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| import streamlit as st | |
| from transformers import pipeline | |
| import re | |
| # Load models | |
| emotion_classifier = pipeline("text-classification", model="bhadresh-savani/distilbert-base-uncased-emotion", top_k=1) | |
| intent_classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli") | |
| text_generator = pipeline("text2text-generation", model="google/flan-t5-base", max_new_tokens=100) | |
| # Candidate intents | |
| candidate_tasks = [ | |
| "change mobile plan", | |
| "top up balance", | |
| "report service outage", | |
| "ask for billing support", | |
| "reactivate service", | |
| "cancel subscription", | |
| "check account status", | |
| "upgrade device" | |
| ] | |
| # Emotion scoring | |
| urgent_emotions = {"anger", "annoyance", "disgust", "frustration", "sadness"} | |
| moderate_emotions = {"confusion", "concern", "nervousness", "fear"} | |
| def get_emotion_score(emotion): | |
| if emotion in urgent_emotions: | |
| return 1.0 | |
| elif emotion in moderate_emotions: | |
| return 0.6 | |
| else: | |
| return 0.2 | |
| def get_content_score(text, top_intents): | |
| score = 0.0 | |
| trigger_words = ["out of service", "urgent", "not working", "stopped", "can't", "immediately"] | |
| if any(kw in text.lower() for kw in trigger_words): | |
| score += 0.3 | |
| if any(label in ["top up balance", "reactivate service", "report service outage"] for label in top_intents): | |
| score += 0.4 | |
| if any(label in ["change mobile plan", "ask for billing support"] for label in top_intents): | |
| score += 0.2 | |
| return min(score + 0.1, 1.0) | |
| def generate_reply(input_text, intent): | |
| prompt = f"Generate a polite and helpful customer service reply to this intent: '{intent}'. Customer said: '{input_text}'" | |
| return text_generator(prompt)[0]['generated_text'] | |
| # UI | |
| st.set_page_config(page_title="Customer Support Assistant", layout="centered") | |
| st.title("📞 Smart Customer Support Assistant (for Agents Only)") | |
| user_input = st.text_area("Enter customer's message or complaint:", height=150) | |
| if st.button("Analyze Message"): | |
| if user_input.strip() == "": | |
| st.warning("Please enter a customer message.") | |
| else: | |
| with st.spinner("Processing..."): | |
| # Emotion detection | |
| emotion_result = emotion_classifier(user_input) | |
| emotion_label = emotion_result[0]['label'] | |
| emotion_score = get_emotion_score(emotion_label) | |
| # Intent detection | |
| intent_result = intent_classifier(user_input, candidate_tasks) | |
| top_intents = [label for label, score in zip(intent_result['labels'], intent_result['scores']) if score > 0.2] | |
| # Content score | |
| content_score = get_content_score(user_input, top_intents) | |
| # Final decision score | |
| final_score = (0.5 * emotion_score) + (0.5 * content_score) | |
| st.subheader("🧾 System Summary") | |
| if final_score < 0.5: | |
| st.markdown("### 🟢 This message was handled automatically.") | |
| if top_intents: | |
| auto_intent = top_intents[0] | |
| auto_reply = generate_reply(user_input, auto_intent) | |
| st.markdown("#### 🤖 Auto-Response Sent to User:") | |
| st.success(auto_reply) | |
| else: | |
| st.info("No clear intent detected. A general auto-reply was used.") | |
| else: | |
| st.markdown("### 🔴 Human Support Required") | |
| # Customer Profile Summary | |
| st.markdown("#### 👤 Customer Status:") | |
| st.write(f"- **Emotion detected**: {emotion_label.capitalize()}") | |
| st.write(f"- **Tone**: {'Urgent' if emotion_score > 0.8 else 'Concerned' if emotion_score > 0.5 else 'Calm'}") | |
| if top_intents: | |
| st.markdown("#### 🧩 Detected Customer Needs:") | |
| for intent in top_intents: | |
| reply = generate_reply(user_input, intent) | |
| st.markdown(f"**• {intent.capitalize()}**") | |
| st.write(f"Recommended Action: {reply}") | |
| else: | |
| st.warning("No clear intent detected. Manual review recommended.") | |