeddings, k_val = 5, out_passages = 1,
vec_score_cut_off = 1, vec_weight = 1, bm25_weight = 1, svm_weight = 1)#,
#vectorstore=globals()["vectorstore"], embeddings=globals()["embeddings"])
# Expand the found passages to the neighbouring context
docs_keep_as_doc, doc_df = get_expanded_passages(vectorstore, docs_keep_out, width=3)
if docs_keep_as_doc == []:
{"answer": "I'm sorry, I couldn't find a relevant answer to this question.", "sources":"I'm sorry, I couldn't find a relevant source for this question."}
# Build up sources content to add to user display
doc_df['meta_clean'] = [f"{' '.join(f'{k}: {v}' for k, v in d.items() if k != 'page_section')}" for d in doc_df['metadata']]
doc_df['content_meta'] = doc_df['meta_clean'].astype(str) + ".
" + doc_df['page_content'].astype(str)
modified_page_content = [f" SOURCE {i+1} - {word}" for i, word in enumerate(doc_df['page_content'])]
docs_content_string = ''.join(modified_page_content)
sources_docs_content_string = '
'.join(doc_df['content_meta'])#.replace(" "," ")#.strip()
instruction_prompt_out = instruction_prompt.format(question=new_question_kworded, summaries=docs_content_string)
print('Final prompt is: ')
print(instruction_prompt_out)
return instruction_prompt_out, sources_docs_content_string, new_question_kworded
def create_full_prompt(user_input, history, extracted_memory, vectorstore, embeddings, model_type):
#if chain_agent is None:
# history.append((user_input, "Please click the button to submit the Huggingface API key before using the chatbot (top right)"))
# return history, history, "", ""
print("\n==== date/time: " + str(datetime.datetime.now()) + " ====")
print("User input: " + user_input)
history = history or []
# Create instruction prompt
instruction_prompt, content_prompt = base_prompt_templates(model_type=model_type)
instruction_prompt_out, docs_content_string, new_question_kworded =\
generate_expanded_prompt({"question": user_input, "chat_history": history}, #vectorstore,
instruction_prompt, content_prompt, extracted_memory, vectorstore, embeddings)
history.append(user_input)
print("Output history is:")
print(history)
return history, docs_content_string, instruction_prompt_out
# Chat functions
def produce_streaming_answer_chatbot(history, full_prompt, model_type):
#print("Model type is: ", model_type)
if model_type == "Flan Alpaca":
# Get the model and tokenizer, and tokenize the user text.
model_inputs = tokenizer(text=full_prompt, return_tensors="pt", return_attention_mask=False).to(torch_device) # return_attention_mask=False was added
# Start generation on a separate thread, so that we don't block the UI. The text is pulled from the streamer
# in the main thread. Adds timeout to the streamer to handle exceptions in the generation thread.
streamer = TextIteratorStreamer(tokenizer, timeout=120., skip_prompt=True, skip_special_tokens=True)
generate_kwargs = dict(
model_inputs,
streamer=streamer,
max_new_tokens=max_new_tokens,
do_sample=sample,
repetition_penalty=repetition_penalty,
top_p=top_p,
temperature=temperature,
top_k=top_k
)
t = Thread(target=model.generate, kwargs=generate_kwargs)
t.start()
# Pull the generated text from the streamer, and update the model output.
start = time.time()
NUM_TOKENS=0
print('-'*4+'Start Generation'+'-'*4)
history[-1][1] = ""
for new_text in streamer:
if new_text == None: new_text = ""
history[-1][1] += new_text
NUM_TOKENS+=1
yield history
time_generate = time.time() - start
print('\n')
print('-'*4+'End Generation'+'-'*4)
print(f'Num of generated tokens: {NUM_TOKENS}')
print(f'Time for complete generation: {time_generate}s')
print(f'Tokens per secound: {NUM_TOKENS/time_generate}')
print(f'Time per token: {(time_generate/NUM_TOKENS)*1000}ms')
elif model_type == "Orca Mini":
tokens = model.tokenize(full_prompt)
gen_config = CtransGenGenerationConfig()
