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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") | |