Update app.py

app.py

@@ -1,10 +1,8 @@
-# Smart Customer Support Assistant (Enhanced UI Version)
-# Note: Enhanced UI with role avatars, structured suggestions, and end chat functionality
-
 import streamlit as st
 from transformers import pipeline
 import re
 
+# Load models
 emotion_classifier = pipeline(
     "text-classification",
     model="j-hartmann/emotion-english-distilroberta-base",
@@ -13,6 +11,7 @@ emotion_classifier = pipeline(
 intent_classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
 text_generator = pipeline("text2text-generation", model="declare-lab/flan-alpaca-base")
 
+# Predefined customer intents
 candidate_tasks = [
     "change mobile plan",
     "top up balance",
@@ -24,18 +23,7 @@ candidate_tasks = [
     "upgrade device"
 ]
 
-def generate_response(intent, human=True):
-    if human:
-        prompt = (
-            f"You are a telecom customer service agent. For the customer intent '{intent}', provide a helpful reply using this 3-part format: "
-            "[Greeting: short polite opener.] [Middle: Mention customer is currently using Plan X at ¥X/month (fictional), recommend Plan Y with XXGB at ¥Y/month (fictional).] [Ending: Ask if they want to proceed.]"
-        )
-    else:
-        prompt = (
-            f"You are a helpful telecom AI assistant. Answer the customer intent '{intent}' in a short, friendly, single sentence. Offer relevant support or recommendation directly, using fictional placeholders like Plan X, ¥X, 10GB etc."
-        )
-    return text_generator(prompt, max_new_tokens=80, do_sample=False)[0]['generated_text']
-
+# Emotion refinement
 urgent_emotions = {"anger", "frustration", "anxiety", "urgency", "afraid", "annoyed"}
 moderate_emotions = {"confused", "sad", "tired", "concerned", "sadness"}
 
@@ -65,15 +53,35 @@ def get_emotion_score(emotion):
     else:
         return 0.2
 
+def generate_response(intent, human=True):
+    if human:
+        prompt = (
+            f"You are a telecom customer service agent. For the customer intent '{intent}', generate a 3-sentence response:\n"
+            "[Greeting: polite opener.]\n"
+            "[Middle: State that current plan is Plan X, ¥X/month, and recommend Plan Y with XXGB at ¥Y/month. Use fictional values.]\n"
+            "[End: Ask if they'd like to proceed. One sentence only.]"
+        )
+    else:
+        prompt = (
+            f"You are a helpful telecom assistant. Briefly resolve the intent '{intent}' in one sentence. "
+            "Use placeholders like Plan X, ¥X/month, and keep the tone friendly."
+        )
+    result = text_generator(prompt, max_new_tokens=80, do_sample=False)
+    return result[0]['generated_text'].strip()
+
+# Streamlit config
 st.set_page_config(page_title="Smart Customer Support Assistant", layout="wide")
 st.sidebar.title("📁 Customer Selector")
+
+# Session state setup
 if "customers" not in st.session_state:
     st.session_state.customers = {"Customer A": [], "Customer B": [], "Customer C": []}
+if "chat_sessions" not in st.session_state:
+    st.session_state.chat_sessions = {}
+
 customer_names = list(st.session_state.customers.keys())
 selected_customer = st.sidebar.selectbox("Choose a customer:", customer_names)
 
-if "chat_sessions" not in st.session_state:
-    st.session_state.chat_sessions = {}
 if selected_customer not in st.session_state.chat_sessions:
     st.session_state.chat_sessions[selected_customer] = {
         "chat": [],
@@ -82,27 +90,28 @@ if selected_customer not in st.session_state.chat_sessions:
         "support_required": "",
         "user_input": ""
     }
+
 session = st.session_state.chat_sessions[selected_customer]
 
+# Title
 st.title("Smart Customer Support Assistant (for Agents Only)")
 
+# Conversation display
 st.markdown("### Conversation")
 for msg in session["chat"]:
     avatar = "👤" if msg['role'] == 'user' else ("🤖" if msg.get("auto") else "👨‍💼")
     with st.chat_message(msg['role'], avatar=avatar):
         st.markdown(msg['content'])
 
-col1, col2 = st.columns([6,1])
+# Customer input and Analyze
+col1, col2 = st.columns([6, 1])
 with col1:
-    session["user_input"] = st.text_input("Enter customer message:", value=session["user_input"])
+    user_input = st.text_input("Enter customer message:", key="customer_input")
 with col2:
     analyze_clicked = st.button("Analyze", use_container_width=True)
 
-if analyze_clicked and session["user_input"].strip():
-    user_input = session["user_input"]
+if analyze_clicked and user_input.strip():
     session["chat"].append({"role": "user", "content": user_input})
-    session["user_input"] = ""
-
     emotion_result = emotion_classifier(user_input)
     emotion_label = get_emotion_label(emotion_result, user_input)
     emotion_score = get_emotion_score(emotion_label)
@@ -133,18 +142,24 @@ if analyze_clicked and session["user_input"].strip():
         session["support_required"] = "🔴 Human support required."
         session["agent_reply"] = ""
 
+    st.rerun()
+
+# Support judgment display
 if session["support_required"]:
     st.markdown(f"### {session['support_required']}")
 
+# Agent input
 st.subheader("Agent Response Console")
-session["agent_reply"] = st.text_area("Compose your reply:", value=session["agent_reply"])
+session["agent_reply"] = st.text_area("Compose your reply:", value=session["agent_reply"], key="agent_reply_box")
 if st.button("Send Reply"):
     if session["agent_reply"].strip():
         session["chat"].append({"role": "assistant", "content": session["agent_reply"], "auto": False})
         session["agent_reply"] = ""
         session["system_result"] = None
         session["support_required"] = ""
+        st.experimental_rerun()
 
+# If human needed: show analysis & suggestions
 if session["system_result"] is not None:
     st.markdown("#### Customer Status")
     st.markdown(f"- **Emotion:** {session['system_result']['emotion'].capitalize()}")
@@ -156,6 +171,7 @@ if session["system_result"] is not None:
         st.markdown(f"**• {intent.capitalize()}**")
         st.code(suggestion)
 
+# End conversation button
 if st.button("End Conversation"):
     session["chat"] = []
     session["system_result"] = None
@@ -163,3 +179,4 @@ if st.button("End Conversation"):
     session["support_required"] = ""
     session["user_input"] = ""
     st.success("Conversation ended and cleared.")
+    st.rerun()