Final Changes and README Update

.github/workflows/discord_notification.yml

@@ -1,17 +0,0 @@
-name: Discord Notification
-on:
-  push:
-    branches: [main]
-
-jobs:
-  notify:
-    runs-on: ubuntu-latest
-
-    steps:
-      - name: Send Discord notification
-        env:
-          DISCORD_WEBHOOK_URL: ${{ secrets.DISCORD_WEBHOOK }}
-        run: |
-          curl -X POST -H "Content-Type: application/json" \
-          -d "{\"content\": \"A new commit was pushed to the main branch by $GITHUB_ACTOR. 😄 CS553_CaseStudy_Group 11 rock!\"}" \
-          $DISCORD_WEBHOOK_URL

.github/workflows/main.yml

@@ -37,3 +37,14 @@ jobs:
         env:
           CaseStudy1: ${{ secrets.CaseStudy1 }}
         run: git push --force https://kroohaniwpi:$CaseStudy1@huggingface.co/spaces/ML-OPS-Grp11/CaseStudy1
+notify:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Send Discord notification
+        env:
+          DISCORD_WEBHOOK_URL: ${{ secrets.DISCORD_WEBHOOK }}
+        run: |
+          curl -X POST -H "Content-Type: application/json" \
+          -d "{\"content\": \"A new commit was pushed to the main branch by $GITHUB_ACTOR. 😄 CS553_CaseStudy_Group 11 rock!\"}" \
+          $DISCORD_WEBHOOK_URL

README.md

@@ -10,13 +10,39 @@ pinned: false
 license: mit
 ---
 
-An example chatbot using [Gradio](https://gradio.app), [`huggingface_hub`](https://huggingface.co/docs/huggingface_hub/v0.22.2/en/index), and the [Hugging Face Inference API](https://huggingface.co/docs/api-inference/index).
+# CS553 Case Study 1 - Group 11
 
-Please note the makefile and github action workflow was used from https://github.com/nogibjj/hugging-face
+A pipeline has been added to the [CS553CaseStudy1 GitHub repository](https://github.com/jvroo/CS553CaseStudy1.git) to ensure that [Hugging Face Space](https://huggingface.co/spaces/ML-OPS-Grp11/CaseStudy1) is automatically updated.
 
-A pipeline has been added to https://github.com/jvroo/CS553CaseStudy1.git
+## Group 11
+**Authors**: Keon Roohani, Shipra Poojary, Jagruti Chitte
 
-This is for Group 11. 
-Authors: Keon Roohani, Shipra Poojary, Jagruti Chitte. 
+## Assignment Overview
 
-Attempt made to connect to discord notifications. 
+**Note**: The work done in this assignment used (Paffenroth, 2024) and (Wu, 2024) as a starting point. Additionally, ChatGPT was used on occasion for formatting of text. For instance this README was formatted as such. 
+
+### Part 1.a: Inference Client Model
+The use of a Client Inference model from Hugging Face was done using `"siebert/sentiment-roberta-large-english"`. This is a text sentiment analysis LLM for the English language (Hartmann et al., 2023).
+
+### Part 2.a: Local Model
+The use of a local model was done using `"distilbert-base-uncased-finetuned-sst-2-english"` (Sanh et al., 2020). This is a lightweight version of BERT that can be used to classify text sentiment.
+
+### CI/CD Implementation
+The CI/CD of this project is implemented using `main.yml`. This file ensures that pytest is run for the local model before any pushes are made to Hugging Face. After a successful test, the main commit is pushed to Hugging Face automatically. This covers sections 1.b and 2.b of the assignment.
+
+### Part 3: Report Submission
+The report for part 3 of the Case Study is submitted on Canvas.
+
+### Part 4: Video Submission
+The video for part 4 of the Case Study is submitted on Canvas.
+
+### Part 5: Discord Notifications
+An attempt was made to connect to Discord notifications. This was done by creating a Discord server and a channel within it. A webhook was created and linked from the channel to the GitHub repository via the secrets in settings. A GitHub Action uses this secret to push a notification every time a commit to the main branch is made. This GitHub action can be found in `main.yml` for your reference. This covers section 5 of the assignment.
+
+**Note**: The GitHub action workflow was used from [nogibjj's GitHub repository](https://github.com/nogibjj/hugging-face) as a reference. His corresponding YouTube video was particularly helpful, which can be found [here](https://www.youtube.com/watch?v=VYSGjUa5sc4&feature=youtu.be).
+
+## References
+- Hartmann, J., Heitmann, M., Siebert, C., & Schamp, C. (2023). More than a Feeling: Accuracy and Application of Sentiment Analysis. *International Journal of Research in Marketing, 40*(1), 75-87.
+- Sanh, V., Debut, L., Chaumond, J., & Wolf, T. (2020). DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter.
+- Paffenroth (2024). Prof Paffenroth's Chatbot. [Hugging Face Space](https://huggingface.co/spaces/rcpaffenroth/chatbot).
+- Wu, Y. (2024). Yang's Chatbot. [Hugging Face Space](https://huggingface.co/spaces/YangWu001/CS553_Example).

app.py

@@ -107,20 +107,20 @@ def analyze_sentiment(movie_name, review, mode):
     # Return the Matplotlib figure directly to plot_output
     return result_text, movie_description, fig
 
-# Function to format movie information into a prettier display
+# Function to format movie information into a plain text format
 def format_movie_description(movie_info):
     if 'Error' in movie_info:
-        return f"**Error:** {movie_info['Error']}"
+        return f"Error: {movie_info['Error']}"
     
-    return f"""
-    **🎬 Title:** {movie_info['Title']}  
-    **📅 Year:** {movie_info['Year']}  
-    **👨‍🎤 Actors:** {movie_info['Actors']}  
-    **🎥 Director:** {movie_info['Director']}  
-    **🏆 Rating:** {movie_info['Rating']}  
-    **📖 Genre:** {movie_info['Genre']}  
-    **📝 Description:** {movie_info['Description']}  
-    """
+    return (
+        f"Title: {movie_info['Title']}\n"
+        f"Year: {movie_info['Year']}\n"
+        f"Actors: {movie_info['Actors']}\n"
+        f"Director: {movie_info['Director']}\n"
+        f"Rating: {movie_info['Rating']}\n"
+        f"Genre: {movie_info['Genre']}\n"
+        f"Description: {movie_info['Description']}"
+    )
 
 # Enhanced CSS for a modern, clean look
 custom_css = """
@@ -153,15 +153,6 @@ body {
     color: #ffffff;
 }
 
-.gr-markdown {
-    margin-bottom: 20px;
-    padding: 12px;
-    border-radius: 8px;
-    border: 1px solid #ddd;
-    background-color: #ffffff;
-    color: #333;
-}
-
 .gr-button {
     background-color: #4CAF50;
     color: white;
@@ -206,7 +197,7 @@ with gr.Blocks(css=custom_css) as demo:
 
         with gr.Column(scale=2):
             sentiment_output = gr.Textbox(label="🗨️ Sentiment Analysis Result", interactive=False)
-            movie_description_output = gr.Markdown(label="📃 Movie Description")
+            movie_description_output = gr.Textbox(label="📃 Movie Description", interactive=False, lines=10)
             plot_output = gr.Plot(label="📊 Sentiment Score Graph")
 
     analyze_button.click(