Update 01_π _Home.py
Browse files- 01_π _Home.py +5 -5
01_π _Home.py
CHANGED
@@ -15,6 +15,8 @@ nltk.download('punkt')
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from nltk import sent_tokenize
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st.sidebar.header("Home")
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asr_model_options = ['tiny.en','base.en','small.en']
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@@ -32,10 +34,10 @@ st.markdown(twitter_link)
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st.markdown(
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"""
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This app assists finance analysts with transcribing and analysis Earnings Calls by carrying out the following tasks:
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- Transcribing earnings calls using Open AI's
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- Analysing the sentiment of transcribed text using the quantized version of [FinBert-Tone](https://huggingface.co/nickmuchi/quantized-optimum-finbert-tone).
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- Summarization of the call with [FaceBook-Bart-Large-CNN](https://huggingface.co/facebook/bart-large-cnn) model with entity extraction
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- Question Answering engine powered by Langchain and [Sentence Transformers](https://huggingface.co/sentence-transformers/all-mpnet-base-v2).
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- Knowledge Graph generation using [Babelscape/rebel-large](https://huggingface.co/Babelscape/rebel-large) model.
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**π Enter a YouTube Earnings Call URL below and navigate to the sidebar tabs**
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@@ -63,8 +65,6 @@ st.markdown(
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unsafe_allow_html=True
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)
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upload_wav = st.file_uploader("Upload a .wav sound file ",key="upload")
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auth_token = os.environ.get("auth_token")
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st.markdown("![visitor badge](https://visitor-badge.glitch.me/badge?page_id=nickmuchi-earnings-call-whisperer)")
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from nltk import sent_tokenize
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auth_token = os.environ.get("auth_token")
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st.sidebar.header("Home")
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asr_model_options = ['tiny.en','base.en','small.en']
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st.markdown(
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"""
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This app assists finance analysts with transcribing and analysis Earnings Calls by carrying out the following tasks:
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- Transcribing earnings calls using Open AI's Whisper API, takes approx 3mins to transcribe a 1hr call less than 25mb in size.
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- Analysing the sentiment of transcribed text using the quantized version of [FinBert-Tone](https://huggingface.co/nickmuchi/quantized-optimum-finbert-tone).
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- Summarization of the call with [FaceBook-Bart-Large-CNN](https://huggingface.co/facebook/bart-large-cnn) model with entity extraction
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- Question Answering Search engine powered by Langchain and [Sentence Transformers](https://huggingface.co/sentence-transformers/all-mpnet-base-v2).
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- Knowledge Graph generation using [Babelscape/rebel-large](https://huggingface.co/Babelscape/rebel-large) model.
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**π Enter a YouTube Earnings Call URL below and navigate to the sidebar tabs**
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unsafe_allow_html=True
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)
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upload_wav = st.file_uploader("Upload a .wav/.mp3/.mp4 sound file ",key="upload")
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st.markdown("![visitor badge](https://visitor-badge.glitch.me/badge?page_id=nickmuchi-earnings-call-whisperer)")
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