# Pull the generated text from the streamer, and update the model output.
start = time.time()
NUM_TOKENS=0
print('-'*4+'Start Generation'+'-'*4)
history[-1][1] = ""
for new_text in model.generate(tokens, **vars(gen_config)): #CtransGen_generate(prompt=full_prompt)#, config=CtransGenGenerationConfig()): # #top_k=top_k, temperature=temperature, repetition_penalty=repetition_penalty,
if new_text == None: new_text = ""
history[-1][1] += model.detokenize(new_text) #new_text
NUM_TOKENS+=1
yield history
time_generate = time.time() - start
print('\n')
print('-'*4+'End Generation'+'-'*4)
print(f'Num of generated tokens: {NUM_TOKENS}')
print(f'Time for complete generation: {time_generate}s')
print(f'Tokens per secound: {NUM_TOKENS/time_generate}')
print(f'Time per token: {(time_generate/NUM_TOKENS)*1000}ms')
# Chat helper functions
def adapt_q_from_chat_history(question, chat_history, extracted_memory, keyword_model=""):#keyword_model): # new_question_keywords,
chat_history_str, chat_history_first_q, chat_history_first_ans, max_memory_length = _get_chat_history(chat_history)
if chat_history_str:
# Keyword extraction is now done in the add_inputs_to_history function
extracted_memory = extracted_memory#remove_q_stopwords(str(chat_history_first_q) + " " + str(chat_history_first_ans))
new_question_kworded = str(extracted_memory) + ". " + question #+ " " + new_question_keywords
#extracted_memory + " " + question
else:
new_question_kworded = question #new_question_keywords
#print("Question output is: " + new_question_kworded)
return new_question_kworded
def create_doc_df(docs_keep_out):
# Extract content and metadata from 'winning' passages.
content=[]
meta=[]
meta_url=[]
page_section=[]
score=[]
for item in docs_keep_out:
content.append(item[0].page_content)
meta.append(item[0].metadata)
meta_url.append(item[0].metadata['source'])
page_section.append(item[0].metadata['page_section'])
score.append(item[1])
# Create df from 'winning' passages
doc_df = pd.DataFrame(list(zip(content, meta, page_section, meta_url, score)),
columns =['page_content', 'metadata', 'page_section', 'meta_url', 'score'])
docs_content = doc_df['page_content'].astype(str)
doc_df['full_url'] = "https://" + doc_df['meta_url']
return doc_df
def hybrid_retrieval(new_question_kworded, vectorstore, embeddings, k_val, out_passages,
vec_score_cut_off, vec_weight, bm25_weight, svm_weight): # ,vectorstore, embeddings
#vectorstore=globals()["vectorstore"]
#embeddings=globals()["embeddings"]
docs = vectorstore.similarity_search_with_score(new_question_kworded, k=k_val)
print("Docs from similarity search:")
print(docs)
# Keep only documents with a certain score
docs_len = [len(x[0].page_content) for x in docs]
docs_scores = [x[1] for x in docs]
# Only keep sources that are sufficiently relevant (i.e. similarity search score below threshold below)
score_more_limit = pd.Series(docs_scores) < vec_score_cut_off
docs_keep = list(compress(docs, score_more_limit))
if docs_keep == []:
docs_keep_as_doc = []
docs_content = []
docs_url = []
return docs_keep_as_doc, docs_content, docs_url
# Only keep sources that are at least 100 characters long
length_more_limit = pd.Series(docs_len) >= 100
docs_keep = list(compress(docs_keep, length_more_limit))
if docs_keep == []:
docs_keep_as_doc = []
docs_content = []
docs_url = []
return docs_keep_as_doc, docs_content, docs_url
docs_keep_as_doc = [x[0] for x in docs_keep]
docs_keep_length = len(docs_keep_as_doc)
if docs_keep_length == 1:
content=[]
meta_url=[]
score=[]
for item in docs_keep:
content.append(item[0].page_content)
meta_url.append(item[0].metadata['source'])
score.append(item[1])
# Create df from 'winning' passages
doc_df = pd.DataFrame(list(zip(content, meta_url, score)),
columns =['page_content', 'meta_url', 'score'])
docs_content = doc_df['page_content'].astype(str)
docs_url = doc_df['meta_url']
return docs_keep_as_doc, docs_content, docs_url
# Check for if more docs are removed than the desired output
if out_passages > docs_keep_length:
out_passages = docs_keep_length
k_val = docs_keep_length
vec_rank = [*range(1, docs_keep_length+1)]
vec_score = [(docs_keep_length/x)*vec_weight for x in vec_rank]
# 2nd level check on retrieved docs with BM25
content_keep=[]
for item in docs_keep:
content_keep.append(item[0].page_content)
corpus = corpus = [doc.lower().split() for doc in content_keep]
dictionary = Dictionary(corpus)
bm25_model = OkapiBM25Model(dictionary=dictionary)
bm25_corpus = bm25_model[list(map(dictionary.doc2bow, corpus))]
bm25_index = SparseMatrixSimilarity(bm25_corpus, num_docs=len(corpus), num_terms=len(dictionary),
normalize_queries=False, normalize_documents=False)
query = new_question_kworded.lower().split()
tfidf_model = TfidfModel(dictionary=dictionary, smartirs='bnn') # Enforce binary weighting of queries
tfidf_query = tfidf_model[dictionary.doc2bow(query)]
similarities = np.array(bm25_index[tfidf_query])
