Spaces:
Running
Running
File size: 4,325 Bytes
c479345 |
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 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 |
import streamlit as st
import json
from transformers import pipeline
# Load pre-trained models
sentiment_analyzer = pipeline('sentiment-analysis', model='nlptown/bert-base-multilingual-uncased-sentiment')
emotion_analyzer = pipeline('text-classification', model='j-hartmann/emotion-english-distilroberta-base')
intent_analyzer = pipeline('text-classification', model='Falconsai/intent_classification')
# Function to determine response length based on text length
def determine_response_length(text):
length = len(text.split())
if length < 5:
return "short"
elif length < 15:
return "medium"
else:
return "long"
# Analyze conversation function
def analyze_conversation(conversation):
output = {"conversation": []}
for entry in conversation:
speaker = entry['speaker']
text = entry['text']
# Sentiment analysis
sentiment_result = sentiment_analyzer(text)
sentiment = sentiment_result[0]['label']
# Emotion detection
emotion_result = emotion_analyzer(text)
emotion = emotion_result[0]['label']
# Intent recognition
intent_result = intent_analyzer(text)
intent = intent_result[0]['label']
# Assign other attributes
tone = "formal" if speaker == "AGENT" else "informal"
confidence_level = "high" if speaker == "AGENT" else "low"
frustration_level = "low" if sentiment == "positive" else "medium"
response_length = determine_response_length(text)
action_required = "no" if speaker == "AGENT" else "yes"
interruption = "no"
cooperation_level = "high" if sentiment == "positive" else "medium"
clarity = "clear" if speaker == "AGENT" else "confusing"
objective = "inform_customer" if speaker == "AGENT" else "get_clarification"
timeline = "present"
motivation = "inform_update" if speaker == "AGENT" else "verify_information"
conversation_stage = "development"
resolution = "pending"
context = "sales"
urgency = "low" if speaker == "AGENT" else "medium"
problem_type = "contract_update"
key_words = ["contract", "electricity"] # Consider dynamic extraction here
expected_detail = "basic" if speaker == "AGENT" else "detailed"
time_gap = "normal"
client_expectation = "get_information"
channel = "telephone"
power_relationship = "balanced"
# Add the entry to the conversation
output["conversation"].append({
"speaker": speaker,
"text": text,
"sentiment": sentiment,
"emotion": emotion,
"intent": intent,
"tone": tone,
"confidence_level": confidence_level,
"frustration_level": frustration_level,
"response_length": response_length,
"action_required": action_required,
"interruption": interruption,
"cooperation_level": cooperation_level,
"clarity": clarity,
"objective": objective,
"timeline": timeline,
"motivation": motivation,
"conversation_stage": conversation_stage,
"resolution": resolution,
"context": context,
"urgency": urgency,
"problem_type": problem_type,
"key_words": key_words,
"expected_detail": expected_detail,
"time_gap": time_gap,
"client_expectation": client_expectation,
"channel": channel,
"power_relationship": power_relationship
})
return output
# Streamlit app layout
st.title("Conversation Labeling App")
st.write("Upload a JSON file containing conversations in the specified format.")
# File uploader
uploaded_file = st.file_uploader("Choose a JSON file", type="json")
if uploaded_file is not None:
# Read the uploaded file
data = json.load(uploaded_file)
if "conversation" in data:
# Analyze the conversation and generate the output
result = analyze_conversation(data["conversation"])
# Display the output
st.subheader("Labeled Conversation")
st.json(result)
else:
st.error("The uploaded JSON file does not contain a 'conversation' key.")
|