slack sentiment component

app.py

@@ -19,8 +19,8 @@ video_analysis=ui_recording_analysis.ui
 slack_sentiment=ui_sentiment_analysis.ui
 usaa_advisor=ui_simple_chat.ui
 
-ui = gr.TabbedInterface([prompt_builder, summarize, image_generator, video_analysis, usaa_advisor], 
-                        (constants.UI_1, constants.UI_2, constants.UI_3,constants.UI_4, constants.UI_6), 
+ui = gr.TabbedInterface([prompt_builder, summarize, image_generator, video_analysis,slack_sentiment, usaa_advisor], 
+                        (constants.UI_1, constants.UI_2, constants.UI_3,constants.UI_4, constants.UI_5,constants.UI_6), 
                         theme=SoftBlue())
 
 ui.launch()    

slack_sentiment_analysis/sentiment_analysis.py

@@ -1,140 +1,90 @@
-import openai, json
+import openai, json, re
 import pandas as pd
-from utilities import prompt_constants, constants
-from datetime import datetime
+from utilities import prompt_constants
 
-def Completion(summary_messages):
+def Completion(slack_message):
     response = openai.ChatCompletion.create(
         model="gpt-3.5-turbo", 
-        messages=summary_messages)
+        messages=[
+            {"role": "system", "content": prompt_constants.SLACK_SENTIMENT_SYSTEM_PROMPT},
+            {"role": "user", "content": slack_message} ])
     print("response")
-    print(response.choices)
-    return json.dumps(response["choices"][0]["message"]["content"])
+    print(response["choices"][0]["message"]["content"])
+    return response["choices"][0]["message"]["content"]
 
 def sanitize_blob(blob_str):
-    import re
     return re.sub(r"(?<=: )'", '"', re.sub(r"'(?=:)", '"', blob_str))
 
-def ReduceBlob(blob):
+def FindScore(response):
+    match = re.search(r"\b(0(\.\d+)?|1(\.0+)?)\b", response)
+    print("after match, match:"+ str(match))
+    if match:
+        #return float(match.group(1))
+        return round(float(match.group(1)), 2)
+    else:
+        return 0
+    
+def CheckType(response):
+    if isinstance(response, float):
+        return round(response, 2)
+    elif isinstance(response, str):
+        return FindScore(response)
+
+def ReduceBlob(blob, summary_messages):
     sanitized_blob = sanitize_blob(blob)
     try:
         response_data = json.loads(sanitized_blob)
     except json.JSONDecodeError:
         print("Invalid JSON format.")
         return None
+    
+    slack_messages = []
+    summary_messages.append({"role": "system", "content": prompt_constants.SLACK_SENTIMENT_SYSTEM_PROMPT})
 
-    messages = []
     for message in response_data["messages"]:
         user = message["user"]
         message_text = message["text"]
         timestamp = message["timestamp"]
+        response = Completion(message_text)
+        summary_messages.append({"role": "user", "content": message_text})
+        summary_messages.append({"role": "assistant", "content": response})
+
+        sentiment_score=CheckType(response)         
+        print("sentiment_score:  " + str(sentiment_score))
 
-        print(f"user: {user} message: {message_text} timestamp: {timestamp}")
+        sentiment="Neutral"
+        if sentiment_score==0:
+            sentiment="Undefined"      
+        elif sentiment_score>0 and sentiment_score<0.3:
+            sentiment="Negative"
+        elif sentiment_score>0.6:
+            sentiment="Positive"  
 