#print(similarities)
temp = similarities.argsort()
ranks = np.arange(len(similarities))[temp.argsort()][::-1]
# Pair each index with its corresponding value
pairs = list(zip(ranks, docs_keep_as_doc))
# Sort the pairs by the indices
pairs.sort()
# Extract the values in the new order
bm25_result = [value for ranks, value in pairs]
bm25_rank=[]
bm25_score = []
for vec_item in docs_keep:
x = 0
for bm25_item in bm25_result:
x = x + 1
if bm25_item.page_content == vec_item[0].page_content:
bm25_rank.append(x)
bm25_score.append((docs_keep_length/x)*bm25_weight)
# 3rd level check on retrieved docs with SVM retriever
svm_retriever = SVMRetriever.from_texts(content_keep, embeddings, k = k_val)
svm_result = svm_retriever.get_relevant_documents(new_question_kworded)
svm_rank=[]
svm_score = []
for vec_item in docs_keep:
x = 0
for svm_item in svm_result:
x = x + 1
if svm_item.page_content == vec_item[0].page_content:
svm_rank.append(x)
svm_score.append((docs_keep_length/x)*svm_weight)
## Calculate final score based on three ranking methods
final_score = [a + b + c for a, b, c in zip(vec_score, bm25_score, svm_score)]
final_rank = [sorted(final_score, reverse=True).index(x)+1 for x in final_score]
# Force final_rank to increment by 1 each time
final_rank = list(pd.Series(final_rank).rank(method='first'))
#print("final rank: " + str(final_rank))
#print("out_passages: " + str(out_passages))
best_rank_index_pos = []
for x in range(1,out_passages+1):
try:
best_rank_index_pos.append(final_rank.index(x))
except IndexError: # catch the error
pass
# Adjust best_rank_index_pos to
best_rank_pos_series = pd.Series(best_rank_index_pos)
docs_keep_out = [docs_keep[i] for i in best_rank_index_pos]
# Keep only 'best' options
docs_keep_as_doc = [x[0] for x in docs_keep_out]
# Make df of best options
doc_df = create_doc_df(docs_keep_out)
return docs_keep_as_doc, doc_df, docs_keep_out
def get_expanded_passages(vectorstore, docs, width):
"""
Extracts expanded passages based on given documents and a width for context.
Parameters:
- vectorstore: The primary data source.
- docs: List of documents to be expanded.
- width: Number of documents to expand around a given document for context.
Returns:
- expanded_docs: List of expanded Document objects.
- doc_df: DataFrame representation of expanded_docs.
"""
from collections import defaultdict
def get_docs_from_vstore(vectorstore):
vector = vectorstore.docstore._dict
return list(vector.items())
def extract_details(docs_list):
docs_list_out = [tup[1] for tup in docs_list]
content = [doc.page_content for doc in docs_list_out]
meta = [doc.metadata for doc in docs_list_out]
return ''.join(content), meta[0], meta[-1]
def get_parent_content_and_meta(vstore_docs, width, target):
target_range = range(max(0, target - width), min(len(vstore_docs), target + width + 1))
parent_vstore_out = [vstore_docs[i] for i in target_range]
content_str_out, meta_first_out, meta_last_out = [], [], []
for _ in parent_vstore_out:
content_str, meta_first, meta_last = extract_details(parent_vstore_out)
content_str_out.append(content_str)
meta_first_out.append(meta_first)
meta_last_out.append(meta_last)
return content_str_out, meta_first_out, meta_last_out
def merge_dicts_except_source(d1, d2):
merged = {}
for key in d1:
if key != "source":
merged[key] = str(d1[key]) + " to " + str(d2[key])
else:
merged[key] = d1[key] # or d2[key], based on preference
return merged
def merge_two_lists_of_dicts(list1, list2):
return [merge_dicts_except_source(d1, d2) for d1, d2 in zip(list1, list2)]
# Step 1: Filter vstore_docs
vstore_docs = get_docs_from_vstore(vectorstore)
doc_sources = {doc.metadata['source'] for doc, _ in docs}
vstore_docs = [(k, v) for k, v in vstore_docs if v.metadata.get('source') in doc_sources]
# Step 2: Group by source and proceed
vstore_by_source = defaultdict(list)
for k, v in vstore_docs:
vstore_by_source[v.metadata['source']].append((k, v))
expanded_docs = []
for doc, score in docs:
search_source = doc.metadata['source']
search_section = doc.metadata['page_section']
parent_vstore_meta_section = [doc.metadata['page_section'] for _, doc in vstore_by_source[search_source]]
search_index = parent_vstore_meta_section.index(search_section) if search_section in parent_vstore_meta_section else -1
content_str, meta_first, meta_last = get_parent_content_and_meta(vstore_by_source[search_source], width, search_index)
meta_full = merge_two_lists_of_dicts(meta_first, meta_last)
expanded_doc = (Document(page_content=content_str[0], metadata=meta_full[0]), score)
expanded_docs.append(expanded_doc)
doc_df = create_doc_df(expanded_docs) # Assuming you've defined the 'create_doc_df' function elsewhere
return expanded_docs, doc_df
def highlight_found_text(search_text: str, full_text: str, hlt_chunk_size:int=hlt_chunk_size, hlt_strat:List=hlt_strat, hlt_overlap:int=hlt_overlap) -> str:
"""
Highlights occurrences of search_text within full_text.
