import streamlit as st
import time
import pandas as pd
import numpy as np


pip install -U sentence-transformers

from sentence_transformers import SentenceTransformer, util


# Load document embeddings
doc_emb = np.loadtxt("abstract-embed.txt", dtype=float)
#doc_emb

# Load data
df = pd.read_csv("sessions.csv", usecols=['Unique ID', 'Name', 'Description', 'Activity Code', 'Start Time', 'End Time', 'Location Name'])
#df.head()

st.title(" Your Top 3 Important Sessions")
st.markdown("This application is a dashboard for displaying your top 3 Sessions at the summit")

doc_emb = np.loadtxt("abstract-embed.txt", dtype=float)
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 model
    model = SentenceTransformer('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.encode(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')