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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}"} | |
| st.set_page_config( | |
| page_title="GoEmotions Dashboard", | |
| layout="wide" | |
| ) | |
| # Set page title | |
| st.title("GoEmotions Dashboard - Analyzing Emotions in Text") | |
| 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"))) | |
| # 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: | |
| # Add page description | |
| description = "The GoEmotions Dashboard is a web-based user interface for analyzing emotions in text. The dashboard is powered by a pre-trained natural language processing model that can detect emotions in text input. Users can input any text and the dashboard will display 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. This dashboard is useful for anyone who wants to understand the emotional content of a text, including content creators, marketers, and researchers." | |
| #st.markdown(description) | |
| # 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] | |
| # Normalize the scores so that they add up to 100% | |
| total = sum(top_scores) | |
| normalized_scores = [score/total * 100 for score in top_scores] | |
| # Create the gauge charts for the top 4 emotion categories using the normalized scores | |
| 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 = normalized_scores[i] | |
| # 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: | |
| st.text("") | |
| st.text("") | |
| st.text("") | |
| st.subheader("Sexism Detection") | |
| st.text("") | |
| label_SD = predicted_probabilities_SD[0]['label'].title() | |
| 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) |