app.py

# Smart Customer Support Assistant (Enhanced UI Version)
# Note: Core analysis logic remains unchanged, now with text generation

import streamlit as st
from transformers import pipeline
import re

# ------------------------------
# Load models (now includes 3rd: text generation)
# ------------------------------
emotion_classifier = pipeline(
    "text-classification",
    model="j-hartmann/emotion-english-distilroberta-base",
    return_all_scores=True
)
intent_classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
text_generator = pipeline("text2text-generation", model="declare-lab/flan-alpaca-base")

# ------------------------------
# Candidate tasks / prompts
# ------------------------------
candidate_tasks = [
    "change mobile plan",
    "top up balance",
    "report service outage",
    "ask for billing support",
    "reactivate service",
    "cancel subscription",
    "check account status",
    "upgrade device"
]

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']
    return output

urgent_emotions = {"anger", "frustration", "anxiety", "urgency", "afraid", "annoyed"}
moderate_emotions = {"confused", "sad", "tired", "concerned", "sadness"}

# ------------------------------
# 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 = 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
    elif emotion.lower() in moderate_emotions:
        return 0.6
    else:
        return 0.2

# ------------------------------
# Streamlit UI Logic
# ------------------------------
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:
    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")
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)

    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

    st.session_state.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."
    else:
        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
        }
        st.session_state.support_required = "🔴 Human support required."

# Show support need status
if st.session_state.support_required:
    st.markdown(f"### {st.session_state.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)
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:
    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("#### Detected Customer Needs")
    for intent in st.session_state.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