         message_obj = {
             "user": user,
             "message": f"{message_text}",
-            "timestamp": f"{timestamp}"
-        }
-        messages.append(message_obj)
-
-    print("messages")
-    print(messages)
-    jsonobj=json.dumps(messages, ensure_ascii=False)
-    print("jsonobj")
-    print(jsonobj)
-    return jsonobj
-
-'''def ValidateJSON(json_str):
-    valid_objects = []
-    open_brackets = 0
-    start_index = 0
-    
-    for i, char in enumerate(json_str):
-        if char == '{':
-            if open_brackets == 0:
-                start_index = i
-            open_brackets += 1
-        elif char == '}':
-            open_brackets -= 1
-            if open_brackets == 0:
-                try:
-                    valid_object = json.loads(json_str[start_index:i+1])
-                    valid_objects.append(valid_object)
-                except json.JSONDecodeError:
-                    break
-    print("returning"+ str(valid_objects))
-    return valid_objects'''
-
-def ValidateJSON(data):
-    stack = []
-    well_formed_jsons = []
-    start_index = 0
-    json_list = []
-    
-    for i, c in enumerate(data):
-        if c == '{':
-            stack.append('{')
-        elif c == '}':
-            if stack and stack[-1] == '{':
-                stack.pop()
-                
-        if not stack:
-            if start_index <= i:
-                potential_json_str = data[start_index:i+1]
-                try:
-                    json_object = json.loads(potential_json_str)
-                    well_formed_jsons.append(json_object)
-                    start_index = i + 1
-                except json.JSONDecodeError:
-                    start_index = i + 1
-                    continue
-                    
-    if well_formed_jsons:
-        json_list.append(well_formed_jsons)
-        
-    response = json.dumps(json_list)
-    print("returning"+ str(response))
-    return response
-
-def CreateSentimentDF(sentiments):
-    users = []
-    sentiment_labels = []
-    sentimentsJSON = json.loads(sentiments)
-
-    print("sentimentsJSON: ")
-    print(sentimentsJSON)
-    for sentiment_obj in sentimentsJSON:
-        users.append(sentiment_obj["user"])
-        sentiment_labels.append(sentiment_obj["sentiment"])
-        
-    # Append the user and sentiment as a new row to the DataFrame
-    df = pd.DataFrame({"User": users, "Sentiment": sentiment_labels})
-    return df
+            "timestamp": f"{timestamp}",
+            "sentiment_score": sentiment_score, 
+            "sentiment": sentiment,
+            "size":5
+        }        
+        slack_messages.append(message_obj)
+    jsonobj = json.dumps(slack_messages, ensure_ascii=False)
+    return jsonobj,summary_messages
 
 def AnalyzeSentiment(blob):    
-    slack_blob=ReduceBlob(blob)
+    summary_messages = []
 
-    summary_messages = [] 
-    summary_messages.append({"role": "system", "content": prompt_constants.SLACK_SENTIMENT_SYSTEM_PROMPT})    
-    summary_messages.append({"role": "user", "content": slack_blob})  
+    slack_blobs,summary_messages=ReduceBlob(blob,summary_messages)   
 
-    response_message = Completion(summary_messages)
-    verify_response=ValidateJSON(response_message)
-    summary_messages.append({"role": "assistant", "content": verify_response})   
-
-    print("verify_response")
-    print(verify_response)
-     #3: find topics using OpenAI gpt-3.5-turbo
-    #sentiments = SortSentiment(response_message)
-    df=CreateSentimentDF(verify_response)
+    print("after reduce")
+    print (slack_blobs)
 
+    df = pd.DataFrame(summary_messages)
     print("DataFrame:")
     print(df)
-    
-    print("Sentiment Counts:")
-    for sentiment, counts in df.items():
-        print(sentiment, counts)
-    
-    return summary_messages, verify_response, df
 
+    sentimentDF=pd.read_json(slack_blobs)
+    print("sentimentDF:")
+    print(sentimentDF)
+    return df, sentimentDF

slack_sentiment_analysis/ui_sentiment_analysis.py

@@ -5,15 +5,12 @@ import os
 from utilities import constants
 from slack_sentiment_analysis import sentiment_analysis
 
+sentimentData=None
+
 def Summary(code):
-    sum_messages,sum_text,sDF = sentiment_analysis.AnalyzeSentiment(code)
-    print("SDF")
-    print(sDF)
-    df = pd.DataFrame(sum_messages) 
-    return sum_text,df,{
-        "x": ["apple", "banana", "cherry"],
-        "y": [1, 2, 3]
-    }
+    global sentimentData
+    sum_messages,sentimentData = sentiment_analysis.AnalyzeSentiment(code)   
+    return sentimentData,sum_messages,sentimentData
 
