satisfy milestone-2 requirements

.github/workflows/sync_to_huggingface_hub.yml

@@ -0,0 +1,20 @@
+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://cgr28:[email protected]/spaces/cgr28/cs482-project main

README.md

@@ -1,9 +1,33 @@
+---
+title: Cs482 Project
+emoji: 💻
+colorFrom: pink
+colorTo: purple
+sdk: streamlit
+sdk_version: 1.17.0
+app_file: app.py
+pinned: false
+---
+
 # cs482-project
-## Instructions
-1. Setup Docker using this video [https://youtu.be/pTFZFxd4hOI](https://youtu.be/pTFZFxd4hOI)
 
-## Screenshot
-### Running from container
+## milestone-1
+
+### Instructions
+
+1. Setup Docker using this [video](https://youtu.be/pTFZFxd4hOI)
+
+### Screenshot
+
+#### Running from container
+
 ![Docker Container](docker-container.png)
-### Running user docker run
-![Docker Run](docker-run.png)
+
+#### Running user docker run
+
+![Docker Run](docker-run.png)
+
+## milestone-2
+
+[HF Space](https://huggingface.co/spaces/cgr28/cs482-project)
+

app.py

@@ -0,0 +1,28 @@
+import streamlit as st
+from transformers import AutoTokenizer, RobertaForSequenceClassification
+import numpy as np
+import torch
+
+st.title("CS482 Project Sentiment Analysis")
+
+text = st.text_area(label="Text to be analyzed", value="This sentiment analysis app is great!")
+
+selected_model = st.radio(label="Model", options=["Model 1", "Model 2"])
+
+analyze_button = st.button(label="Analyze")
+
+st.markdown("**:red[Sentiment:]**")
+
+if analyze_button:
+    if selected_model=="Model 1":
+        tokenizer = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-emotion")
+        model = RobertaForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-emotion")
+    else:
+        tokenizer = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment-latest")
+        model = RobertaForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment-latest")
+    inputs = tokenizer(text, return_tensors="pt")
+    with torch.no_grad():
+        logits = model(**inputs).logits
+    prediction_id = logits.argmax().item()
+    results = model.config.id2label[prediction_id]
+    st.write(results)

main.py

@@ -1,3 +0,0 @@
-import torch
-x = torch.rand(5, 3)
-print(x)