app.py

import os
import time
import threading
import streamlit as st
from twilio.rest import Client
from sentence_transformers import SentenceTransformer
from transformers import AutoTokenizer
import faiss
import numpy as np
import docx
from groq import Groq
import requests
from io import StringIO
from pdfminer.high_level import extract_text_to_fp
from pdfminer.layout import LAParams
from twilio.base.exceptions import TwilioRestException
import pdfplumber
import datetime
import csv
import json
import re

APP_START_TIME = datetime.datetime.now(datetime.timezone.utc)
os.environ["PYTORCH_JIT"] = "0"

# ---------------- PDF & DOCX & JSON Extraction ----------------
def _extract_tables_from_page(page):
    tables = page.extract_tables()
    formatted_tables = []
    for table in tables:
        formatted_table = []
        for row in table:
            formatted_row = [cell if cell is not None else "" for cell in row]
            formatted_table.append(formatted_row)
        formatted_tables.append(formatted_table)
    return formatted_tables

def extract_text_from_pdf(pdf_path):
    text_output = StringIO()
    all_tables = []
    try:
        with pdfplumber.open(pdf_path) as pdf:
            for page in pdf.pages:
                all_tables.extend(_extract_tables_from_page(page))
                text = page.extract_text()
                if text:
                    text_output.write(text + "\n\n")
    except Exception as e:
        print(f"pdfplumber error: {e}")
        with open(pdf_path, 'rb') as file:
            extract_text_to_fp(file, text_output, laparams=LAParams(), output_type='text')
    return text_output.getvalue(), all_tables

def _format_tables_internal(tables):
    formatted_tables_str = []
    for table in tables:
        with StringIO() as csvfile:
            writer = csv.writer(csvfile)
            writer.writerows(table)
            formatted_tables_str.append(csvfile.getvalue())
    return "\n\n".join(formatted_tables_str)

def clean_extracted_text(text):
    return '\n'.join(' '.join(line.strip().split()) for line in text.splitlines() if line.strip())

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 ""

def load_json_data(json_path):
    try:
        with open(json_path, 'r', encoding='utf-8') as f:
            data = json.load(f)
        if isinstance(data, dict):
            # Flatten dictionary values (avoiding nested structures as strings)
            return "\n".join(f"{key}: {value}" for key, value in data.items() if not isinstance(value, (dict, list)))
        elif isinstance(data, list):
            # Flatten list of dictionaries
            all_items = []
            for item in data:
                if isinstance(item, dict):
                    all_items.append("\n".join(f"{key}: {value}" for key, value in item.items() if not isinstance(value, (dict, list))))
            return "\n\n".join(all_items)
        else:
            return json.dumps(data, ensure_ascii=False, indent=2)
    except Exception as e:
        print(f"JSON read error: {e}")
        return ""

# ---------------- Chunking ----------------
def chunk_text(text, tokenizer, chunk_size=128, chunk_overlap=32):
    tokens = tokenizer.tokenize(text)
    chunks = []
    start = 0
    while start < len(tokens):
        end = min(start + chunk_size, len(tokens))
        chunk = tokens[start:end]
        chunks.append(tokenizer.convert_tokens_to_string(chunk))
        if end == len(tokens): break
        start += chunk_size - chunk_overlap
    return chunks

def retrieve_chunks(question, index, embed_model, text_chunks, k=3):
    q_embedding = embed_model.encode(question)
    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 information 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 WhatsApp assistant for an online toy shop. "
                    "Help customers with toys, delivery, and returns in a helpful tone."
                )
            },
            {"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 Integration ----------------
def fetch_latest_incoming_message(client, conversation_sid):
    try:
        messages = client.conversations.v1.conversations(conversation_sid).messages.list()
        for msg in reversed(messages):
            if msg.author.startswith("whatsapp:"):
                return {
                    "sid": msg.sid,
                    "body": msg.body,
                    "author": msg.author,
                    "timestamp": msg.date_created,
                }
    except TwilioRestException as e:
        print(f"Twilio error: {e}")
    return None

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

# ---------------- Knowledge Base Setup ----------------
def setup_knowledge_base():
    folder_path = "docs"
    all_text = ""

    for filename in os.listdir(folder_path):
        file_path = os.path.join(folder_path, filename)
        if filename.endswith(".pdf"):
            text, tables = extract_text_from_pdf(file_path)
            all_text += clean_extracted_text(text) + "\n"
            all_text += _format_tables_internal(tables) + "\n"
        elif filename.endswith(".docx"):
            text = extract_text_from_docx(file_path)
            all_text += clean_extracted_text(text) + "\n"
        elif filename.endswith(".json"):
            text = load_json_data(file_path)
            all_text += text + "\n"
        elif filename.endswith(".csv"):
            try:
                with open(file_path, newline='', encoding='utf-8') as csvfile:
                    reader = csv.DictReader(csvfile)
                    for row in reader:
                        line = ' | '.join(f"{k}: {v}" for k, v in row.items())
                        all_text += line + "\n"
            except Exception as e:
                print(f"CSV read error: {e}")

    tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
    chunks = chunk_text(all_text, tokenizer)
    model = SentenceTransformer('all-mpnet-base-v2')
    embeddings = model.encode(chunks, show_progress_bar=False)
    dim = embeddings[0].shape[0]
    index = faiss.IndexFlatL2(dim)
    index.add(np.array(embeddings).astype('float32'))
    return index, model, chunks

# ---------------- Monitor Twilio Conversations ----------------
def start_conversation_monitor(client, index, embed_model, text_chunks):
    processed_convos = set()
    last_processed_timestamp = {}

    def poll_convo(convo_sid):
        while True:
            latest_msg = fetch_latest_incoming_message(client, convo_sid)
            if latest_msg:
                msg_time = latest_msg["timestamp"]
                if convo_sid not in last_processed_timestamp or msg_time > last_processed_timestamp[convo_sid]:
                    last_processed_timestamp[convo_sid] = msg_time
                    question = latest_msg["body"]
                    sender = latest_msg["author"]
                    print(f"📩 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)
            time.sleep(5)

    for convo in client.conversations.v1.conversations.list():
        if convo.sid not in processed_convos:
            processed_convos.add(convo.sid)
            threading.Thread(target=poll_convo, args=(convo.sid,), daemon=True).start()

# ---------------- Main Entry ----------------
if __name__ == "__main__":
    st.title("🤖 ToyBot WhatsApp Assistant")
    st.write("Initializing knowledge base...")

    index, model, chunks = setup_knowledge_base()

    st.success("Knowledge base loaded.")
    st.write("Waiting for WhatsApp messages...")

    account_sid = os.environ.get("TWILIO_ACCOUNT_SID")
    auth_token = os.environ.get("TWILIO_AUTH_TOKEN")
    if not account_sid or not auth_token:
        st.error("❌ Twilio credentials not set.")
    else:
        client = Client(account_sid, auth_token)
        start_conversation_monitor(client, index, model, chunks)
        st.info("✅ Bot is now monitoring Twilio conversations.")