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"""
Demo UI page
"""
import streamlit as st
#import tools.ocr_video as ocr
import os
import shutil
import uuid
from model_loader import HFPretrainedModel
from transformers import pipeline
import torch
if "session_id" not in st.session_state:
st.session_state["session_id"] = uuid.uuid1()
# Temporary folder path
TMP_PATH = "tmp-{"+str(st.session_state["session_id"])+"}/"
st.title("Demo page")
st.markdown("""Upload the US political campaign video to predict its orientation (base/center).""")
video_file = st.file_uploader("Choose the US political campaign video", type=["wmv", "avi", "mov"], disabled=True)
text = st.text_input("Transcript of the video", "")
b = st.button("Predict")
if b:
st.markdown("""---""")
status_bar = st.progress(0)
upload_cap = st.caption("Uploading video...")
#if os.path.isdir(TMP_PATH):
# shutil.rmtree(TMP_PATH)
#os.mkdir(TMP_PATH)
#with open(TMP_PATH+"uploaded_video_tmp", "wb") as f:
# f.write(video_file.getbuffer())
status_bar.progress(50)
#upload_cap.caption("Extracting text from frames... (can take some time)")
#text_ocr = ocr.get_formated_text(ocr.retrieve_text(TMP_PATH+"uploaded_video_tmp", frames_path = "tmp_frames-{"+str(st.session_state["session_id"])+"}", show_print = False))
upload_cap.caption("Extracting text sentiment...")
sentiment_analysis = pipeline("sentiment-analysis", model="siebert/sentiment-roberta-large-english")
text_sentiment = sentiment_analysis(text)[0]["label"]
status_bar.progress(80)
#shutil.rmtree(TMP_PATH)
status_bar.progress(90)
upload_cap.caption("Prediction...")
model = HFPretrainedModel("distilbert-base-uncased", "deano/political-campaign-analysis-110922")
#query_dict = {"text": [text], "text_ocr": [text_ocr]}
query_dict = {"text": [text], "label_sentiment": [text_sentiment]}
# Predicted confidence for each label
conf = model.predict(query_dict)
col1, col2 = st.columns(2)
col1.metric("Base", "{:.2f}".format(conf[1].item()*100)+"%", "")
col2.metric("Center", "{:.2f}".format(conf[0].item()*100)+"%", "")
status_bar.progress(100)
upload_cap.caption("Done")
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