Update app.py

app.py

@@ -1,8 +1,13 @@
+# Smart Customer Support Assistant (Enhanced UI Version)
+# Note: Core analysis logic remains unchanged
+
 import streamlit as st
 from transformers import pipeline
 import re
 
-# Load upgraded models
+# ------------------------------
+# Load models (same as before)
+# ------------------------------
 emotion_classifier = pipeline(
     "text-classification",
     model="j-hartmann/emotion-english-distilroberta-base",
@@ -10,7 +15,9 @@ emotion_classifier = pipeline(
 )
 intent_classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
 
-# Candidate intents
+# ------------------------------
+# Candidate tasks / templates
+# ------------------------------
 candidate_tasks = [
     "change mobile plan",
     "top up balance",
@@ -22,34 +29,52 @@ candidate_tasks = [
     "upgrade device"
 ]
 
-# Emotion scoring
+intent_solutions = {
+    "top up balance": "Your balance is \u00a512. Promo: recharge \u00a5100 get \u00a55 bonus.",
+    "reactivate service": "Service suspended due to unpaid \u00a538. Recharge to restore in 30 mins.",
+    "change mobile plan": "You're on Basic (\u00a568/5GB). Suggest Plus (\u00a598/20GB).",
+    "check account status": "Data: 3.2GB/5GB. Balance: \u00a512. Calls left: 22 mins.",
+    "ask for billing support": "Last bill: \u00a596 (Mar). Includes \u00a516 overage.",
+    "cancel subscription": "Contract ends: 2025-06-30. No penalty after this date.",
+    "upgrade device": "Eligible for upgrade. New iPhone plan: \u00a5399/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"}
 
-# ⬇️ 情绪判断（融合模型输出 + 语言信号）
-def get_emotion_label(emotion_result, text):
-    sorted_emotions = sorted(emotion_result[0], key=lambda x: x['score'], reverse=True)
-    top_emotion = sorted_emotions[0]['label']
-    return refine_emotion_label(text, top_emotion)
-
+# ------------------------------
+# 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 = 0
-    if any(word in text_lower for word in urgent_keywords):
-        signal_score += 1
-    if exclamations >= 2:
-        signal_score += 1
-    if upper_words >= 1:
-        signal_score += 1
-
+    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
@@ -58,95 +83,85 @@ def get_emotion_score(emotion):
     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):
-    intent_solutions = {
-        "top up balance": "Your balance is ¥12. Current promo: recharge ¥100 get ¥5 bonus.",
-        "reactivate service": "Your service is suspended due to unpaid ¥38. Recharge to restore within 30 mins.",
-        "change mobile plan": "You're on Plan Basic (¥68/5GB). Suggest Plan Plus (¥98/20GB).",
-        "check account status": "Your data usage is 3.2GB/5GB. Balance: ¥12. Calls left: 22 mins.",
-        "ask for billing support": "Last bill was ¥96 for March. Includes data overage ¥16.",
-        "cancel subscription": "Your contract ends on 2025-06-30. No penalty after this date.",
-        "upgrade device": "You're eligible for upgrade. New iPhone plan starts at ¥399/month.",
-        "report service outage": "We're detecting signal issues in your area (ZIP: XXX). Engineers notified."
-    }
-    intent_closings = {
-        "top up balance": "Would you like to proceed with a recharge now?",
-        "reactivate service": "Shall I help you restart your service now?",
-        "report service outage": "Would you like me to file a service report for you?",
-        "change mobile plan": "Would you like to switch to a better plan?",
-        "ask for billing support": "Shall I show your last 3 billing records?",
-        "cancel subscription": "Shall I guide you through cancellation?",
-        "check account status": "Want a summary of your usage and balance?",
-        "upgrade device": "Would you like me to show available upgrade options?"
-    }
-
-    solution = intent_solutions.get(intent.lower(), "Here's how we can assist you with this issue.")
-    closing = intent_closings.get(intent.lower(), "Is there anything else I can help with?")
-    opening = "Thank you for contacting us. I understand your concern."
-    return f"{opening} {solution} {closing}"
-
-# 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 (model + rule)
+# ------------------------------
+# Streamlit UI Logic
+# ------------------------------
+st.set_page_config(page_title="Smart Customer Support Assistant", layout="centered")
+st.title("\ud83d\udcde 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 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 > 0.15][:3]
 
-            # Content score
-            content_score = get_content_score(user_input, top_intents)
+            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 decision score
-            final_score = (0.5 * emotion_score) + (0.5 * content_score)
+            final_score = 0.5 * emotion_score + 0.5 * content_score
 
-            st.subheader("🧾 System Summary")
+            # Store user message
+            st.session_state.chat.append({"role": "user", "content": user_input})
 
-            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.")
+            # 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.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(reply)
-                else:
-                    st.warning("No clear intent detected. Manual review recommended.")
+                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("\ud83d\udc69\u200d\ud83d\udcbb 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("#### \ud83d\udc64 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("#### \ud83e\uddf0 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