import streamlit as st
import os
import git
from load_model import entity_extractor 

print(os.path.exists("./rajaatif786/TickerExtraction/entity_model2.pt"))
import pandas as pd
import numpy as np
from EntityExtractor import LABEL_MAP

#os.chdir("./TickerExtraction")

texts=[st.text_input("Enter Text")]
st.write(texts[0])        
data,df=entity_extractor.input_text(texts)
probs = entity_extractor.extract_entity_probabilities( dataset=data)
for i in range(len(probs)):
  prediction="Predicted Company Ticker: \n"+str(list(LABEL_MAP.keys())[list(LABEL_MAP.values()).index(np.argmax(probs[i]))])+'\n'
st.write(prediction)