Restore

.gitattributes

@@ -0,0 +1,2 @@
+myvenv filter=lfs diff=lfs merge=lfs -text
+myenv filter=lfs diff=lfs merge=lfs -text

.github/workflows/check.yml

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+name: Test Model
+
+on:
+  push:
+    branches:
+      - main
+  pull_request:
+    branches:
+      - main
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v3
+
+      - name: Set up Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: '3.8'
+
+      - name: Install dependencies
+        run: |
+          pip install -r requirements.txt
+          pip install pytest
+
+      - name: Run tests
+        run: |
+          pytest
+

.github/workflows/discord_notification.yml

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+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

.github/workflows/main.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@v2
+        with:
+          fetch-depth: 0
+      - name: Add remote 
+        env:
+          CaseStudy1: ${{ secrets.CaseStudy1 }}
+        run: git remote add space https://kroohaniwpi:$CaseStudy1@huggingface.co/spaces/ML-OPS-Grp11/CaseStudy1
+      - name: Push to hub
+        env:
+          CaseStudy1: ${{ secrets.CaseStudy1 }}
+        run: git push --force https://kroohaniwpi:$CaseStudy1@huggingface.co/spaces/ML-OPS-Grp11/CaseStudy1

.gitignore

@@ -160,3 +160,15 @@ cython_debug/
 #  and can be added to the global gitignore or merged into this file.  For a more nuclear
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
 #.idea/
+
+
+# Virtual Environment
+myenv/
+*.myenv/
+.myenv/
+env/
+*.env/
+.env/
+venv/
+*.venv/
+.venv/

README.md

@@ -1 +1,20 @@
-# CS553CaseStudy1
+---
+title: CaseStudy1
+emoji: 💬
+colorFrom: yellow
+colorTo: purple
+sdk: gradio
+sdk_version: 4.36.1
+app_file: app.py
+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).
+
+Please note the makefile and github action workflow was used from https://github.com/nogibjj/hugging-face
+
+A pipeline has been added to https://github.com/jvroo/CS553CaseStudy1.git
+
+This is for Group 11. 
+Authors: Keon Roohani, Shipra Poojary, Jagruti Chitte. 

