streamlit app

.github/workflows/check.yml

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+name: Check file size
+on:               # or directly `on: [push]` to run the action on every push on any branch
+  pull_request:
+    branches: [main]
+
+  # to run this workflow manually from the Actions tab
+  workflow_dispatch:
+
+jobs:
+  sync-to-hub:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Check large files
+        uses: ActionsDesk/lfs-warning@v2.0
+        with:
+          filesizelimit: 10485760 # this is 10MB so we can sync to HF Spaces

.github/workflows/main.yml

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+name: Sync to Hugging Face hub
+on:
+  push:
+  pull_request:
+    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: ${{ secrets.HF_TOKEN }}
+          HF_username: devanshsrivastav # your huggingface.co username here (check your profile page)
+          HF_space_name: GoEmotions  # name of the hub space to which you want to push the model
+        run: git push https://$HF_username:$HF_TOKEN@huggingface.co/spaces/$HF_username/$HF_space_name main

.gitignore

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+.env
+.DS_Store
+tumai
+venv

.streamlit/config.toml

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+[theme]
+base="light"

README.md

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+---
+license: apache-2.0
+title: GoEmotions Dashboard
+sdk: streamlit
+sdk_version: "1.22.0"
+app_file: app.py
+---
+
+# GoEmotions Dashboard - Analyzing Emotions in Text
+
+This is a Python script that uses Streamlit, Plotly, and the Hugging Face Inference API to create a web-based dashboard for analyzing emotions in text. Finally this dashboard is deployed on Hugging Face Spaces using GitHub Actions.
+
+## Pre-requisites:
+
+- Python 3.7 or higher
+
+## Project Structure:
+```dir
+GoEmotions/
+├── app.py
+├── requirements.txt
+├── .env
+├── README.md
+├── assets/
+└── .github/workflows
+```
+
+## Setup
+
+`Step 1` - Clone this repository to your local machine using the following command, or open the repository in GitHub Codespaces.
+
+```bash
+git clone https://github.com/devansh-srivastav/GoEmotions.git
+```
+
+`Step 2` - Navigate to the root directory of the project.
+
+```bash
+cd GoEmotions
+```
+
+`Step 3` - Create and activate a new python virtual environment: (This step can be skipped if working on GitHub Codespaces!)
+
+```bash
+python -m venv venv
+```
+
+For Windows
+```bash
+venv\Scripts\activate
+```
+
+For Linux/MacOS
+
+```bash
+source venv/bin/activate
+```
+
+`Step 4` - Install the required packages using pip: (This step can be skipped if working on GitHub Codespaces as it automatically installs the requirements!)
+
+```bash
+pip install -r requirements.txt
+```
+
+`Step 5`- Create a free account on the [Hugging Face website](https://huggingface.co/) and generate an API key (read).
+
+`Step 6`
+
+- Create a `.env` file in the root directory of the project and add your
+- Hugging Face API key like this: `HF_API_KEY=<your_api_key_here>`
+
+`Step 7` - Run the Streamlit app.
+
+```bash
+streamlit run app.py
+```
+
+or 
+
+```bash
+python -m streamlit run app.py
+```
+
+- If you want to run this application on GitHub Codespaces, you will need to add the following flags to the `streamlit run` command:
+
+```bash
+python -m streamlit run app.py --server.enableCORS false --server.enableXsrfProtection false
+```
+
+## Deployment to Spaces (CI/CD)
+
+`Step 1`
+Commit your code and push it to your GitHub repository
+
+`Step 2`
+Create a new Space on Hugging Face, add it as an additional remote to git and force push your code on Spaces:
+
+```bash
+git remote add space https://huggingface.co/spaces/HF_USERNAME/SPACE_NAME
+```
+
+```bash
+git push --force space main
+```
+
+`Step 3`
+In the main.yml, add your Hugging Face username and Space name to the variables 'HF_username' and 'HF_space_name'
+
+`Step 4`
+Create a new API key on Hugging Face (write) and add it as a secret to your GitHub Repository naming it as 'HF_TOKEN'.
+
+`Step 5`
+Trigger the CI/CD pipeline by a push or a pull request to your main branch.
+
+## Usage:
+
+- A web-based dashboard will open in your default browser.
+- Type or paste a text input in the text box provided.
+- The dashboard will visualise the detected emotions in a set of gauges, with each gauge representing the intensity of a specific emotion category. The gauge colors are based on a predefined color map for each emotion category.
+- Moreover, the dashboard will display the results from Hate Speech Analysis and Sexism Detection models. 

