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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/bhadresh-savani/bert-base-go-emotion" | |
| API_URL_HS = "https://api-inference.huggingface.co/models/IMSyPP/hate_speech_en" | |
| headers = {"Authorization": f"Bearer {HF_API_KEY}"} | |
| # Set page title | |
| st.title("GoEmotions Dashboard - Analyzing Emotions in Text") | |
| # 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) | |
| 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) | |
| return (json.loads(response_ED.content.decode("utf-8")),json.loads(response_HS.content.decode("utf-8"))) | |
| # Define color map for each emotion category | |
| color_map = { | |
| 'admiration': ['#1f77b4', '#aec7e8', '#ff7f0e', '#d62728'], | |
| 'amusement': ['#ff7f0e', '#ffbb78', '#2ca02c', '#d62728'], | |
| 'anger': ['#d62728', '#ff9896', '#2ca02c', '#bcbd22'], | |
| 'annoyance': ['#d62728', '#ff9896', '#2ca02c', '#bcbd22'], | |
| 'approval': ['#1f77b4', '#aec7e8', '#ff7f0e', '#d62728'], | |
| 'caring': ['#1f77b4', '#aec7e8', '#ff7f0e', '#d62728'], | |
| 'confusion': ['#9467bd', '#c5b0d5', '#ff7f0e', '#d62728'], | |
| 'curiosity': ['#9467bd', '#c5b0d5', '#ff7f0e', '#d62728'], | |
| 'desire': ['#ff7f0e', '#ffbb78', '#2ca02c', '#d62728'], | |
| 'disappointment': ['#d62728', '#ff9896', '#2ca02c', '#bcbd22'], | |
| 'disapproval': ['#d62728', '#ff9896', '#2ca02c', '#bcbd22'], | |
| 'disgust': ['#d62728', '#ff9896', '#2ca02c', '#bcbd22'], | |
| 'embarrassment': ['#9467bd', '#c5b0d5', '#ff7f0e', '#d62728'], | |
| 'excitement': ['#ff7f0e', '#ffbb78', '#2ca02c', '#d62728'], | |
| 'fear': ['#d62728', '#ff9896', '#2ca02c', '#bcbd22'], | |
| 'gratitude': ['#1f77b4', '#aec7e8', '#ff7f0e', '#d62728'], | |
| 'grief': ['#d62728', '#ff9896', '#2ca02c', '#bcbd22'], | |
| 'joy': ['#ff7f0e', '#ffbb78', '#2ca02c', '#d62728'], | |
| 'love': ['#1f77b4', '#aec7e8', '#ff7f0e', '#d62728'], | |
| 'nervousness': ['#9467bd', '#c5b0d5', '#ff7f0e', '#d62728'], | |
| 'optimism': ['#1f77b4', '#aec7e8', '#ff7f0e', '#d62728'], | |
| 'pride': ['#1f77b4', '#aec7e8', '#ff7f0e', '#d62728'], | |
| 'realization': ['#9467bd', '#c5b0d5', '#ff7f0e', '#d62728'], | |
| 'relief': ['#1f77b4', '#aec7e8', '#ff7f0e', '#d62728'], | |
| 'remorse': ['#d62728', '#ff9896', '#2ca02c', '#bcbd22'], | |
| 'sadness': ['#d62728', '#ff9896', '#2ca02c', '#bcbd22'], | |
| 'surprise': ['#9467bd', '#c5b0d5', '#ff7f0e', '#d62728'], | |
| 'neutral': ['#1f77b4', '#aec7e8', '#ff7f0e', '#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!", | |
| ] | |
| # 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_input("Enter text to analyze emotions:", selected_option) | |
| # Add submit button | |
| if st.button("Submit"): | |
| # Call API and get predicted probabilities for each emotion category and hate speech classification | |
| payload = {"inputs": text_input, "use_cache": True, "wait_for_model": True} | |
| response_ED, response_HS = query(payload) | |
| predicted_probabilities_ED = response_ED[0] | |
| predicted_probabilities_HS = response_HS[0] | |
| # Sort the predicted probabilities in descending order | |
| sorted_probs_ED = sorted(predicted_probabilities_ED, key=lambda x: x['score'], reverse=True) | |
| # Get the top 4 emotion categories and their scores | |
| top_emotions = sorted_probs_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): | |
| category = emotion['label'] | |
| color = color_map[category] | |
| value = normalized_scores[i] | |
| row = i // 2 + 1 | |
| col = i % 2 + 1 | |
| 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 layout | |
| 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.header("Emotion Detection") | |
| st.text("") | |
| st.plotly_chart(fig, use_container_width=True) | |
| # Display Hate Speech Classification | |
| hate_detection = label_hs[predicted_probabilities_HS[0]['label']] | |
| st.text("") | |
| st.text("") | |
| st.text("") | |
| st.header("Hate Speech Analysis") | |
| st.text("") | |
| col1, col2 = st.columns(2) | |
| col1.image(f"assets/{hate_detection}.jpg", width=200) | |
| col2.text("") | |
| col2.text("") | |
| col2.text("") | |
| col2.text("") | |
| col2.subheader(f"The given text is {hate_detection}") | |