Parameters:
- search_text (str): The text to be searched for within full_text.
- full_text (str): The text within which search_text occurrences will be highlighted.
Returns:
- str: A string with occurrences of search_text highlighted.
Example:
>>> highlight_found_text("world", "Hello, world! This is a test. Another world awaits.")
'Hello, world! This is a test. Another world awaits.'
"""
def extract_text_from_input(text, i=0):
if isinstance(text, str):
return text.replace(" ", " ").strip()
elif isinstance(text, list):
return text[i][0].replace(" ", " ").strip()
else:
return ""
def extract_search_text_from_input(text):
if isinstance(text, str):
return text.replace(" ", " ").strip()
elif isinstance(text, list):
return text[-1][1].replace(" ", " ").strip()
else:
return ""
full_text = extract_text_from_input(full_text)
search_text = extract_search_text_from_input(search_text)
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=hlt_chunk_size,
separators=hlt_strat,
chunk_overlap=hlt_overlap,
)
sections = text_splitter.split_text(search_text)
found_positions = {}
for x in sections:
text_start_pos = 0
while text_start_pos != -1:
text_start_pos = full_text.find(x, text_start_pos)
if text_start_pos != -1:
found_positions[text_start_pos] = text_start_pos + len(x)
text_start_pos += 1
# Combine overlapping or adjacent positions
sorted_starts = sorted(found_positions.keys())
combined_positions = []
if sorted_starts:
current_start, current_end = sorted_starts[0], found_positions[sorted_starts[0]]
for start in sorted_starts[1:]:
if start <= (current_end + 10):
current_end = max(current_end, found_positions[start])
else:
combined_positions.append((current_start, current_end))
current_start, current_end = start, found_positions[start]
combined_positions.append((current_start, current_end))
# Construct pos_tokens
pos_tokens = []
prev_end = 0
for start, end in combined_positions:
if end-start > 15: # Only combine if there is a significant amount of matched text. Avoids picking up single words like 'and' etc.
pos_tokens.append(full_text[prev_end:start])
pos_tokens.append('' + full_text[start:end] + '')
prev_end = end
pos_tokens.append(full_text[prev_end:])
return "".join(pos_tokens)
# # Chat history functions
def clear_chat(chat_history_state, sources, chat_message, current_topic):
chat_history_state = []
sources = ''
chat_message = ''
current_topic = ''
return chat_history_state, sources, chat_message, current_topic
def _get_chat_history(chat_history: List[Tuple[str, str]], max_memory_length:int = max_memory_length): # Limit to last x interactions only
if (not chat_history) | (max_memory_length == 0):
chat_history = []
if len(chat_history) > max_memory_length:
chat_history = chat_history[-max_memory_length:]
#print(chat_history)
first_q = ""
first_ans = ""
for human_s, ai_s in chat_history:
first_q = human_s
first_ans = ai_s
#print("Text to keyword extract: " + first_q + " " + first_ans)
break
conversation = ""
for human_s, ai_s in chat_history:
human = f"Human: " + human_s
ai = f"Assistant: " + ai_s
conversation += "\n" + "\n".join([human, ai])
return conversation, first_q, first_ans, max_memory_length
def add_inputs_answer_to_history(user_message, history, current_topic):
if history is None:
history = [("","")]
#history.append((user_message, [-1]))
chat_history_str, chat_history_first_q, chat_history_first_ans, max_memory_length = _get_chat_history(history)
# Only get the keywords for the first question and response, or do it every time if over 'max_memory_length' responses in the conversation
if (len(history) == 1) | (len(history) > max_memory_length):
#print("History after appending is:")
#print(history)
first_q_and_first_ans = str(chat_history_first_q) + " " + str(chat_history_first_ans)
#ner_memory = remove_q_ner_extractor(first_q_and_first_ans)
keywords = keybert_keywords(first_q_and_first_ans, n = 8, kw_model=kw_model)
