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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 streamlit as st
import openai
# connect to pinecone environment
pinecone.init(
api_key="d4f20339-fcc1-4a11-b04f-3800203eacd2",
environment="us-east1-gcp"
)
index_name = "abstractive-question-answering"
index = pinecone.Index(index_name)
# Initialize models from HuggingFace
@st.cache_resource
def get_t5_model():
return pipeline("summarization", model="t5-base", tokenizer="t5-base")
@st.cache_resource
def get_flan_t5_model():
return pipeline("summarization", model="google/flan-t5-base", tokenizer="google/flan-t5-base")
@st.cache_resource
def get_embedding_model():
device = 'cuda' if torch.cuda.is_available() else 'cpu'
model = SentenceTransformer("flax-sentence-embeddings/all_datasets_v3_mpnet-base", device=device)
model.max_seq_length = 512
return model
@st.cache_data()
def save_key(api_key):
return api_key
retriever_model = get_embedding_model()
def query_pinecone(query, top_k, model):
# generate embeddings for the query
xq = model.encode([query]).tolist()
# search pinecone index for context passage with the answer
xc = index.query(xq, top_k=top_k, include_metadata=True)
return xc
def format_query(query_results):
# extract passage_text from Pinecone search result
context = [result['metadata']['merged_text'] for result in query_results['matches']]
return context
def gpt3_summary(text):
response = openai.Completion.create(
model="text-davinci-003",
prompt=text+"\n\nTl;dr",
temperature=0.1,
max_tokens=512,
top_p=1.0,
frequency_penalty=0.0,
presence_penalty=1
)
return response.choices[0].text
def gpt3_qa(query, answer):
response = openai.Completion.create(
model="text-davinci-003",
prompt="Q: " + query + "\nA: " + answer,
temperature=0,
max_tokens=512,
top_p=1,
frequency_penalty=0.0,
presence_penalty=0.0,
stop=["\n"]
)
return response.choices[0].text
st.title("Abstractive Question Answering - APPL")
query_text = st.text_input("Input Query", value="Who is the CEO of Apple?")
num_results = int(st.number_input("Number of Results to query", 1, 5, value=2))
query_results = query_pinecone(query_text, num_results, retriever_model)
context_list = format_query(query_results)
# Choose decoder model
models_choice = ["GPT3 (text_davinci)", "GPT3 - QA", "T5", "FLAN-T5"]
decoder_model = st.selectbox(
'Select Decoder Model',
models_choice)
st.subheader("Answer:")
if decoder_model == "GPT3 (text_davinci)":
openai_key = st.text_input("Enter OpenAI key")
api_key = save_key(openai_key)
openai.api_key = api_key
output_text = []
for context_text in context_list:
output_text.append(gpt3_summary(context_text))
generated_text = " ".join(output_text)
st.write(gpt3_summary(generated_text))
elif decoder_model=="GPT3 - QA":
openai_key = st.text_input("Enter OpenAI key")
api_key = save_key(openai_key)
openai.api_key = api_key
output_text = []
for context_text in context_list:
output_text.append(gpt3_qa(query_text, context_text))
generated_text = " ".join(output_text)
st.write(gpt3_qa(query_text, generated_text))
elif decoder_model == "T5":
t5_pipeline = get_t5_model()
output_text = []
for context_text in context_list:
output_text.append(t5_pipeline(context_text)[0]["summary_text"])
generated_text = " ".join(output_text)
st.write(t5_pipeline(generated_text)[0]["summary_text"])
elif decoder_model == "FLAN-T5":
flan_t5_pipeline = get_flan_t5_model()
output_text = []
for context_text in context_list:
output_text.append(flan_t5_pipeline(context_text)[0]["summary_text"])
generated_text = " ".join(output_text)
st.write(flan_t5_pipeline(generated_text)[0]["summary_text"])
st.subheader("Retrieved Text:")
for context_text in context_list:
st.markdown(f"- {context_text}")
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