Spaces:
Runtime error
Runtime error
| 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: | |
| # Add page description | |
| description = "The GoEmotions Dashboard is a web-based user interface for analyzing emotions in text. The dashboard is powered by pre-trained natural language processing models that can detect emotions, identify hatespeech and sexist speech in text input. 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] | |
| # 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) |