#keywords.append(ner_memory)
# Remove duplicate words while preserving order
ordered_tokens = set()
result = []
for word in keywords:
if word not in ordered_tokens:
ordered_tokens.add(word)
result.append(word)
extracted_memory = ' '.join(result)
else: extracted_memory=current_topic
print("Extracted memory is:")
print(extracted_memory)
return history, extracted_memory
# Keyword functions
def remove_q_stopwords(question): # Remove stopwords from question. Not used at the moment
# Prepare keywords from question by removing stopwords
text = question.lower()
# Remove numbers
text = re.sub('[0-9]', '', text)
tokenizer = RegexpTokenizer(r'\w+')
text_tokens = tokenizer.tokenize(text)
#text_tokens = word_tokenize(text)
tokens_without_sw = [word for word in text_tokens if not word in stopwords]
# Remove duplicate words while preserving order
ordered_tokens = set()
result = []
for word in tokens_without_sw:
if word not in ordered_tokens:
ordered_tokens.add(word)
result.append(word)
new_question_keywords = ' '.join(result)
return new_question_keywords
def remove_q_ner_extractor(question):
predict_out = ner_model.predict(question)
predict_tokens = [' '.join(v for k, v in d.items() if k == 'span') for d in predict_out]
# Remove duplicate words while preserving order
ordered_tokens = set()
result = []
for word in predict_tokens:
if word not in ordered_tokens:
ordered_tokens.add(word)
result.append(word)
new_question_keywords = ' '.join(result).lower()
return new_question_keywords
def apply_lemmatize(text, wnl=WordNetLemmatizer()):
def prep_for_lemma(text):
# Remove numbers
text = re.sub('[0-9]', '', text)
print(text)
tokenizer = RegexpTokenizer(r'\w+')
text_tokens = tokenizer.tokenize(text)
#text_tokens = word_tokenize(text)
return text_tokens
tokens = prep_for_lemma(text)
def lem_word(word):
if len(word) > 3: out_word = wnl.lemmatize(word)
else: out_word = word
return out_word
return [lem_word(token) for token in tokens]
def keybert_keywords(text, n, kw_model):
tokens_lemma = apply_lemmatize(text)
lemmatised_text = ' '.join(tokens_lemma)
keywords_text = keybert.KeyBERT(model=kw_model).extract_keywords(lemmatised_text, stop_words='english', top_n=n,
keyphrase_ngram_range=(1, 1))
keywords_list = [item[0] for item in keywords_text]
return keywords_list
# Gradio functions
def turn_off_interactivity(user_message, history):
return gr.update(value="", interactive=False), history + [[user_message, None]]
def restore_interactivity():
return gr.update(interactive=True)
def update_message(dropdown_value):
return gr.Textbox.update(value=dropdown_value)
def hide_block():
return gr.Radio.update(visible=False)
# Vote function
def vote(data: gr.LikeData, chat_history, instruction_prompt_out, model_type):
import os
import pandas as pd
chat_history_last = str(str(chat_history[-1][0]) + " - " + str(chat_history[-1][1]))
response_df = pd.DataFrame(data={"thumbs_up":data.liked,
"chosen_response":data.value,
"input_prompt":instruction_prompt_out,
"chat_history":chat_history_last,
"model_type": model_type,
"date_time": pd.Timestamp.now()}, index=[0])
if data.liked:
print("You upvoted this response: " + data.value)
if os.path.isfile("thumbs_up_data.csv"):
existing_thumbs_up_df = pd.read_csv("thumbs_up_data.csv")
thumbs_up_df_concat = pd.concat([existing_thumbs_up_df, response_df], ignore_index=True).drop("Unnamed: 0",axis=1, errors="ignore")
thumbs_up_df_concat.to_csv("thumbs_up_data.csv")
else:
response_df.to_csv("thumbs_up_data.csv")
else:
print("You downvoted this response: " + data.value)
if os.path.isfile("thumbs_down_data.csv"):
existing_thumbs_down_df = pd.read_csv("thumbs_down_data.csv")
thumbs_down_df_concat = pd.concat([existing_thumbs_down_df, response_df], ignore_index=True).drop("Unnamed: 0",axis=1, errors="ignore")
thumbs_down_df_concat.to_csv("thumbs_down_data.csv")
else:
response_df.to_csv("thumbs_down_data.csv")