Delete app.py

app.py

@@ -1,55 +0,0 @@
-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 *