app.py

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+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
+import streamlit as st
+import requests
+import json
+import os
+from dotenv import load_dotenv
+load_dotenv()
+
+# AI model code
+HF_API_KEY = os.getenv("HF_API_KEY")
+
+# API_URL_ED = "https://api-inference.huggingface.co/models/j-hartmann/emotion-english-distilroberta-base" #alternate ED model(slow loading on first run)
+API_URL_ED = "https://api-inference.huggingface.co/models/bhadresh-savani/bert-base-go-emotion"
+API_URL_HS = "https://api-inference.huggingface.co/models/IMSyPP/hate_speech_en"
+API_URL_SD = "https://api-inference.huggingface.co/models/NLP-LTU/bertweet-large-sexism-detector"
+
+headers = {"Authorization": f"Bearer {HF_API_KEY}"}
+
+def query(payload):
+    response_ED = requests.request("POST", API_URL_ED, headers=headers, json=payload)
+    response_HS = requests.request("POST", API_URL_HS, headers=headers, json=payload)
+    response_SD = requests.request("POST", API_URL_SD, headers=headers, json=payload)
+    return (json.loads(response_ED.content.decode("utf-8")),json.loads(response_HS.content.decode("utf-8")),json.loads(response_SD.content.decode("utf-8")))
+
+st.set_page_config(
+    page_title="GoEmotions Dashboard",
+    layout="wide"
+)
+
+# Set page title
+st.title("GoEmotions Dashboard - Analyzing Emotions in Text")
+
+# Define color map for each emotion category
+color_map = {
+    'admiration': ['#1f77b4', '#98df8a', '#2ca02c', '#d62728'],
+    'amusement': ['#ff7f0e', '#98df8a', '#2ca02c', '#d62728'],
+    'anger': ['#ffbb78', '#ff7f0e', '#d62728', '#bcbd22'],
+    'annoyance': ['#ffbb78', '#ff7f0e', '#d62728', '#bcbd22'],
+    'approval': ['#1f77b4', '#98df8a', '#2ca02c', '#d62728'],
+    'caring': ['#98df8a', '#2ca02c', '#FF69B4', '#d62728'],
+    'confusion': ['#ffbb78', '#ff7f0e', '#9467bd', '#d62728'],
+    'curiosity': ['#ffbb78', '#ff7f0e', '#9467bd', '#d62728'],
+    'desire': ['#2ca02c', '#ff7f0e', '#98df8a', '#d62728'],
+    'disappointment': ['#ffbb78', '#ff7f0e', '#d62728', '#bcbd22'],
+    'disapproval': ['#ffbb78', '#ff7f0e', '#d62728', '#bcbd22'],
+    'disgust': ['#ffbb78', '#ff7f0e', '#d62728', '#bcbd22'],
+    'embarrassment': ['#ffbb78', '#ff7f0e', '#9467bd', '#d62728'],
+    'excitement': ['#ff7f0e', '#2ca02c', '#98df8a', '#d62728'],
+    'fear': ['#ffbb78', '#ff7f0e', '#d62728', '#bcbd22'],
+    'gratitude': ['#98df8a', '#2ca02c', '#1f77b4', '#d62728'],
+    'grief': ['#ffbb78', '#d62728', '#bcbd22', '#ff7f0e'],
+    'joy': ['#ff7f0e', '#98df8a', '#2ca02c', '#d62728'],
+    'love': ['#FF69B4', '#98df8a', '#2ca02c', '#d62728'],
+    'nervousness': ['#ffbb78', '#ff7f0e', '#9467bd', '#d62728'],
+    'optimism': ['#98df8a', '#2ca02c', '#1f77b4', '#d62728'],
+    'pride': ['#98df8a', '#ff7f0e', '#1f77b4', '#d62728'],
+    'realization': ['#9467bd', '#ff7f0e', '#ffbb78', '#d62728'],
+    'relief': ['#1f77b4', '#98df8a', '#2ca02c', '#d62728'],
+    'remorse': ['#ffbb78', '#ff7f0e', '#d62728', '#bcbd22'],
+    'sadness': ['#ffbb78', '#ff7f0e', '#d62728', '#bcbd22'],
+    'surprise': ['#ff7f0e', '#ffbb78', '#9467bd', '#d62728'],
+    'neutral': ['#2ca02c', '#98df8a', '#1f77b4', '#d62728']
+}
+
+
+# Labels for Hate Speech Classification
+label_hs = {"LABEL_0": "Acceptable", "LABEL_1": "Inappropriate", "LABEL_2": "Offensive", "LABEL_3": "Violent"}
+
+
+# Define default options
+
+default_options = [
+    "I'm so excited for my vacation next week!",
+    "I'm feeling so stressed about work.",
+    "I just received great news from my doctor!",
+    "I can't wait to see my best friend tomorrow.",
+    "I'm feeling so lonely and sad today."
+    "I'm so angry at my neighbor for being so rude.",
+    "You are so annoying!",
+    "You people from small towns are so dumb.",
+    "If you don't agree with me, you are a moron.",
+    "I hate you so much!",
+    "If you don't listen to me, I'll beat you up!",
+]
+
+with st.sidebar:
+
+    # Create dropdown with default options
+    selected_option = st.selectbox("Select a default option or enter your own text:", default_options)
+
+    # Display text input with selected option as default value
