Update app.py

app.py

@@ -2,25 +2,25 @@ import os
 import time
 import streamlit as st
 from twilio.rest import Client
+from twilio.base.exceptions import TwilioRestException
 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
 
-# --- Streamlit Config ---
-st.set_page_config(page_title="Quasa – A Smart WhatsApp Chatbot", layout="wide")
-
-# --- Utility: Extract Text ---
+# --- 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 in pdf_reader.pages:
+            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
@@ -35,7 +35,6 @@ def extract_text_from_docx(docx_path):
     except:
         return ""
 
-# --- Chunking ---
 def chunk_text(text, tokenizer, chunk_size=150, chunk_overlap=30):
     tokens = tokenizer.tokenize(text)
     chunks, start = [], 0
@@ -46,36 +45,24 @@ def chunk_text(text, tokenizer, chunk_size=150, chunk_overlap=30):
         start += chunk_size - chunk_overlap
     return chunks
 
-# --- Retrieve Relevant 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]]
 
-# --- GROQ API Call ---
+# --- GROQ Answer Generator ---
 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")
+    api_key = os.environ["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."
-    )
+    prompt = f"Based on the following context, answer the question: '{question}'\n\nContext:\n{context}"
     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": "system", "content": "You are a real human and you will respond as real human. You will be asked questions and you will respond like a human."},
             {"role": "user", "content": prompt},
         ],
         "temperature": 0.5,
@@ -91,9 +78,10 @@ def generate_answer_with_groq(question, context, retries=3, delay=2):
             if "503" in str(e) and attempt < retries - 1:
                 time.sleep(delay)
                 continue
-            return f"⚠️ Groq API Error: {str(e)}"
+            else:
+                return f"⚠️ Groq API Error: {str(e)}"
 
-# --- Twilio Helpers ---
+# --- Twilio Chat Handlers ---
 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)
@@ -105,84 +93,67 @@ def fetch_latest_incoming_message(account_sid, auth_token, conversation_sid):
 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
-        )
+        message = client.conversations.v1.conversations(conversation_sid).messages.create(author="system", body=body)
         return message.sid
     except Exception as e:
         return str(e)
 
-# --- UI Styling ---
-st.markdown("""
-    <style>
-        .big-font { font-size: 28px !important; font-weight: bold; }
-        .small-font { font-size: 16px; color: #555; }
-        .stButton > button {
-            background-color: #0066CC; color: white;
-            padding: 0.5em 1em; border-radius: 8px; font-size: 18px;
-        }
-        .stTextInput > div > input { font-size: 16px; }
-    </style>
-""", unsafe_allow_html=True)
-
-st.markdown('<div class="big-font">📱 Quasa – A Smart WhatsApp Chatbot</div>', unsafe_allow_html=True)
-st.markdown('<div class="small-font">Talk to your documents using WhatsApp. Powered by Groq, Twilio, and RAG.</div>', unsafe_allow_html=True)
-
-# --- Credentials ---
-account_sid = st.secrets.get("TWILIO_SID") or st.text_input("🔐 Twilio SID", "")
-auth_token = st.secrets.get("TWILIO_TOKEN") or st.text_input("🔐 Twilio Auth Token", type="password")
-groq_key = st.secrets.get("GROQ_API_KEY") or st.text_input("🔐 GROQ API Key", type="password")
-
-if all([account_sid, auth_token, groq_key]):
-    os.environ["GROQ_API_KEY"] = groq_key
-
-    # Conversation SID Input
-    conversation_sid = st.text_input("💬 Enter Twilio Conversation SID", key="conv_sid")
-
-    if st.button("🔄 Load Conversation"):
-        if not conversation_sid:
-            st.warning("Please enter a valid Conversation SID.")
-        else:
-            st.success(f"Conversation SID `{conversation_sid}` loaded!")
-
-            @st.cache_resource
-            def setup_knowledge_base():
-                folder_path = "docs"
-                all_text = ""
-                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))
-                return index, model, chunks
-
-            index, embedding_model, text_chunks = setup_knowledge_base()
-            st.success("✅ Knowledge base loaded!")
-
-            if "last_processed_index" not in st.session_state:
-                st.session_state.last_processed_index = -1
-
-            if st.button("📲 Check WhatsApp for New Message"):
-                with st.spinner("Checking messages..."):
-                    question, sender, msg_index = fetch_latest_incoming_message(account_sid, auth_token, conversation_sid)
-                    if question and msg_index != st.session_state.last_processed_index:
-                        st.session_state.last_processed_index = msg_index
-                        st.info(f"📥 New message 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 to user!")
-                        st.markdown(f"### 🧠 Answer:\n\n{answer}")
-                    else:
-                        st.warning("No new messages found.")
+# --- Streamlit UI ---
+st.set_page_config(page_title="SMEHelpBot – WhatsApp Integration", layout="wide")
+st.title("📱 SMEHelpBot + WhatsApp (via Twilio)")
+
+# Load from secrets
+account_sid = st.secrets.get("TWILIO_SID")
+auth_token = st.secrets.get("TWILIO_TOKEN")
+GROQ_API_KEY = st.secrets.get("GROQ_API_KEY")
+
+# Fallback for manual input
+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 "")
+
+# New: Let user enter conversation_sid manually
+conversation_sid = st.text_input("Twilio Conversation SID")
+
+if all([account_sid, auth_token, GROQ_API_KEY, conversation_sid]):
+    os.environ["GROQ_API_KEY"] = GROQ_API_KEY
+
+    @st.cache_resource
+    def setup_knowledge_base():
+        folder_path = "docs"
+        all_text = ""
+        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))
+        return index, model, chunks
+
+    index, embedding_model, text_chunks = setup_knowledge_base()
+
+    st.success("✅ Knowledge base ready. Monitoring WhatsApp...")
+
+    if st.button("🔁 Check for New WhatsApp Query"):
+        with st.spinner("Checking messages..."):
+            question, sender, msg_index = fetch_latest_incoming_message(account_sid, auth_token, conversation_sid)
+            if question:
+                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.warning("No new messages from users found.")
 else:
-    st.warning("Please enter all required credentials.")
+    st.warning("❗ Please provide all required credentials including Conversation SID.")