app.py

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+import gradio as gr
+from transformers import pipeline
+import matplotlib.pyplot as plt
+import numpy as np
+
+# Define models for local and remote inference
+local_model = "distilbert-base-uncased-finetuned-sst-2-english"
+remote_model = "distilbert-base-uncased-finetuned-sst-2-english"  # You can use the same model for both for now
+
+# Load the local sentiment analysis pipeline with the specified model
+local_pipeline = pipeline("sentiment-analysis", model=local_model)
+
+# Initialize the inference pipeline for remote model (same model for simplicity here)
+remote_pipeline = pipeline("sentiment-analysis", model=remote_model)
+
+# Function to perform sentiment analysis using the local pipeline
+def local_sentiment_analysis(review):
+    try:
+        result = local_pipeline(review)
+        sentiment = result[0]['label']
+        score = result[0]['score']
+        return sentiment, score
+    except Exception as e:
+        return f"Error: {str(e)}", 0.0
+
+# Function to perform sentiment analysis using the remote pipeline
+def remote_sentiment_analysis(review):
+    try:
+        result = remote_pipeline(review)
+        sentiment = result[0]['label']
+        score = result[0]['score']
+        return sentiment, score
+    except Exception as e:
+        return f"Error: {str(e)}", 0.0
+
+# Function to analyze sentiment and return the result as a string and plot
+def analyze_sentiment(review, mode):
+    if not review.strip():
+        return "Error: Review text cannot be empty.", None
+
+    if mode == "Local Pipeline":
+        sentiment, score = local_sentiment_analysis(review)
+        model_info = f"Using local model: {local_model}"
+    elif mode == "Inference API":
+        sentiment, score = remote_sentiment_analysis(review)
+        model_info = f"Using remote model: {remote_model}"
+    else:
+        return "Invalid mode selected.", None
+
+    # Format the sentiment result
+    result_text = f"Sentiment: {sentiment}, Confidence: {score:.2f}\n{model_info}"
+
+    # Enhanced plot
+    fig, ax = plt.subplots(figsize=(8, 5))
+    
+    categories = ['POSITIVE', 'NEGATIVE']
+    sentiment_scores = [score if sentiment == 'POSITIVE' else (1 - score), score if sentiment == 'NEGATIVE' else (1 - score)]
+    colors = ['#4CAF50' if sentiment == 'POSITIVE' else '#F44336', '#F44336']
+
+    bars = ax.bar(categories, sentiment_scores, color=colors)
+    ax.set_ylim(0, 1)
+    ax.set_ylabel('Confidence Score')
+    ax.set_title('Sentiment Analysis Result')
+
+    # Add text labels above bars
+    for bar in bars:
+        height = bar.get_height()
+        ax.annotate(f'{height:.2f}',
+                    xy=(bar.get_x() + bar.get_width() / 2, height),
+                    xytext=(0, 3),  # 3 points vertical offset
+                    textcoords="offset points",
+                    ha='center', va='bottom')
+
+    return result_text, fig  # Return the Matplotlib figure directly
+
+# Custom CSS for styling
+custom_css = """
+body {
+    background-color: #2c2f33;
+    color: #f0f0f0;
+    font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+}
+
+.gr-textbox, .gr-radio {
+    margin-bottom: 20px;
+    border: 1px solid #444;
+    padding: 10px;
+    border-radius: 8px;
+    box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
+    background-color: #3a3d41;
+}
+
+.gr-button {
+    background-color: #7289da;
+    color: white;
+    border: none;
+    padding: 10px 20px;
+    font-size: 16px;
+    cursor: pointer;
+    transition: 0.3s;
+    border-radius: 8px;
+    margin-top: 10px;
+}
+
+.gr-button:hover {
+    background-color: #5b6eae;
+}
+
+#component-2 {
+    font-size: 18px;
+    margin-bottom: 20px;
+}
+
+#component-3 {
+    font-size: 18px;
+    margin-bottom: 20px;
+}
+
+#component-4 {
+    font-size: 16px;
+    padding: 15px;
+    background-color: #3a3d41;
+    border: 1px solid #444;
+    border-radius: 8px;
+    box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
+}
+
+h1 {
+    text-align: center;
+    font-size: 32px;
+    margin-bottom: 40px;
+    color: #7289da;
+}
+"""
+
+# Gradio interface
+with gr.Blocks(css=custom_css) as demo:
+    gr.Markdown("<h1>Movie Review Sentiment Analysis</h1>")
+
+    with gr.Column():
+        with gr.Row():
+            review_input = gr.Textbox(
+                label="Enter Movie Review", placeholder="Type your movie review here...", lines=4
+            )
+
+        with gr.Row():
+            mode_input = gr.Radio(
+                ["Local Pipeline", "Inference API"], label="Select Processing Mode", value="Inference API"
+            )
+
+        with gr.Row():
+            analyze_button = gr.Button("Analyze Sentiment")
+
+        # Output box to display the sentiment analysis result
+        sentiment_output = gr.Textbox(label="Sentiment Analysis Result", interactive=False)
+        # Plot output to display the sentiment score graph
+        plot_output = gr.Plot(label="Sentiment Score Graph")
+
+    analyze_button.click(analyze_sentiment, [review_input, mode_input], [sentiment_output, plot_output])
+
+# Run the Gradio app
+if __name__ == "__main__":
+    demo.launch('share=True')

requirements.txt

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+huggingface_hub==0.23.*
+gradio==4.39.*
+torch==2.4.*
+transformers==4.43.*
+accelerate==0.33.*
+pytest

tests/test_model.py

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+# test_model.py
+# test_model.py
+import pytest
+from app import local_pipeline  # Import the local_pipeline from app.py
+
+def test_local_sentiment_analysis():
+    review = "I love this movie!"
+    result = local_pipeline(review)
+    assert result[0]['label'] in ['POSITIVE', 'NEGATIVE']
+    assert 0.0 <= result[0]['score'] <= 1.0
+
+def test_local_sentiment_analysis_new():
+    """Test local sentiment analysis"""
+    review = "I love this movie!"
+    
+    # Run the local sentiment analysis pipeline
+    result = local_pipeline(review)
+    
+    # Assert that the result contains the expected keys
+    assert 'label' in result[0], "Local result does not contain 'label'"
+    assert 'score' in result[0], "Local result does not contain 'score'"
+    
+    # Check if the label is one of the expected sentiment labels
+    assert result[0]['label'] in ['POSITIVE', 'NEGATIVE'], "Unexpected sentiment label"
+    
+    # Check if the score is a float
+    assert isinstance(result[0]['score'], float), "Score should be a float"
+
+    # Print result for manual inspection (optional)
+    print("Local Analysis Result:", result)
+