+    text_input = st.text_area("Enter text to analyze emotions:", value = selected_option, height=100)
+
+    # Add submit button
+    submit = st.button("Submit")
+
+# If submit button is clicked
+if submit:
+
+    # Call API and get predicted probabilities for each emotion category and hate speech classification
+    payload = {"inputs": text_input, "options": {"wait_for_model": True, "use_cache": True}}
+    response_ED, response_HS, response_SD = query(payload)
+    predicted_probabilities_ED = response_ED[0]
+    predicted_probabilities_HS = response_HS[0]
+    predicted_probabilities_SD = response_SD[0]
+
+    # Creating columns to visualize the results 
+    ED, _, HS, __, SD = st.columns([4,1,2,1,2])
+
+    with ED:
+        # Get the top 4 emotion categories and their scores
+        top_emotions = predicted_probabilities_ED[:4]
+        top_scores = [e['score'] for e in top_emotions]
+
+        # Create the gauge charts for the top 4 emotion categories
+        fig = make_subplots(rows=2, cols=2, specs=[[{'type': 'indicator'}, {'type': 'indicator'}],
+                                                    [{'type': 'indicator'}, {'type': 'indicator'}]],
+                            vertical_spacing=0.4)
+
+        for i, emotion in enumerate(top_emotions):
+            # Get the emotion category, color, and normalized score for the current emotion
+            category = emotion['label']
+            color = color_map[category]
+            value = top_scores[i] * 100
+            
+            # Calculate the row and column position for adding the trace to the subplots
+            row = i // 2 + 1
+            col = i % 2 + 1
+            
+            # Add a gauge chart trace for the current emotion category
+            fig.add_trace(go.Indicator(
+                domain={'x': [0, 1], 'y': [0, 1]},
+                value=value,
+                mode="gauge+number",
+                title={'text': category.capitalize()},
+                gauge={'axis': {'range': [None, 100]},
+                    'bar': {'color': color[3]},
+                    'bgcolor': 'white',
+                    'borderwidth': 2,
+                    'bordercolor': color[1],
+                    'steps': [{'range': [0, 33], 'color': color[0]},
+                                {'range': [33, 66], 'color': color[1]},
+                                {'range': [66, 100], 'color': color[2]}],
+                    'threshold': {'line': {'color': "black", 'width': 4},
+                                    'thickness': 0.5,
+                                    'value': 50}}), row=row, col=col)
+
+        # Update the layout of the figure
+        fig.update_layout(height=400, margin=dict(t=50, b=5, l=0, r=0))
+
+
+        # Display gauge charts
+        st.text("")
+        st.text("")
+        st.text("")
+        st.subheader("Emotion Detection")
+        st.text("")
+        st.plotly_chart(fig, use_container_width=True)
+    
+    with _:
+        st.text("")
+        
+
+    with HS:
+        # Display Hate Speech Classification
+        hate_detection = label_hs[predicted_probabilities_HS[0]['label']]
+        st.text("")
+        st.text("")
+        st.text("")
+        st.subheader("Hate Speech Analysis")
+        st.text("")
+        st.image(f"assets/{hate_detection}.jpg", width=200)
+        st.text("")
+        st.text("")
+        st.markdown(f"#### The given text is: {hate_detection}")
+
+    with __:
+        st.text("")
+
+    with SD:
+        label_SD = predicted_probabilities_SD[0]['label'].title()
+        st.text("")
+        st.text("")
+        st.text("")
+        st.subheader("Sexism Detection")
+        st.text("")
+        st.image(f"assets/{label_SD}.jpg", width=200)
+        st.text("")
+        st.text("")
+        st.markdown(f"#### The given text is: {label_SD}")
+
+        
+
+
+ 
+
+
+hide_st_style = """
+            <style>
+            #MainMenu {visibility: hidden;}
+            footer {visibility: hidden;}
+            </style>
+            """
+st.markdown(hide_st_style, unsafe_allow_html=True)

requirements.txt

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+plotly==5.3.1
+streamlit==1.22.0
+requests==2.26.0
+python-dotenv==0.19.1
+protobuf==3.20.*
+altair<5