File size: 3,367 Bytes
26ead64 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 | import openai, json, re, random
import pandas as pd
from utilities import date_format, prompt_constants
def Completion(slack_message):
response = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=[
{"role": "system", "content": prompt_constants.SLACK_SENTIMENT_SYSTEM_PROMPT},
{"role": "user", "content": slack_message} ])
print("response")
print(response["choices"][0]["message"]["content"])
return response["choices"][0]["message"]["content"]
def sanitize_blob(blob_str):
return re.sub(r"(?<=: )'", '"', re.sub(r"'(?=:)", '"', blob_str))
def FindScore(response):
match = re.search(r"\b(0(\.\d+)?|1(\.0+)?)\b", response)
random_offset = round(random.uniform(0.01, .099), 3)
if match:
value = round(float(match.group(1)), 2)
return value + random_offset
else:
return 0
def CheckType(response):
if isinstance(response, float):
return round(response, 2)
elif isinstance(response, str):
return FindScore(response)
def ProcessMessage(message, summary_messages, slack_messages, id, parent_user=None ):
user = message["user"]
message_text = message["text"]
timestamp = message["timestamp"]
response = Completion(message_text) # Assuming Completion is defined elsewhere
summary_messages.append({"role": "user", "content": message_text})
summary_messages.append({"role": "assistant", "content": response})
sentiment_score = CheckType(response) # Assuming CheckType is defined elsewhere
sentiment = "Neutral"
if sentiment_score == 0:
sentiment = "Undefined"
elif 0 < sentiment_score < 0.3:
sentiment = "Negative"
elif sentiment_score > 0.6:
sentiment = "Positive"
dateX, timeX, twentyfour_time = date_format.TimeStampToDateAndTime(timestamp)
message_obj = {
"id": id,
"user": user,
"message": f"{message_text}",
"date": dateX +": " +twentyfour_time,
"time": timestamp,
"twentyfour_time": twentyfour_time,
"sentiment_score": sentiment_score,
"sentiment": sentiment,
"size": 8,
"parent_user": parent_user
}
id=id+1
slack_messages.append(message_obj)
# Process nested replies if any
if "replies" in message:
for reply in message["replies"]:
ProcessMessage(reply, summary_messages, slack_messages, id, parent_user=user)
id=id+1
def ParseBlobs(blob, summary_messages):
global id
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})
for message in response_data["messages"]:
ProcessMessage(message,summary_messages,slack_messages, id)
id=id+1
jsonobj = json.dumps(slack_messages, ensure_ascii=False)
return jsonobj,summary_messages
def AnalyzeSentiment(blob):
global id
summary_messages = []
id=3
slack_blobs,summary_messages=ParseBlobs(blob,summary_messages)
df = pd.DataFrame(summary_messages)
sentimentDF=pd.read_json(slack_blobs)
return df, sentimentDF, id+3 |