 def InitDF():
     global commDF
@@ -34,24 +31,25 @@ with gr.Blocks() as ui:
         with gr.Column():
             gr.Markdown(constants.DIRECTIONS_MD)
             gr.Markdown(value=constants.SLACK_SENTIMENT_ANALYSIS_DIRECTIONS)
-    with gr.Row():
-        with gr.Column():
-            code=gr.Textbox(label=constants.ORIGINAL_DOC, interactive=True, placeholder=constants.SUMMARY_MD_DIRECTIONS, lines=3)
-        with gr.Column():
-            sentiment=gr.HTML(wrap=True, label=constants.SUMMARY)
+
+    code=gr.Textbox(label=constants.ORIGINAL_DOC, interactive=True, placeholder=constants.SUMMARY_MD_DIRECTIONS, height="500", lines=3)   
+    sentiment=gr.DataFrame(wrap=True, label=constants.SUMMARY, height="500")
     gr.Markdown(constants.CODE_DOC_EXAMPLES_MD)
     with gr.Row():
-        examples = gr.Radio([constants.JSON_1, constants.JSON_2],
+        examples = gr.Radio([constants.JSON_1, constants.JSON_2, constants.JSON_3, constants.JSON_4],value=constants.JSON_4,
                              show_label=False, info=constants.ANALYZE)
     with gr.Row():
         sentimentize=gr.Button(value=constants.ANALYZE_SENTIMENT, variant="primary")
     with gr.Column():
         gr.Markdown(constants.TOPICS_MD)
-        sentimentPlot= gr.BarPlot ()        
+        sentimentPlot = gr.ScatterPlot(value=sentimentData, x="user", y="sentiment",label="Sentiment", 
+                                        color_legend_title="Sentiment", color="sentiment", interactive=True, 
+                                        height = 500, width=500,
+                                        tooltip=["user","sentiment","sentiment_score","timestamp"])
     with gr.Row():
         commDF=gr.DataFrame(type="pandas", value=pd.DataFrame({"role": [""], "content": [""] }),
                                wrap=True, show_label=False, label=constants.OPENAI_LOG)
     sentimentize.click(Summary, inputs=[code], outputs=[sentiment,commDF,sentimentPlot])
     examples.input(UpdateWithExample,inputs=[examples], outputs=[code])
 
-    InitDF()
+InitDF()

utilities/constants.py

@@ -50,6 +50,8 @@ JSON_PREFIX = "utilities/data/json/"
 
 JSON_1="Slack 1"
 JSON_2="Slack 2"
+JSON_3="Slack 3"
+JSON_4="Slack 4"
 
 LANGUAGE_1="English"
 LANGUAGE_2="Spanish"

utilities/data/json/slack_4.json

@@ -0,0 +1,164 @@
+{
+    "messages": [
+        {
+            "user": "Anne Smith",
+            "text": "Hey everyone, it's a sunny morning! Makes me feel a bit better. 🌞",
+            "timestamp": "2023-09-08T08:00:00Z",
+            "replies": [
+                {
+                    "user": "David Jones",
+                    "text": "Some positivity! That's a good change! 🌞",
+                    "timestamp": "2023-09-08T08:10:00Z"
+                }
+            ],
+            "reactions": {
+                "🌞": 4,
+                "😊": 5
+            }
+        },
+        {
+            "user": "Bob Wright",
+            "text": "Anyone else excited for the team building event tomorrow?",
+            "timestamp": "2023-09-08T09:00:00Z",
+            "replies": [
+                {
+                    "user": "Nora West",
+                    "text": "Absolutely! It's gonna be so much fun.",
+                    "timestamp": "2023-09-08T09:10:00Z"
+                }
+            ],
+            "reactions": {
+                "👍": 3,
+                "😃": 5
+            }
+        },
+        {
+            "user": "Cathy Newsome",
+            "text": "The internet connection is driving me nuts! Can we get this fixed? 😠",
+            "timestamp": "2023-09-08T10:00:00Z",
+            "replies": [
+                {
+                    "user": "Olivia Prestley",
+                    "text": "I second that. This is unacceptable! 😤",
+                    "timestamp": "2023-09-08T10:10:00Z"
+                }
+            ],
+            "reactions": {
+                "😠": 6,
+                "😤": 4
+            }
+        },
+        {
+            "user": "David Jones",
+            "text": "My code just won't compile and it's super frustrating! 😡",
+            "timestamp": "2023-09-08T11:00:00Z",
+            "replies": [
+                {
+                    "user": "Anne Smith",
+                    "text": "Been there, David. Hang in there! 😖",
+                    "timestamp": "2023-09-08T11:10:00Z"
+                }
+            ],
+            "reactions": {
+                "😡": 4,
+                "😖": 3
+            }
+        },
+        {
+            "user": "Ed Norton",
+            "text": "Anyone else noticed that the snacks have been restocked? 😁",
+            "timestamp": "2023-09-08T12:00:00Z",
+            "replies": [
+                {
+                    "user": "Felix Mars",
+                    "text": "Finally some good news! 😃",
+                    "timestamp": "2023-09-08T12:10:00Z"
+                }
+            ],
+            "reactions": {
+                "😁": 5,
+                "😃": 3
+            }
+        },
+        {
+            "user": "Felix Mars",
+            "text": "I just finished a major project. I could really use some down time. 😓",
+            "timestamp": "2023-09-08T13:00:00Z",
+            "replies": [
+                {
+                    "user": "Heidi Pierce",
+                    "text": "You've earned it, Felix! Take a break. 😌",
+                    "timestamp": "2023-09-08T13:10:00Z"
+                }
+            ],
+            "reactions": {
+                "😓": 4,
+                "😌": 3
+            }
+        },
+        {
+            "user": "Heidi Pierce",
+            "text": "Who else is up for a movie night this Friday? 🍿",
+            "timestamp": "2023-09-08T14:00:00Z",
+            "replies": [
+                {
+                    "user": "Kristy Klaus",
+                    "text": "Count me in! 🙋‍♀️",
+                    "timestamp": "2023-09-08T14:10:00Z"
+                }
+            ],
+            "reactions": {
+                "🍿": 4,
+                "🙋‍♀️": 5
+            }
+        },
+        {
+            "user": "Ingrid Gonzales",
+            "text": "Another system update? Why do they always come at the worst times? 😡",
+            "timestamp": "2023-09-08T15:00:00Z",
+            "replies": [
+                {
+                    "user": "Laurence Welk",
+                    "text": "So true, Ingrid. It's like they know when we're busiest. 😤",
+                    "timestamp": "2023-09-08T15:10:00Z"
+                }
+            ],
+            "reactions": {
+                "😡": 5,
+                "😤": 4
+            }
+        },
+        {
+            "user": "Jessie Jacobs",
+            "text": "Thank you, team, for pulling off a successful project! 🙌",
+            "timestamp": "2023-09-08T16:00:00Z",
+            "replies": [
+                {
+                    "user": "Nora West",
+                    "text": "Great teamwork! So proud of us! 🎉",
+                    "timestamp": "2023-09-08T16:10:00Z"
+                }
+            ],
+            "reactions": {
+                "🙌": 4,
+                "🎉": 5
+            }
+        },
+        {
+            "user": "Kristy Klaus",
+            "text": "Traffic is terrible today. I might be late. 😑",
+            "timestamp": "2023-09-08T17:00:00Z",
+            "replies": [
+                {
+                    "user": "Pierce Frank",
+                    "text": "No worries, Kristy. We'll cover for you. 👍",
+                    "timestamp": "2023-09-08T17:10:00Z"
+                }
+            ],
+            "reactions": {
+                "😑": 3,
+                "👍": 4
+            }
+        }
+    ]
+}

