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| # Smart Customer Support Assistant (Enhanced UI Version) | |
| # Note: Core analysis logic remains unchanged | |
| import streamlit as st | |
| from transformers import pipeline | |
| import re | |
| # ------------------------------ | |
| # Load models (same as before) | |
| # ------------------------------ | |
| emotion_classifier = pipeline( | |
| "text-classification", | |
| model="j-hartmann/emotion-english-distilroberta-base", | |
| return_all_scores=True | |
| ) | |
| intent_classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli") | |
| # ------------------------------ | |
| # Candidate tasks / templates | |
| # ------------------------------ | |
| candidate_tasks = [ | |
| "change mobile plan", | |
| "top up balance", | |
| "report service outage", | |
| "ask for billing support", | |
| "reactivate service", | |
| "cancel subscription", | |
| "check account status", | |
| "upgrade device" | |
| ] | |
| intent_solutions = { | |
| "top up balance": "Your balance is ¥12. Promo: recharge ¥100 get ¥5 bonus.", | |
| "reactivate service": "Service suspended due to unpaid ¥38. Recharge to restore in 30 mins.", | |
| "change mobile plan": "You're on Basic (¥68/5GB). Suggest Plus (¥98/20GB).", | |
| "check account status": "Data: 3.2GB/5GB. Balance: ¥12. Calls left: 22 mins.", | |
| "ask for billing support": "Last bill: ¥96 (Mar). Includes ¥16 overage.", | |
| "cancel subscription": "Contract ends: 2025-06-30. No penalty after this date.", | |
| "upgrade device": "Eligible for upgrade. New iPhone plan: ¥399/month.", | |
| "report service outage": "Signal issues detected (ZIP: XXX). Engineers notified." | |
| } | |
| intent_closings = { | |
| "top up balance": "Proceed with recharge now?", | |
| "reactivate service": "Shall I help restart your service?", | |
| "report service outage": "Shall I file a service report?", | |
| "change mobile plan": "Switch to a better plan?", | |
| "ask for billing support": "Show recent billing records?", | |
| "cancel subscription": "Guide you through cancellation?", | |
| "check account status": "Show usage and balance?", | |
| "upgrade device": "See available upgrades?" | |
| } | |
| urgent_emotions = {"anger", "frustration", "anxiety", "urgency", "afraid", "annoyed"} | |
| moderate_emotions = {"confused", "sad", "tired", "concerned", "sadness"} | |
| # ------------------------------ | |
| # Emotion processing | |
| # ------------------------------ | |
| def refine_emotion_label(text, model_emotion): | |
| text_lower = text.lower() | |
| urgent_keywords = ["fix", "now", "immediately", "urgent", "can't", "need", "asap"] | |
| exclamations = text.count("!") | |
| upper_words = sum(1 for word in text.split() if word.isupper()) | |
| signal_score = sum([ | |
| any(word in text_lower for word in urgent_keywords), | |
| exclamations >= 2, | |
| upper_words >= 1 | |
| ]) | |
| if model_emotion.lower() in {"joy", "neutral", "sadness"} and signal_score >= 2: | |
| return "urgency" | |
| return model_emotion | |
| def get_emotion_label(emotion_result, text): | |
| sorted_emotions = sorted(emotion_result[0], key=lambda x: x['score'], reverse=True) | |
| return refine_emotion_label(text, sorted_emotions[0]['label']) | |
| def get_emotion_score(emotion): | |
| if emotion.lower() in urgent_emotions: | |
| return 1.0 | |
| elif emotion.lower() in moderate_emotions: | |
| return 0.6 | |
| else: | |
| return 0.2 | |
| # ------------------------------ | |
| # Streamlit UI Logic | |
| # ------------------------------ | |
| st.set_page_config(page_title="Smart Customer Support Assistant", layout="centered") | |
| st.title("Smart Customer Support Assistant (for Agents Only)") | |
| # Session state to store chat | |
| if 'chat' not in st.session_state: | |
| st.session_state.chat = [] | |
| if 'system_result' not in st.session_state: | |
| st.session_state.system_result = None | |
| if 'agent_reply' not in st.session_state: | |
| st.session_state.agent_reply = "" | |
| # Display chat history | |
| for msg in st.session_state.chat: | |
| with st.chat_message(msg['role']): | |
| st.markdown(msg['content']) | |
| # Customer input + Analyze button | |
| col1, col2 = st.columns([5,1]) | |
| with col1: | |
| user_input = st.text_input("Enter customer message:", key="user_input") | |
| with col2: | |
| if st.button("Analyze"): | |
| if user_input.strip(): | |
| # Run analysis pipeline | |
| emotion_result = emotion_classifier(user_input) | |
| emotion_label = get_emotion_label(emotion_result, user_input) | |
| emotion_score = get_emotion_score(emotion_label) | |
| intent_result = intent_classifier(user_input, candidate_tasks) | |
| top_intents = [label for label, score in zip(intent_result['labels'], intent_result['scores']) if score > 0.15][:3] | |
| content_score = 0.0 | |
| if any(x in user_input.lower() for x in ["out of service", "can't", "urgent", "immediately"]): | |
| content_score += 0.4 | |
| if any(label in ["top up balance", "reactivate service"] for label in top_intents): | |
| content_score += 0.4 | |
| final_score = 0.5 * emotion_score + 0.5 * content_score | |
| # Store user message | |
| st.session_state.chat.append({"role": "user", "content": user_input}) | |
| # Auto response or escalate to agent | |
| if final_score < 0.5 and top_intents: | |
| intent = top_intents[0] | |
| response = f"Thank you for contacting us. I understand your concern. {intent_solutions[intent]} {intent_closings[intent]}" | |
| st.session_state.chat.append({"role": "assistant", "content": response}) | |
| else: | |
| st.session_state.system_result = { | |
| "emotion": emotion_label, | |
| "tone": "Urgent" if emotion_score > 0.8 else "Concerned" if emotion_score > 0.5 else "Calm", | |
| "intents": top_intents | |
| } | |
| # If human support needed | |
| if st.session_state.system_result: | |
| st.markdown("---") | |
| st.subheader("Agent Response Panel") | |
| # Agent editable response | |
| st.session_state.agent_reply = st.text_area("Compose your reply:", value=st.session_state.agent_reply) | |
| if st.button("Send Reply"): | |
| if st.session_state.agent_reply.strip(): | |
| st.session_state.chat.append({"role": "assistant", "content": st.session_state.agent_reply}) | |
| st.session_state.agent_reply = "" | |
| st.session_state.system_result = None | |
| # Context info | |
| st.markdown("#### Customer Status") | |
| st.markdown(f"- **Emotion:** {st.session_state.system_result['emotion'].capitalize()}") | |
| st.markdown(f"- **Tone:** {st.session_state.system_result['tone']}") | |
| # Suggested replies | |
| st.markdown("#### Detected Customer Needs") | |
| for intent in st.session_state.system_result['intents']: | |
| st.markdown(f"**• {intent.capitalize()}**") | |
| suggestion = f"Thank you for contacting us. I understand your concern. {intent_solutions[intent]} {intent_closings[intent]}" | |
| if st.button(f"Use suggestion for '{intent}'", key=intent): | |
| st.session_state.agent_reply = suggestion | |