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import textwrap
# from dotenv import load_dotenv
import streamlit as st
import os
import nltk
from nltk.tokenize import sent_tokenize
import openai
nltk.download('punkt')
openai.organization = os.environ['org']
openai.api_key = os.environ['api_key']
st.title("Ekatra QnA")
st.write("AI Powered Smart Search System")
st.markdown('_Welcome to Question Answering System 🧠 🤖_')
top_match_sentences = []
@st.cache(allow_output_mutation=True)
def process_pdf_data(uploaded_files):
"""
The function accepts an uploaded PDF file as input and then proceeds to extract, preprocess, and vectorize the text. It ultimately returns a list of filtered sentences and their respective embeddings.
Parameters:
uploaded_files (list): List of uploaded files
Returns:
filt1_list (list): List of filtered sentences
embeddings (list): List of embeddings of the sentences
"""
filt1_list = []
embeddings = []
text_ext = []
for i in uploaded_files:
if i.type == "application/pdf":
# Reading the pdf file and extracting the text
text_ext += op.read_pdf(i)
# Applying sent_tokenize to the text and storing the result in a list
sent_toks = []
for i in text_ext:
sent_toks.append(sent_tokenize(i))
concat_list = [j for i in sent_toks for j in i]
# Removing the new line characters from the list
for i in concat_list:
a = (i.replace('\n', ' '))
filt1_list.append(a)
# Creating embeddings for the sentences
embeddings = op.create_content_embeddings(filt1_list)
return filt1_list, embeddings
# Streamlit code to upload files
uploaded_files = st.file_uploader(
"Upload files - ", accept_multiple_files=True, type=['pdf'])
if st.button("Process!"):
if len(uploaded_files) != 0:
# Calling the function process_pdf_data to process the uploaded files
filt1_list, embeddings = process_pdf_data(uploaded_files)
st.write("Process Completed")
else:
st.warning("Please upload a PDF file.")
# Streamlit code to take user input after vectorization of the documents
query = st.text_input('Ask me anything!', placeholder='Type.....')
try:
if st.button("Confirm!"):
cached_data = process_pdf_data(uploaded_files)
filt1_list = cached_data[0]
embeddings = cached_data[1]
# Creating embeddings for the query
query_embedding = op.create_query_embeddings(query)
# Calculating cosine similarity between the query and the sentences
cosine_lis = op.calculate_cosine(
query_embedding, embeddings, filt1_list)
# Fetching the top 15 sentences with the highest cosine similarity
indexes_final = op.fetch_top_rank_ans(cosine_lis, 15)
# Fetching the most relevant sentence from the top 15 sentences, and providing it as the context to the GPT-3 model
most_relevant = op.fetch_most_relevant(
indexes_final, filt1_list, cosine_lis, query)
# Calling the GPT-3 model to generate the answer
COMPLETIONS_API_PARAMS = {
"temperature": 0.0,
"max_tokens": 300,
"model": "text-davinci-003",
}
response = openai.Completion.create(
prompt=most_relevant,
**COMPLETIONS_API_PARAMS
)
# print("\n\n", textwrap.fill(
# response["choices"][0]["text"].strip(" \n")))
# Displaying the answer to the user
st.write(textwrap.fill(response["choices"][0]["text"].strip(" \n")))
except:
st.warning("Something went wrong. Please try again.")
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