utilities/prompt_constants.py

@@ -47,5 +47,5 @@ ANALYSIS_SYSTEM_PROMPT="You will be provided a transcript, your task is to summa
 KEYWORD_SYSTEM_PROMPT="You will be provided with a block of text, and your task is to extract a list of up to 15 key concepts from it, filter to keywords that appear more than once or are relevant to the central theme. Response should be just a numbered list, no title or explanation."
 
 #slack analysis presets
-SLACK_SENTIMENT_SYSTEM_PROMPT="You are a sentiment analysis bot. You will be provided with a blob of Slack content as JSON, and your task is to provide sentiment analysis for each message by adding 'Sentiment' field to the message. Example:  {'user': 'Olivia Prestley','text': 'I cant wait to escape this office. The weekend can't come soon enough. 😒','timestamp': '2023-09-07T17:00:00Z', 'reactions': {'😒': 5, '😞': 3, '😔': 2  } }, becomes:  {'user': 'Olivia Prestley','text': 'I cannot wait to escape this office. The weekend cannot come soon enough. 😒','timestamp': '2023-09-07T17:00:00Z', 'reactions': {'😒': 5, '😞': 3, '😔': 2  }, 'sentiment': 'Sad' }"
+SLACK_SENTIMENT_SYSTEM_PROMPT="You are a sentiment analysis bot and your task is to provide sentiment analysis for each message. Your response be a score between 0.00 - 1.00, such that [0, 0.3)(Negative), [0.3, 0.6)(Neutral), and [0.6,1.00](Positive)."
 SLACK_THEMES_SYSTEM_PROMPT="You will be provided with a blob of JSON in this format: {'user': 'Olivia Prestley','text': 'I cannot wait to escape this office. The weekend cannot come soon enough. 😒','timestamp': '2023-09-07T17:00:00Z', 'reactions': {'😒': 5, '😞': 3, '😔': 2  }, 'sentiment': 'Sad' }, and your task is to extract a list of up to 15 keywords from it, filter to keywords that appear more than once or are relevant to the central theme."