Spaces:
Running
Running
| 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.") | |