Update app.py

app.py

@@ -1,7 +1,7 @@
 import streamlit as st
 from transformers import pipeline
 
-# Load models
+# Load Models
 emotion_classifier = pipeline(
     "text-classification",
     model="j-hartmann/emotion-english-distilroberta-base",
@@ -10,25 +10,23 @@ 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")
 
+# Tasks
 candidate_tasks = [
-    "change mobile plan",
-    "top up balance",
-    "report service outage",
-    "ask for billing support",
-    "reactivate service",
-    "cancel subscription",
-    "check account status",
-    "upgrade device"
+    "change mobile plan", "top up balance", "report service outage",
+    "ask for billing support", "reactivate service", "cancel subscription",
+    "check account status", "upgrade device"
 ]
 
+# Emotion Tiers
 urgent_emotions = {"anger", "frustration", "anxiety", "urgency", "afraid", "annoyed"}
 moderate_emotions = {"confused", "sad", "tired", "concerned", "sadness"}
 
+# Utilities
 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())
+    upper_words = sum(1 for w in text.split() if w.isupper())
     signal_score = sum([
         any(word in text_lower for word in urgent_keywords),
         exclamations >= 2,
@@ -38,8 +36,8 @@ def refine_emotion_label(text, model_emotion):
         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)
+def get_emotion_label(result, text):
+    sorted_emotions = sorted(result[0], key=lambda x: x['score'], reverse=True)
     return refine_emotion_label(text, sorted_emotions[0]['label'])
 
 def get_emotion_score(emotion):
@@ -52,17 +50,15 @@ def get_emotion_score(emotion):
 
 def generate_response(intent, human=True):
     prompt = (
-        f"You are a telecom customer service assistant. For the customer intent '{intent}', generate a 3-part response:
-"
-        "[Greeting: polite welcome.]
-"
-        "[Middle: mention the customer is currently on Plan X (¥X/month), and suggest switching to Plan Y with XXGB at ¥Y/month. Use fictional placeholder values.]
-"
+        f"You are a telecom customer service assistant. For the customer intent '{intent}', generate a 3-part response:\n"
+        "[Greeting: polite welcome.]\n"
+        "[Middle: mention the customer is currently on Plan X (¥X/month), and suggest switching to Plan Y with XXGB at ¥Y/month. Use fictional placeholder values.]\n"
         "[End: ask if they'd like to proceed with the new plan or need more details.]"
     )
     result = text_generator(prompt, max_new_tokens=100, do_sample=False)
     return result[0]['generated_text'].strip()
 
+# App UI Setup
 st.set_page_config(page_title="Smart Customer Support Assistant", layout="wide")
 st.sidebar.title("📁 Customer Selector")
 
@@ -71,71 +67,68 @@ if "customers" not in st.session_state:
 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)
+selected_customer = st.sidebar.selectbox("Choose a customer:", list(st.session_state.customers.keys()))
 
 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": "",
-        "user_input": ""
+        "chat": [], "system_result": None,
+        "agent_reply": "", "support_required": "", "user_input": ""
     }
 
 session = st.session_state.chat_sessions[selected_customer]
-
 st.title("Smart Customer Support Assistant (for Agents Only)")
 
+# Conversation Window
 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'])
 
+# Input & Analysis
 col1, col2 = st.columns([6, 1])
 with col1:
     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 user_input.strip():
-    session["chat"].append({"role": "user", "content": user_input})
-    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
-
-    if final_score < 0.5 and top_intents:
-        intent = top_intents[0]
-        response = generate_response(intent, human=True)
-        session["chat"].append({"role": "assistant", "content": response, "auto": True})
-        session["system_result"] = None
-        session["support_required"] = "🟢 Automated response handled this request."
-    else:
-        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
-        }
-        session["support_required"] = "🔴 Human support required."
-        session["agent_reply"] = ""
-
-    st.rerun()
-
+    if st.button("Analyze"):
+        if user_input.strip():
+            session["chat"].append({"role": "user", "content": user_input})
+            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
+
+            if final_score < 0.5 and top_intents:
+                intent = top_intents[0]
+                response = generate_response(intent, human=True)
+                session["chat"].append({"role": "assistant", "content": response, "auto": True})
+                session["system_result"] = None
+                session["support_required"] = "🟢 Automated response handled this request."
+            else:
+                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
+                }
+                session["support_required"] = "🔴 Human support required."
+                session["agent_reply"] = ""
+            st.rerun()
+
+# Support Tag
 if session["support_required"]:
     st.markdown(f"### {session['support_required']}")
 
+# Agent Reply Console
 st.subheader("Agent Response Console")
 session["agent_reply"] = st.text_area("Compose your reply:", value=session["agent_reply"], key="agent_reply_box")
 if st.button("Send Reply"):
@@ -146,17 +139,18 @@ if st.button("Send Reply"):
         session["support_required"] = ""
         st.experimental_rerun()
 
+# Human Needed View
 if session["system_result"] is not None:
     st.markdown("#### Customer Status")
     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 session['system_result']['intents']:
+    for intent in session["system_result"]["intents"]:
         suggestion = generate_response(intent, human=True)
         st.markdown(f"**• {intent.capitalize()}**")
         st.code(suggestion)
 
+# End Button
 if st.button("End Conversation"):
     session["chat"] = []
     session["system_result"] = None