Duplicate from JulianHame/sentiment-analysis-app

.github/workflows/sync_to_huggingface.yml

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+name: Sync to Hugging Face hub
+on:
+  push:
+    branches: [main]
+
+  # to run this workflow manually from the Actions tab
+  workflow_dispatch:
+
+jobs:
+  sync-to-hub:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+        with:
+          fetch-depth: 0
+          lfs: true
+      - name: Push to hub
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+        run: git push --force https://JulianHame:$HF_TOKEN@huggingface.co/spaces/JulianHame/streamlit-application main

README.md

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+---
+title: Sentiment Analysis Application
+emoji: 😻
+colorFrom: red
+colorTo: indigo
+sdk: streamlit
+sdk_version: 1.17.0
+app_file: app.py
+pinned: false
+duplicated_from: JulianHame/sentiment-analysis-app
+---
+
+# CS482-project-streamlit-application
+huggingface spaces deployment of a streamlit python application

app.py

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+import streamlit as st
+from transformers import pipeline
+
+st.title('Sentiment Analyser')
+st.image("https://www.pngall.com/wp-content/uploads/5/Emotion-Transparent.png")
+
+st.write("---")
+st.header('Step 1 - Select a language model')
+col1, col2 = st.columns(2)
+
+selection = "N/A"
+with col1:
+    selection = st.radio("Pick one of the four pre-trained models below:",
+        key = "modelChoice",
+        options = ["DistilBERT", "FinBERT", "SiEBERT", "Twitter-roBERTa"],
+    )
+
+pipe = pipeline('sentiment-analysis')
+if selection == "DistilBERT":
+    pipe = pipeline(model = "distilbert-base-uncased-finetuned-sst-2-english")
+if selection == "FinBERT":
+    pipe = pipeline(model = "yiyanghkust/finbert-tone")
+if selection == "Twitter-roBERTa":
+    pipe = pipeline(model = "cardiffnlp/twitter-roberta-base-sentiment-latest")
+if selection == "SiEBERT":
+    pipe = pipeline(model = "siebert/sentiment-roberta-large-english")
+
+with col2:
+    st.caption('DistilBERT - One of the most popular and widely-used language models. Labels text as POSITIVE or NEGATIVE. Developed by Hugging Face.')
+    st.caption('FinBERT - A finance-oriented model trained with analysis reports. Labels text as POSITIVE, NEGATIVE or NEUTRAL. Developed by yiyanghkust.')
+    st.caption('SiEBERT - A model trained on diverse text sources to improve generalization. Labels text as POSITIVE or NEGATIVE. Developed by siebert.')
+    st.caption('Twitter-roBERTa - A model trained on over 124M tweets. Labels text as POSITIVE, NEGATIVE or NEUTRAL. Developed by cardiffnlp.')
+
+st.write("---")
+st.header('Step 2 - Enter some text')
+text = st.text_area('The sentiment of the text entered here will be determined based on the model you chose above.',
+                   value = "It was the best of times, it was the worst of times.")
+
+st.write("---")
+st.header('Step 3 - View your results')
+
+if text:
+    st.write('Model used: ', selection)
+    out = pipe(text)
+    st.json(out)

requirements.txt

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+streamlit
+opencv-python-headless
+numpy
+easyocr
+Pillow
+torch
+transformers