add files

.gitignore

@@ -0,0 +1,2 @@
+.env
+.DS_Store

EDxHuggingface.py

@@ -0,0 +1,128 @@
+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 = "https://api-inference.huggingface.co/models/bhadresh-savani/bert-base-go-emotion"
+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):
+    data = json.dumps(payload)
+    response = requests.request("POST", API_URL, headers=headers, data=data)
+    return json.loads(response.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']
+}
+
+# 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."
+]
+
+
+# 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
+    response = query(text_input)
+    predicted_probabilities = response[0]
+
+    # Sort the predicted probabilities in descending order
+    sorted_probs = sorted(predicted_probabilities, key=lambda x: x['score'], reverse=True)
+
+    # Get the top 4 emotion categories and their scores
+    top_emotions = sorted_probs[: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.plotly_chart(fig, use_container_width=True)
+

README.md

@@ -0,0 +1,32 @@
+# GoEmotions Dashboard - Analyzing Emotions in Text
+
+This is a Python script that uses Streamlit, Plotly, and the Hugging Face API to create a web-based dashboard for analyzing emotions in text.
+
+## Requirements:
+
+Python 3.7 or higher
+Hugging Face API
+
+## Installation
+
+- Clone this repository to your local machine.
+- Install the required packages using pip:
+
+```bash
+pip install -r requirements.txt
+```
+
+- Create a free account on the Hugging Face website to get an API key.
+
+- 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>`
+
+## Usage
+
+- Navigate to the root directory of the project.
+- Run the Streamlit app by typing `streamlit run app.py` in the command line.
+
+- A web-based dashboard will open in your default browser.
+- Type or paste a text input in the text box provided.
+- 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.

requirements.txt

@@ -0,0 +1,4 @@
+plotly==5.3.1
+streamlit==1.3.0
+requests==2.26.0
+python-dotenv==0.19.1