|
import streamlit as st |
|
import pandas as pd |
|
import pandas as pd |
|
from tqdm import tqdm |
|
import pinecone |
|
import torch |
|
from sentence_transformers import SentenceTransformer |
|
from transformers import ( |
|
pipeline, |
|
AutoTokenizer, |
|
AutoModelForCausalLM, |
|
AutoModelForSeq2SeqLM, |
|
) |
|
import openai |
|
import streamlit_scrollable_textbox as stx |
|
|
|
|
|
@st.experimental_singleton |
|
def get_data(): |
|
data = pd.read_csv("earnings_calls_cleaned_metadata.csv") |
|
return data |
|
|
|
|
|
|
|
|
|
|
|
@st.experimental_singleton |
|
def get_t5_model(): |
|
return pipeline("summarization", model="t5-small", tokenizer="t5-small") |
|
|
|
|
|
@st.experimental_singleton |
|
def get_flan_t5_model(): |
|
return pipeline( |
|
"summarization", model="google/flan-t5-small", tokenizer="google/flan-t5-small" |
|
) |
|
|
|
|
|
@st.experimental_singleton |
|
def get_mpnet_embedding_model(): |
|
device = "cuda" if torch.cuda.is_available() else "cpu" |
|
model = SentenceTransformer( |
|
"sentence-transformers/all-mpnet-base-v2", device=device |
|
) |
|
model.max_seq_length = 512 |
|
return model |
|
|
|
|
|
@st.experimental_singleton |
|
def get_sgpt_embedding_model(): |
|
device = "cuda" if torch.cuda.is_available() else "cpu" |
|
model = SentenceTransformer( |
|
"Muennighoff/SGPT-125M-weightedmean-nli-bitfit", device=device |
|
) |
|
model.max_seq_length = 512 |
|
return model |
|
|
|
|
|
@st.experimental_memo |
|
def save_key(api_key): |
|
return api_key |
|
|
|
|
|
def query_pinecone( |
|
query, top_k, model, index, year, quarter, ticker, participant_type, threshold=0.25 |
|
): |
|
|
|
xq = model.encode([query]).tolist() |
|
|
|
if participant_type == "Company Speaker": |
|
participant = "Speaker" |
|
else: |
|
participant = participant_type |
|
|
|
if year == "All": |
|
if quarter == "All": |
|
xc = index.query( |
|
xq, |
|
top_k=top_k, |
|
filter={ |
|
"Year": { |
|
"$in": [ |
|
int("2020"), |
|
int("2019"), |
|
int("2018"), |
|
int("2017"), |
|
int("2016"), |
|
] |
|
}, |
|
"Quarter": {"$in": ["Q1", "Q2", "Q3", "Q4"]}, |
|
"Ticker": {"$eq": ticker}, |
|
"QA_Flag": {"$eq": participant}, |
|
}, |
|
include_metadata=True, |
|
) |
|
else: |
|
xc = index.query( |
|
xq, |
|
top_k=top_k, |
|
filter={ |
|
"Year": { |
|
"$in": [ |
|
int("2020"), |
|
int("2019"), |
|
int("2018"), |
|
int("2017"), |
|
int("2016"), |
|
] |
|
}, |
|
"Quarter": {"$eq": quarter}, |
|
"Ticker": {"$eq": ticker}, |
|
"QA_Flag": {"$eq": participant}, |
|
}, |
|
include_metadata=True, |
|
) |
|
else: |
|
|
|
xc = index.query( |
|
xq, |
|
top_k=top_k, |
|
filter={ |
|
"Year": int(year), |
|
"Quarter": {"$eq": quarter}, |
|
"Ticker": {"$eq": ticker}, |
|
"QA_Flag": {"$eq": participant}, |
|
}, |
|
include_metadata=True, |
|
) |
|
|
|
|
|
filtered_matches = [] |
|
for match in xc["matches"]: |
|
if match["score"] >= threshold: |
|
filtered_matches.append(match) |
|
xc["matches"] = filtered_matches |
|
return xc |
|
|
|
|
|
def format_query(query_results): |
|
|
|
context = [result["metadata"]["Text"] for result in query_results["matches"]] |
|
return context |
|
|
|
|
|
def sentence_id_combine(data, query_results, lag=1): |
|
|
|
ids = [result["metadata"]["Sentence_id"] for result in query_results["matches"]] |
|
|
|
new_ids = [id + i for id in ids for i in range(-lag, lag + 1)] |
|
|
|
new_ids = sorted(set(new_ids)) |
|
|
|
lookup_ids = [ |
|
new_ids[i : i + (lag * 2 + 1)] for i in range(0, len(new_ids), lag * 2 + 1) |
|
] |
|
|
|
context_list = [ |
|
" ".join(data.Text.iloc[lookup_id].to_list()) for lookup_id in lookup_ids |
|
] |
|
return context_list |
|
|
|
|
|
def text_lookup(data, sentence_ids): |
|
context = ". ".join(data.iloc[sentence_ids].to_list()) |
|
return context |
|
|
|
|
|
def generate_prompt(query_text, context_list): |
|
context = " ".join(context_list) |
|
prompt = f"""Answer the question as accurately as possible using the provided context. Try to include as many key details as possible. |
|
Context: {context} |
|
Question: {query_text} |
|
Answer:""" |
|
return prompt |
|
|
|
|
|
def generate_prompt_2(query_text, context_list): |
|
context = " ".join(context_list) |
|
prompt = f""" |
|
Context information is below: |
|
--------------------- |
|
{context} |
|
--------------------- |
|
Given the context information and prior knowledge, answer this question: |
|
{query_text} |
|
Try to include as many key details as possible and format the answer in points.""" |
|
return prompt |
|
|
|
|
|
def gpt_model(prompt): |
|
response = openai.Completion.create( |
|
model="text-davinci-003", |
|
prompt=prompt, |
|
temperature=0.1, |
|
max_tokens=1024, |
|
top_p=1.0, |
|
frequency_penalty=0.5, |
|
presence_penalty=1, |
|
) |
|
return response.choices[0].text |
|
|
|
|
|
|
|
|
|
|
|
def retrieve_transcript(data, year, quarter, ticker): |
|
row = ( |
|
data.loc[ |
|
(data.Year == int(year)) |
|
& (data.Quarter == quarter) |
|
& (data.Ticker == ticker), |
|
["File_Name"], |
|
] |
|
.drop_duplicates() |
|
.iloc[0, 0] |
|
) |
|
print(row) |
|
|
|
|
|
open_file = open( |
|
f"Transcripts/{ticker}/{row}", |
|
"r", |
|
) |
|
file_text = open_file.read() |
|
return file_text |
|
|