conversate

.gitignore

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+.env

.streamlit/config.toml

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+[theme]
+base="light"

README.md

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+---
+license: apache-2.0
+title: Psychological Safety Dashboard
+sdk: streamlit
+sdk_version: "1.35.0"
+app_file: app.py
+---
+
+# Conversate: Psychological Safety in the Workplace

app.py

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+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
+import streamlit as st
+import json
+import os
+import pandas as pd
+from mistralai.client import MistralClient
+from mistralai.models.chat_completion import ChatMessage
+from dotenv import load_dotenv
+load_dotenv()
+
+api_key = os.getenv("MISTRAL_API_KEY")
+model = "mistral-large-latest"
+client = MistralClient(api_key=api_key)
+
+st.set_page_config(
+    page_title="Psychological Safety Dashboard",
+    layout="wide"
+)
+
+# Set page title
+st.title("Conversate: Psychological Safety in the Workplace")
+
+uploaded_file = st.file_uploader("Upload the slack conversation", type=['json'])
+
+# Function to process uploaded file
+@st.cache_data(show_spinner=False)
+def process_file(uploaded_file):
+    json_data = json.load(uploaded_file)
+    messages_with_profile = [item for item in json_data if "user_profile" in item]
+
+    conversation = []
+    for item in messages_with_profile:
+        d = {"user": item["user_profile"]["real_name"], "message": item["text"]}
+        conversation.append(d)
+
+    df = []
+    for con in conversation:
+        user = con['user']
+        message = con['message']
+        messages = [
+            ChatMessage(
+                role="user",
+                content=f""" Given the message {message}, select the best label for the following categories that fits the message, omit any explanations or details and just respond in form of a python dictionary without any additional spaces or new lines. The dictionary should be in a single line without additional spaces. The keys and values of the dictionary must be enclosed with double quotes:\
+                    sentiment = ['positive', 'negative', 'neutral'],\
+                    emotions = [ 'admiration', 'amusement', 'anger', 'annoyance', 'approval', 'caring', 'confusion', 'curiosity', 'desire', 'disappointment', 'disapproval', 'disgust', 'embarrassment', 'excitement', 'fear', 'gratitude', 'grief', 'joy', 'love', 'nervousness', 'optimism', 'pride', 'realization', 'relief', 'remorse', 'sadness', 'surprise', 'neutral'],\
+                    toxicity = ['yes', 'no'],\
+                    harassment indicators = ['harassment', 'hate speech', 'bullying', 'none'],\
+                    sexist = ['yes', 'no'],\
+                    spam = ['yes', 'no'],\
+                    racism = ['yes', 'no'],\
+                    profanity = ['yes', 'no']"""
+            )
+        ]
+
+        # No streaming
+        chat_response = client.chat(
+            model=model,
+            messages=messages,
+        )
+
+        res = json.loads(chat_response.choices[0].message.content)
+
+        df.append({
+            'user': user,
+            'message': message,
+            'sentiment': res['sentiment'],
+            'emotions': res['emotions'],
+            'toxicity': res['toxicity'],
+            'harassment indicators': res['harassment indicators'],
+            'sexist': res['sexist'],
+            'spam': res['spam'],
+            'racism': res['racism'],
+            'profanity': res['profanity']
+        })
+
+    return pd.DataFrame(df)
+
+# Load data if file is uploaded
+if uploaded_file is not None:
+    with st.spinner(f"Analyzing the conversation... (This may take a while depending on the size of the conversation)"):
+        data = process_file(uploaded_file)
+
+    default_options = list(set(data['user'])) + ['Entire Team']
+
+    # 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']
+    }
+
+    with st.sidebar:
+
+        # Create dropdown with default options
+        selected_option = st.selectbox("Select the team member:", default_options)
+
+        # Add submit button
+        submit = st.button("Submit")
+
+    if submit:
+        with st.spinner('Processing messages by {s}...'.format(s=selected_option)):
+            
+
+            if selected_option == 'Entire Team':
+                msgs = list(data['message'])
+                messages = [
+                ChatMessage(role="user", content=" Given the messages by the team, describe their communication style. What is they doing right and wrong and how should it be improved for effective communication. What must be done by this team to ensure pyschological safety in the team. Explain in detail. The messages by this team are {m}".format(m=msgs)
+                            )
+                ]
+            else:
+                msgs = list(data[data['user'] == selected_option]['message'])
+                messages = [
+                ChatMessage(role="user", content=" Given the messages by {s}, describe his/her communication style. What is he/she doing right and wrong and how should it be improved for effective communication. What must be done by this person to ensure pyschological safety in the team. Explain in detail. The messages by this person are {m}".format(s=selected_option, m=msgs)
+                            )
+                ]
+
+            # No streaming
+            chat_response = client.chat(
+                model=model,
+                messages=messages,
+            ) 
+
+            st.subheader(f"Team Member Selected: {selected_option}")
+
+            with st.expander("See explanation"):
+                st.write(chat_response.choices[0].message.content)
+
+
+            st.write("")
+            st.write("")
+            st.write("")
+
+            senti, _, emot = st.columns([4.5,1,4.5])
+
+            with senti:
+            
+                if selected_option == 'Entire Team':
+                    sentiment_labels = list(dict(data['sentiment'].value_counts()).keys())
+                    sentiment_values = list(dict(data['sentiment'].value_counts()).values())
+                else:
+                # Sentiment counts
+                    sentiment_labels = list(dict(data[data['user'] == selected_option]['sentiment'].value_counts()).keys())
+                    sentiment_values = list(dict(data[data['user'] == selected_option]['sentiment'].value_counts()).values())
+
+                # Define colors for each sentiment category
+                colors = ['lightblue', 'lightcoral', 'lightgreen']
+
+                # Create a pie chart
