Update app.py

app.py

@@ -1,5 +1,5 @@
 # Smart Customer Support Assistant (Enhanced UI Version)
-# Note: Core analysis logic remains unchanged, now with text generation
+# Note: Core analysis logic remains unchanged, now with text generation and customer selection
 
 import streamlit as st
 from transformers import pipeline
@@ -31,8 +31,8 @@ candidate_tasks = [
 ]
 
 def generate_response(intent):
-    prompt = f"Write a polite and helpful customer service response for this request: '{intent}'"
-    output = text_generator(prompt, max_new_tokens=80, do_sample=True)[0]['generated_text']
+    prompt = f"Generate a polite and helpful customer service response for the request '{intent}'. Include a greeting, summary of current status like plan or balance using anonymized placeholders (e.g. Plan X, ¥X), a suitable recommendation, and end with a question offering assistance."
+    output = text_generator(prompt, max_new_tokens=100, do_sample=True)[0]['generated_text']
     return output
 
 urgent_emotions = {"anger", "frustration", "anxiety", "urgency", "afraid", "annoyed"}
@@ -68,28 +68,37 @@ def get_emotion_score(emotion):
         return 0.2
 
 # ------------------------------
-# Streamlit UI Logic
+# UI: Sidebar for customer selection
+# ------------------------------
+st.set_page_config(page_title="Smart Customer Support Assistant", layout="wide")
+st.sidebar.title("📁 Customer Selector")
+if "customers" not in st.session_state:
+    st.session_state.customers = {"Customer A": [], "Customer B": [], "Customer C": []}
+customer_names = list(st.session_state.customers.keys())
+selected_customer = st.sidebar.selectbox("Choose a customer:", customer_names)
+
+# Load or init selected customer's session
+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": [],
+        "system_result": None,
+        "agent_reply": "",
+        "support_required": ""
+    }
+session = st.session_state.chat_sessions[selected_customer]
+
+# ------------------------------
+# Main Interface
 # ------------------------------
-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 = ""
-if 'support_required' not in st.session_state:
-    st.session_state.support_required = ""
-
-# Always show conversation
 st.markdown("### Conversation")
-for msg in st.session_state.chat:
+for msg in session["chat"]:
     with st.chat_message(msg['role']):
         st.markdown(msg['content'])
 
-# Input row with button aligned right
 col1, col2 = st.columns([6,1])
 with col1:
     user_input = st.text_input("Enter customer message:", key="user_input")
@@ -97,7 +106,6 @@ with col2:
     analyze_clicked = st.button("Analyze")
 
 if analyze_clicked and 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)
@@ -112,46 +120,42 @@ if analyze_clicked and user_input.strip():
         content_score += 0.4
 
     final_score = 0.5 * emotion_score + 0.5 * content_score
-
-    st.session_state.chat.append({"role": "user", "content": user_input})
+    session["chat"].append({"role": "user", "content": user_input})
 
     if final_score < 0.5 and top_intents:
         intent = top_intents[0]
         response = generate_response(intent)
-        st.session_state.chat.append({"role": "assistant", "content": response})
-        st.session_state.system_result = None
-        st.session_state.support_required = "🟢 Automated response handled this request."
+        session["chat"].append({"role": "assistant", "content": response})
+        session["system_result"] = None
+        session["support_required"] = "🟢 Automated response handled this request."
     else:
-        st.session_state.system_result = {
+        session["system_result"] = {
             "emotion": emotion_label,
             "tone": "Urgent" if emotion_score > 0.8 else "Concerned" if emotion_score > 0.5 else "Calm",
             "intents": top_intents
         }
-        st.session_state.support_required = "🔴 Human support required."
+        session["support_required"] = "🔴 Human support required."
 
-# Show support need status
-if st.session_state.support_required:
-    st.markdown(f"### {st.session_state.support_required}")
+if session["support_required"]:
+    st.markdown(f"### {session['support_required']}")
 
-# Always show agent input box
 st.subheader("Agent Response Console")
-st.session_state.agent_reply = st.text_area("Compose your reply:", value=st.session_state.agent_reply)
+session["agent_reply"] = st.text_area("Compose your reply:", value=session["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
-        st.session_state.support_required = ""
-
-# If human support needed, show status and suggestions
-if st.session_state.system_result is not None:
+    if session["agent_reply"].strip():
+        session["chat"].append({"role": "assistant", "content": session["agent_reply"]})
+        session["agent_reply"] = ""
+        session["system_result"] = None
+        session["support_required"] = ""
+
+if session["system_result"] is not None:
     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']}")
+    st.markdown(f"- **Emotion:** {session['system_result']['emotion'].capitalize()}")
+    st.markdown(f"- **Tone:** {session['system_result']['tone']}")
 
     st.markdown("#### Detected Customer Needs")
-    for intent in st.session_state.system_result['intents']:
+    for intent in session['system_result']['intents']:
         suggestion = generate_response(intent)
         st.markdown(f"**• {intent.capitalize()}**")
-        if st.button(suggestion, key=f"btn_{intent}"):
-            st.session_state.agent_reply = suggestion
+        if st.button(f"Use suggestion: {suggestion}", key=f"btn_{selected_customer}_{intent}"):
+            session["agent_reply"] = suggestion