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 = """ """ st.markdown(hide_st_style, unsafe_allow_html=True)