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| import os | |
| import time | |
| 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 | |
| from streamlit_autorefresh import st_autorefresh | |
| # Extract text from PDF with fallback | |
| # --- Document Loaders --- | |
| def extract_text_from_pdf(pdf_path): | |
| try: | |
| text = "" | |
| with open(pdf_path, 'rb') as file: | |
| pdf_reader = PyPDF2.PdfReader(file) | |
| for page_num in range(len(pdf_reader.pages)): | |
| page = pdf_reader.pages[page_num] | |
| 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 "" | |
| 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): | |
| question_embedding = embed_model.encode([question])[0] | |
| D, I = index.search(np.array([question_embedding]), k) | |
| return [text_chunks[i] for i in I[0]] | |
| # Generate answer using Groq API with retries and timeout | |
| def generate_answer_with_groq(question, context, retries=3, delay=2): | |
| url = "https://api.groq.com/openai/v1/chat/completions" | |
| api_key = os.environ.get("GROQ_API_KEY") | |
| if not api_key: | |
| return "⚠️ GROQ_API_KEY not set." | |
| 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. " | |
| "Always sound warm, helpful, and trustworthy like a professional customer support agent." | |
| ) | |
| }, | |
| {"role": "user", "content": prompt}, | |
| ], | |
| "temperature": 0.5, | |
| "max_tokens": 300, | |
| } | |
| for attempt in range(retries): | |
| try: | |
| response = requests.post(url, headers=headers, json=payload, timeout=10) | |
| response.raise_for_status() | |
| result = response.json() | |
| return result['choices'][0]['message']['content'].strip() | |
| except requests.exceptions.HTTPError as e: | |
| if response.status_code == 503 and attempt < retries - 1: | |
| time.sleep(delay) | |
| continue | |
| else: | |
| return f"⚠️ Groq API HTTPError: {e}" | |
| except Exception as e: | |
| return f"⚠️ Groq API Error: {e}" | |
| # Twilio message fetch and send | |
| def fetch_latest_incoming_message(account_sid, auth_token, conversation_sid): | |
| client = Client(account_sid, auth_token) | |
| 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(account_sid, auth_token, conversation_sid, body): | |
| try: | |
| client = Client(account_sid, auth_token) | |
| message = client.conversations.v1.conversations(conversation_sid).messages.create(author="system", body=body) | |
| return message.sid | |
| except Exception as e: | |
| return str(e) | |
| # Streamlit UI | |
| st.set_page_config(page_title="Quasa – A Smart WhatsApp Chatbot", layout="wide") | |
| st.title("📱 Quasa – A Smart WhatsApp Chatbot") | |
| if "last_index" not in st.session_state: | |
| st.session_state.last_index = -1 | |
| 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("⚠️ Some secrets not found. Please enter missing credentials below:") | |
| account_sid = st.text_input("Twilio SID", value=account_sid or "") | |
| auth_token = st.text_input("Twilio Auth Token", type="password", value=auth_token or "") | |
| GROQ_API_KEY = st.text_input("GROQ API Key", type="password", value=GROQ_API_KEY or "") | |
| enable_autorefresh = st.checkbox("🔄 Enable Auto-Refresh", value=True) | |
| interval_seconds = st.selectbox("Refresh Interval (seconds)", options=[5, 10, 15, 30, 60], index=4) | |
| if enable_autorefresh: | |
| st_autorefresh(interval=interval_seconds * 1000, key="auto-refresh") | |
| if all([account_sid, auth_token, GROQ_API_KEY, conversation_sid]): | |
| os.environ["GROQ_API_KEY"] = GROQ_API_KEY | |
| def setup_knowledge_base(): | |
| folder_path = "docs" | |
| all_text = "" | |
| try: | |
| for file in os.listdir(folder_path): | |
| if file.endswith(".pdf"): | |
| all_text += extract_text_from_pdf(os.path.join(folder_path, file)) + "\n" | |
| elif file.endswith((".docx", ".doc")): | |
| all_text += extract_text_from_docx(os.path.join(folder_path, file)) + "\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 | |
| except Exception as e: | |
| st.error(f"Error setting up knowledge base: {e}") | |
| return None, None, None | |
| index, embedding_model, text_chunks = setup_knowledge_base() | |
| if index is None: | |
| st.stop() | |
| st.success("✅ Knowledge base ready. Monitoring WhatsApp...") | |
| with st.spinner("⏳ Checking for new WhatsApp messages..."): | |
| question, sender, msg_index = fetch_latest_incoming_message(account_sid, auth_token, conversation_sid) | |
| if question and msg_index > st.session_state.last_index: | |
| st.session_state.last_index = msg_index | |
| st.info(f"📥 New Question from {sender}:\n\n> {question}") | |
| relevant_chunks = retrieve_chunks(question, index, embedding_model, text_chunks) | |
| context = "\n\n".join(relevant_chunks) | |
| answer = generate_answer_with_groq(question, context) | |
| send_twilio_message(account_sid, auth_token, conversation_sid, answer) | |
| st.success("📤 Answer sent via WhatsApp!") | |
| st.markdown(f"### ✨ Answer:\n\n{answer}") | |
| else: | |
| st.caption("✅ No new message yet. Waiting for refresh...") | |
| else: | |
| st.warning("❗ Please provide all required credentials and conversation SID.") |