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01_🏠_Home.py

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+import whisper
+import os
+from pytube import YouTube
+import pandas as pd
+import plotly_express as px
+import nltk
+import plotly.graph_objects as go
+from optimum.onnxruntime import ORTModelForSequenceClassification
+from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification, AutoModelForTokenClassification
+from sentence_transformers import SentenceTransformer, CrossEncoder, util
+import streamlit as st
+import en_core_web_lg
+
+nltk.download('punkt')
+
+from nltk import sent_tokenize
+
+
+st.set_page_config(
+    page_title="Home",
+    page_icon="📞",
+)
+
+st.sidebar.header("Home")
+st.markdown("## Earnings Call Analysis Whisperer")
+
+st.markdown(
+    """
+    This app assists finance analysts with transcribing and analysis Earnings Calls by carrying out the following tasks:
+    - Transcribing earnings calls using Open AI's [Whisper](https://github.com/openai/whisper).
+    - Analysing the sentiment of transcribed text using the quantized version of [FinBert-Tone](https://huggingface.co/nickmuchi/quantized-optimum-finbert-tone).
+    - Summarization of the call with [FaceBook-Bart-Large-CNN](https://huggingface.co/facebook/bart-large-cnn) model with entity extraction
+    - Semantic search engine with [Sentence-Transformers](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) and reranking results with a Cross-Encoder.
+    
+    **👇 Enter a YouTube Earnings Call URL below and navigate to the sidebar tabs** 
+    
+"""
+)
+
+if "url" not in st.session_state:
+    st.session_state.url = ''
+        
+url_input = st.text_input(
+        label='Enter YouTube URL, e.g "https://www.youtube.com/watch?v=8pmbScvyfeY"', key="url")
+        
+st.markdown(
+    "<h3 style='text-align: center; color: red;'>OR</h3>",
+    unsafe_allow_html=True
+)
+
+upload_wav = st.file_uploader("Upload a .wav sound file ",key="upload")
+
+auth_token = os.environ.get("auth_token")
+
+from functions import *