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import re
import openai
import pandas as pd
import streamlit_scrollable_textbox as stx
import torch
from InstructorEmbedding import INSTRUCTOR
from gradio_client import Client
from transformers import (
AutoModelForMaskedLM,
AutoTokenizer,
)
from rank_bm25 import BM25Okapi, BM25L, BM25Plus
import numpy as np
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer
import re
import streamlit as st
@st.cache_resource
def get_data():
data = pd.read_csv("earnings_calls_cleaned_metadata_keywords_indices.csv")
return data
# Preprocessing for BM25
def tokenizer(
string, reg="[a-zA-Z'-]+|[0-9]{1,}%|[0-9]{1,}\.[0-9]{1,}%|\d+\.\d+%}"
):
regex = reg
string = string.replace("-", " ")
return " ".join(re.findall(regex, string))
def preprocess_text(text):
# Convert to lowercase
text = text.lower()
# Tokenize the text
tokens = word_tokenize(text)
# Remove stop words
stop_words = set(stopwords.words("english"))
tokens = [token for token in tokens if token not in stop_words]
# Stem the tokens
porter_stemmer = PorterStemmer()
tokens = [porter_stemmer.stem(token) for token in tokens]
# Join the tokens back into a single string
preprocessed_text = " ".join(tokens)
preprocessed_text = tokenizer(preprocessed_text)
return preprocessed_text
# Initialize models from HuggingFace
@st.cache_resource
def get_splade_sparse_embedding_model():
model_sparse = "naver/splade-cocondenser-ensembledistil"
# check device
device = "cuda" if torch.cuda.is_available() else "cpu"
tokenizer = AutoTokenizer.from_pretrained(model_sparse)
model_sparse = AutoModelForMaskedLM.from_pretrained(model_sparse)
# move to gpu if available
model_sparse.to(device)
return model_sparse, tokenizer
@st.cache_resource
def get_instructor_embedding_model():
model = INSTRUCTOR("hkunlp/instructor-xl")
return model
@st.cache_resource
def get_instructor_embedding_model_api():
client = Client("https://awinml-api-instructor-xl-2.hf.space/")
return client
@st.cache_resource
def get_alpaca_model():
client = Client("https://awinml-alpaca-cpp.hf.space")
return client
@st.cache_resource
def get_vicuna_ner_1_model():
client = Client("https://awinml-api-vicuna-openblas-ner-1.hf.space/")
return client
@st.cache_resource
def get_vicuna_ner_2_model():
client = Client("https://awinml-api-vicuna-openblas-ner-2.hf.space/")
return client
@st.cache_resource
def get_vicuna_text_gen_model():
client = Client("https://awinml-api-vicuna-openblas-4.hf.space/")
return client
@st.cache_resource
def get_bm25_model(data):
corpus = data.Text.tolist()
corpus_clean = [preprocess_text(x) for x in corpus]
tokenized_corpus = [doc.split(" ") for doc in corpus_clean]
bm25 = BM25Plus(tokenized_corpus)
return corpus, bm25
@st.cache_resource
def save_key(api_key):
return api_key
# Text Generation
def vicuna_text_generate(prompt, model):
generated_text = model.predict(prompt, api_name="/predict")
return generated_text
def gpt_turbo_model(prompt):
response = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=[
{"role": "user", "content": prompt},
],
temperature=0.01,
max_tokens=1024,
)
return response["choices"][0]["message"]["content"]
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