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| import streamlit as st | |
| import pandas as pd | |
| import altair as alt | |
| from transformers import pipeline | |
| #!pip install -U sentence-transformers | |
| #from sentence_transformers import SentenceTransformer, util | |
| #from sentence_transformers import SentenceTransformer | |
| import numpy as np | |
| import sys | |
| import json | |
| #from torch.utils.data import DataLoader | |
| #from sentence_transformers import SentenceTransformer, LoggingHandler, util, models, evaluation, losses, InputExample | |
| import logging | |
| from datetime import datetime | |
| import gzip | |
| import os | |
| import tarfile | |
| from collections import defaultdict | |
| #from torch.utils.data import IterableDataset | |
| import tqdm | |
| #from torch.utils.data import Dataset | |
| import random | |
| from shutil import copyfile | |
| from urllib.error import URLError | |
| # Load document embeddings | |
| # set up title and sidebar | |
| st.title(" Your top 3 Important Sessions") | |
| st.markdown("This application is a dashboard for displaying your top 3 Sessions at the Nashville summit") | |
| doc_emb = np.loadtxt("abstract-embed.txt", dtype=float) | |
| # Load data | |
| df = pd.read_csv("sessions.csv", usecols=['Unique ID', 'Name', 'Description', 'Activity Code', 'Start Time', 'End Time', 'Location Name']) | |
| # front end elements of the web page | |
| html_temp = """ | |
| <div style ="background-color:lightblue;padding:13px"> | |
| <h1 style ="color:white;text-align:center;">Sentence Similarity App for Nashville Analytic Summit 2022</h1> | |
| </div> | |
| """ | |
| def main(): | |
| # display the front end aspect | |
| st.markdown(html_temp, unsafe_allow_html = True) | |
| # Get attributes from dataframe | |
| docs = list(df["Description"]) | |
| titles = list(df["Name"]) | |
| start_times = list(df["Start Time"]) | |
| end_times = list(df["End Time"]) | |
| locations = list(df["Location Name"]) | |
| # Query | |
| # Load the model | |
| model = pipeline('sentiment-analysis')#('sentence-transformers/multi-qa-MiniLM-L6-cos-v1') | |
| query = st.text_input("Enter your query: ") | |
| if query: | |
| #st.text_area('Text area') | |
| #age = st.number_input("Age in Years") | |
| #Encode query and documents | |
| query_emb = model(query)#.astype(float) | |
| #Compute dot score between query and all document embeddings | |
| scores = util.dot_score(query_emb, doc_emb.astype(float))[0].cpu().tolist() | |
| #Combine docs & scores with other attributes | |
| doc_score_pairs = list(zip(docs, scores, titles, start_times, end_times, locations)) | |
| # top_k results to return | |
| top_k=3 | |
| print(" Your top", top_k, "most similar sessions in the Summit:") | |
| #Sort by decreasing score | |
| doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True) | |
| #Output presentation recommendations | |
| for doc, score, title, start_time, end_time, location in doc_score_pairs[:top_k]: | |
| st.write("Score: %f" %score) | |
| st.write("Title: %s" %title) | |
| st.write("Abstract: %s" %doc) | |
| st.write("Location: %s" %location) | |
| st.write(f"From {start_time} to {end_time}") | |
| st.write('\n') | |
| if __name__ == "__main__": | |
| main() |