+                fig = go.Figure(data=[go.Pie(labels=sentiment_labels, values=sentiment_values)])
+
+                # Update pie chart layout
+                fig.update_traces(hoverinfo='label+percent', textinfo='value', textfont_size=20,
+                                marker=dict(colors=colors, line=dict(color='#000000', width=2)))
+
+                # Set title
+                # fig.update_layout(title='Sentiment Distribution')
+                st.subheader("Sentiment Distribution")
+
+                # Display pie chart
+                st.plotly_chart(fig, use_container_width=True)
+
+            with _:
+                st.write("")
+
+            with emot:
+
+                if selected_option == 'Entire Team':
+                    emotion_labels = list(dict(data['emotions'].value_counts()).keys())
+                    emotion_values = list(dict(data['emotions'].value_counts()).values())
+                else:
+                
+                    emotion_labels = list(dict(data[data['user'] == selected_option]['emotions'].value_counts()).keys())
+                    emotion_values = list(dict(data[data['user'] == selected_option]['emotions'].value_counts()).values())
+
+                predicted_probabilities_ED = [count / sum(emotion_values) for count in emotion_values]
+
+                top_emotions = emotion_labels[:4]
+                top_scores = predicted_probabilities_ED[:4]
+                # 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
+                    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.subheader("Emotion Detection")
+            
+                st.plotly_chart(fig, use_container_width=True)
+
+            st.write("")
+            st.write("")
+            st.write("")
+
+            tox, rac, sex, spam, prof = st.columns([2,2,2,2,2])
+
+            with tox:
+
+                #Toxicity
+                toxicity = False
+                if selected_option == 'Entire Team':
+                    toxicity = 'yes' in dict(data['toxicity'].value_counts())
+                else:
+                    toxicity = 'yes' in dict(data[data['user'] == selected_option]['toxicity'].value_counts())
+
+                if toxicity:
+                    st.subheader("Toxicity detected in the conversation.")
+                    st.image(f"imgs/toxic_yes.jpeg", width=200)
+                else:
+                    st.subheader("No toxicity detected in the conversation.")
+                    st.image(f"imgs/toxic_no.jpeg", width=200)
+
+            with rac:
+                #Racism
+                racism = False
+                if selected_option == 'Entire Team':
+                    racism = 'yes' in dict(data['racism'].value_counts())
+                else:
+                    racism = 'yes' in dict(data[data['user'] == selected_option]['racism'].value_counts())
+
+                if racism:
+                    st.subheader("Racism detected in the conversation.")
+                    st.image(f"imgs/racism_yes.jpeg", width=200)
+                else:
+                    st.subheader("No racism detected in the conversation.")
+                    st.image(f"imgs/racism_no.jpeg", width=200)
+
+            with sex:
+                #Sexism
+                sexism = False
+                if selected_option == 'Entire Team':
+                    sexism = 'yes' in dict(data['sexist'].value_counts())
+                else:
+                    sexism = 'yes' in dict(data[data['user'] == selected_option]['sexist'].value_counts())
+
+                if sexism:
+                    st.subheader("Sexism detected in the conversation.")
+                    st.image(f"imgs/sexism_yes.png", width=200)
+                else:
+                    st.subheader("No sexism detected in the conversation.")
+                    st.image(f"imgs/sexism_no.jpeg", width=200)
+
+            with spam:
+                #spam
+                spam = False
+                if selected_option == 'Entire Team':
+                    spam = 'yes' in dict(data['spam'].value_counts())
+                else:
+                    spam = 'yes' in dict(data[data['user'] == selected_option]['spam'].value_counts())
+
+                if spam:
+                    st.subheader("Spam detected in the conversation.")
+                    st.image(f"imgs/spam_yes.jpeg", width=200)
+                else:
+                    st.subheader("No spam detected in the conversation.")
+                    st.image(f"imgs/spam_no.png", width=200)
+
+            with prof:
+                #profanity
+                profanity = False
+                if selected_option == 'Entire Team':
+                    profanity = 'yes' in dict(data['profanity'].value_counts())
+                else:
+                    profanity = 'yes' in dict(data[data['user'] == selected_option]['profanity'].value_counts())
+
+                if profanity:
+                    st.subheader("Profanity detected in the conversation.")
+                    st.image(f"imgs/profanity_yes.jpeg", width=200)
+                else:
+                    st.subheader("No profanity detected in the conversation.")
+                    st.image(f"imgs/profanity_no.jpeg", width=200)
+
+            st.write("")
+            st.write("")
+            st.write("")
+
+            _, har, __ = st.columns([4,2,4])
+
+            with _:
+                st.write("")
+
+            with har:
+            
+                # Harassment Indicator
+                if selected_option == 'Entire Team':
+                    harassment = list(dict(data['harassment indicators'].value_counts()).keys())
+                else:
+                    harassment = list(dict(data[data['user'] == selected_option]['harassment indicators'].value_counts()).keys())
+                
+                filtered_values = [value for value in harassment if value != 'none']
+
+                if len(filtered_values) > 0:
+                    st.subheader(f"Harassment indicators detected in the conversation: {', '.join(filtered_values)}",)
+                    st.image(f"imgs/harass_yes.jpeg", width=200)
+                else:
+                    st.subheader("No harassment indicators detected in the conversation.")
+                    st.image(f"imgs/harass_no.jpeg", width=200)
+
+            with __:
+                st.write("")
+
+
+hide_st_style = """
+            <style>
+            #MainMenu {visibility: hidden;}
+            footer {visibility: hidden;}
+            </style>
+            """
+st.markdown(hide_st_style, unsafe_allow_html=True)

requirements.txt

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+streamlit==1.35.0
+mistralai==0.1.8
+python-dotenv==1.0.1
+pandas==2.1.1
+plotly==5.18.1