app.py

# app.py

import os
import time
import threading
import streamlit as st
from twilio.rest import Client
from pdfminer.high_level import extract_text
from sentence_transformers import SentenceTransformer
from transformers import AutoTokenizer
import faiss
import numpy as np
import docx
from groq import Groq
import PyPDF2
import requests

# --- Text Extraction Utilities ---
def extract_text_from_pdf(pdf_path):
    try:
        text = ""
        with open(pdf_path, 'rb') as file:
            reader = PyPDF2.PdfReader(file)
            for page in reader.pages:
                page_text = page.extract_text()
                if page_text:
                    text += page_text
        return text
    except:
        return extract_text(pdf_path)

def extract_text_from_docx(docx_path):
    try:
        doc = docx.Document(docx_path)
        return '\n'.join(para.text for para in doc.paragraphs)
    except:
        return ""

# --- Chunking & Retrieval ---
def chunk_text(text, tokenizer, chunk_size=150, chunk_overlap=30):
    tokens = tokenizer.tokenize(text)
    chunks, start = [], 0
    while start < len(tokens):
        end = min(start + chunk_size, len(tokens))
        chunk_tokens = tokens[start:end]
        chunks.append(tokenizer.convert_tokens_to_string(chunk_tokens))
        start += chunk_size - chunk_overlap
    return chunks

def retrieve_chunks(question, index, embed_model, text_chunks, k=3):
    q_embedding = embed_model.encode([question])[0]
    D, I = index.search(np.array([q_embedding]), k)
    return [text_chunks[i] for i in I[0]]

# --- Groq Answer Generator ---
def generate_answer_with_groq(question, context):
    url = "https://api.groq.com/openai/v1/chat/completions"
    api_key = os.environ.get("GROQ_API_KEY")
    headers = {
        "Authorization": f"Bearer {api_key}",
        "Content-Type": "application/json",
    }
    prompt = (
        f"Customer asked: '{question}'\n\n"
        f"Here is the relevant product or policy info to help:\n{context}\n\n"
        f"Respond in a friendly and helpful tone as a toy shop support agent."
    )
    payload = {
        "model": "llama3-8b-8192",
        "messages": [
            {
                "role": "system",
                "content": (
                    "You are ToyBot, a friendly and helpful WhatsApp assistant for an online toy shop. "
                    "Your goal is to politely answer customer questions, help them choose the right toys, "
                    "provide order or delivery information, explain return policies, and guide them through purchases. "
                )
            },
            {"role": "user", "content": prompt},
        ],
        "temperature": 0.5,
        "max_tokens": 300,
    }
    response = requests.post(url, headers=headers, json=payload)
    response.raise_for_status()
    return response.json()['choices'][0]['message']['content'].strip()

# --- Twilio Functions ---
def get_latest_whatsapp_conversation_sid(client):
    conversations = client.conversations.v1.conversations.list(limit=20)
    for convo in conversations:
        try:
            participants = client.conversations.v1.conversations(convo.sid).participants.list()
            for p in participants:
                if (p.identity and p.identity.startswith("whatsapp:")) or (
                    p.messaging_binding and p.messaging_binding.get("address", "").startswith("whatsapp:")
                ):
                    return convo.sid
        except:
            continue
    return conversations[0].sid if conversations else None

def fetch_latest_incoming_message(client, conversation_sid):
    messages = client.conversations.v1.conversations(conversation_sid).messages.list(limit=10)
    for msg in reversed(messages):
        if msg.author.startswith("whatsapp:"):
            return msg.body, msg.author, msg.index
    return None, None, None

def send_twilio_message(client, conversation_sid, body):
    return client.conversations.v1.conversations(conversation_sid).messages.create(
        author="system", body=body
    )

# --- Load Knowledge Base ---
def setup_knowledge_base():
    folder_path = "docs"
    all_text = ""
    for file in os.listdir(folder_path):
        path = os.path.join(folder_path, file)
        if file.endswith(".pdf"):
            all_text += extract_text_from_pdf(path) + "\n"
        elif file.endswith((".docx", ".doc")):
            all_text += extract_text_from_docx(path) + "\n"
    tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
    chunks = chunk_text(all_text, tokenizer)
    model = SentenceTransformer('all-mpnet-base-v2')
    embeddings = model.encode(chunks)
    dim = embeddings[0].shape[0]
    index = faiss.IndexFlatL2(dim)
    index.add(np.array(embeddings).astype('float32'))
    return index, model, chunks

# --- Background Polling Thread ---
def start_message_monitor(client, convo_sid, index, embed_model, text_chunks):
    last_index = -1

    def poll_loop():
        nonlocal last_index
        while True:
            try:
                question, sender, msg_index = fetch_latest_incoming_message(client, convo_sid)
                if question and msg_index > last_index:
                    last_index = msg_index
                    print(f"\n📥 New Message from {sender}: {question}")
                    context = "\n\n".join(retrieve_chunks(question, index, embed_model, text_chunks))
                    answer = generate_answer_with_groq(question, context)
                    send_twilio_message(client, convo_sid, answer)
                    print(f"📤 Sent Reply: {answer}")
                time.sleep(3)
            except Exception as e:
                print("❌ Error in polling loop:", e)
                time.sleep(5)

    thread = threading.Thread(target=poll_loop, daemon=True)
    thread.start()

# --- Streamlit UI ---
st.set_page_config(page_title="Quasa – A Smart WhatsApp Chatbot", layout="wide")
st.title("📱 Quasa – A Smart WhatsApp Chatbot")

account_sid = st.secrets.get("TWILIO_SID")
auth_token = st.secrets.get("TWILIO_TOKEN")
GROQ_API_KEY = st.secrets.get("GROQ_API_KEY")

if not all([account_sid, auth_token, GROQ_API_KEY]):
    st.warning("⚠️ Provide all credentials below:")
    account_sid = st.text_input("Twilio SID", value=account_sid or "")
    auth_token = st.text_input("Twilio Token", type="password", value=auth_token or "")
    GROQ_API_KEY = st.text_input("GROQ API Key", type="password", value=GROQ_API_KEY or "")

if all([account_sid, auth_token, GROQ_API_KEY]):
    os.environ["GROQ_API_KEY"] = GROQ_API_KEY
    client = Client(account_sid, auth_token)
    conversation_sid = get_latest_whatsapp_conversation_sid(client)

    if conversation_sid:
        st.success("✅ WhatsApp connected. Initializing chatbot...")
        index, model, chunks = setup_knowledge_base()
        start_message_monitor(client, conversation_sid, index, model, chunks)
        st.success("🟢 Chatbot is running in background and will reply automatically.")
    else:
        st.error("❌ No WhatsApp conversation found.")