Update app.py

app.py

@@ -1,35 +1,38 @@
 import os
-import json
 import time
 import threading
-import datetime
-import csv
-import docx
 import streamlit as st
-from io import StringIO
+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
-import pdfplumber
+from io import StringIO
 from pdfminer.high_level import extract_text_to_fp
 from pdfminer.layout import LAParams
-from sentence_transformers import SentenceTransformer
-from transformers import AutoTokenizer
-import faiss
-from twilio.rest import Client
 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"
 
-DOCS_FOLDER = "./docs"
-
-# ---------------- PDF / DOCX / JSON LOADERS ----------------
+# ---------------- PDF & DOCX & JSON Extraction ----------------
 def _extract_tables_from_page(page):
     tables = page.extract_tables()
     formatted_tables = []
     for table in tables:
-        formatted_row = [[cell if cell else "" for cell in row] for row in table]
-        formatted_tables.append(formatted_row)
+        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):
@@ -43,39 +46,55 @@ def extract_text_from_pdf(pdf_path):
                 if text:
                     text_output.write(text + "\n\n")
     except Exception as e:
-        print(f"[pdfplumber error] Falling back: {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 Exception as e:
-        print(f"[DOCX error] {e}")
+        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, list):
-            return "\n\n".join("\n".join(f"{k}: {v}" for k, v in d.items()) for d in data if isinstance(d, dict))
         if isinstance(data, dict):
-            return "\n".join(f"{k}: {v}" for k, v in data.items())
-        return str(data)
+            # 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 error] {e}")
+        print(f"JSON read error: {e}")
         return ""
 
-def clean_extracted_text(text):
-    return '\n'.join(' '.join(line.strip().split()) for line in text.splitlines() if line.strip())
-
-# ---------------- CHUNKING ----------------
+# ---------------- Chunking ----------------
 def chunk_text(text, tokenizer, chunk_size=128, chunk_overlap=32):
     tokens = tokenizer.tokenize(text)
-    chunks, start = [], 0
+    chunks = []
+    start = 0
     while start < len(tokens):
         end = min(start + chunk_size, len(tokens))
         chunk = tokens[start:end]
@@ -87,9 +106,9 @@ def chunk_text(text, tokenizer, chunk_size=128, chunk_overlap=32):
 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] if i < len(text_chunks)]
+    return [text_chunks[i] for i in I[0]]
 
-# ---------------- GROQ ANSWER GENERATOR ----------------
+# ---------------- 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")
@@ -97,92 +116,131 @@ def generate_answer_with_groq(question, context):
         "Authorization": f"Bearer {api_key}",
         "Content-Type": "application/json",
     }
-
     prompt = (
         f"Customer asked: '{question}'\n\n"
-        f"Here is the relevant information:\n{context}\n\n"
-        "Reply as a friendly toy shop assistant, include customer name from the context if possible."
+        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, "
+        f"addressing the customer by their name if it's available in the context."
     )
-
     payload = {
         "model": "llama3-8b-8192",
         "messages": [
             {
                 "role": "system",
-                "content": "You are ToyBot, a helpful assistant for an online toy shop."
+                "content": (
+                    "You are ToyBot, a friendly WhatsApp assistant for an online toy shop. "
+                    "Help customers with toys, delivery, and returns in a helpful tone. "
+                    "When responding, try to find the customer's name in the provided context "
+                    "and address them directly. If the context contains order details and status, "
+                    "include that information in your response."
+                )
             },
-            {"role": "user", "content": prompt}
-        ]
+            {"role": "user", "content": prompt},
+        ],
+        "temperature": 0.5,
+        "max_tokens": 300,
     }
-
     response = requests.post(url, headers=headers, json=payload)
-    return response.json()["choices"][0]["message"]["content"]
-
-# ---------------- TWILIO MONITOR ----------------
-def handle_incoming_messages(index, embed_model, tokenizer, text_chunks):
-    client = Client(os.environ["TWILIO_ACCOUNT_SID"], os.environ["TWILIO_AUTH_TOKEN"])
-    conversation_sid = os.environ.get("TWILIO_CONVERSATION_SID")
-
-    if not conversation_sid:
-        print("❌ TWILIO_CONVERSATION_SID not set")
-        return
-
-    last_check_time = APP_START_TIME
-
-    while True:
-        try:
-            messages = client.conversations.conversations(conversation_sid).messages.list(order="asc")
-            for msg in messages:
-                msg_time = msg.date_created.replace(tzinfo=datetime.timezone.utc)
-                if msg_time > last_check_time:
-                    print(f"📩 New message from {msg.author}: {msg.body}")
-                    answer = generate_answer_with_groq(msg.body, "\n".join(retrieve_chunks(msg.body, index, embed_model, text_chunks)))
-                    client.conversations.conversations(conversation_sid).messages.create(author="ToyBot", body=answer)
-            last_check_time = datetime.datetime.now(datetime.timezone.utc)
-        except TwilioRestException as e:
-            print(f"[Twilio error] {e}")
-        time.sleep(10)
-
-# ---------------- STREAMLIT UI ----------------
-st.title("🎁 ToyShop Assistant – RAG WhatsApp Bot")
-
-def load_all_documents(folder_path):
-    full_text = ""
-    all_tables = []
+    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):
-        filepath = os.path.join(folder_path, filename)
-        ext = filename.lower().split(".")[-1]
-        if ext == "pdf":
-            text, tables = extract_text_from_pdf(filepath)
-            all_tables.extend(tables)
-        elif ext == "docx":
-            text = extract_text_from_docx(filepath)
-        elif ext == "json":
-            text = load_json_data(filepath)
-        else:
+        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(filepath, "r", encoding="utf-8") as f:
-                    text = f.read()
-            except Exception:
-                continue
-        full_text += clean_extracted_text(text) + "\n\n"
-    return full_text, all_tables
-
-with st.spinner("Loading documents..."):
-    full_text, tables = load_all_documents(DOCS_FOLDER)
-
-    tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
-    embed_model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
-    chunks = chunk_text(full_text, tokenizer)
-    embeddings = embed_model.encode(chunks)
-    
-    index = faiss.IndexFlatL2(embeddings.shape[1])
-    index.add(np.array(embeddings))
-
-    if "listener_started" not in st.session_state:
-        threading.Thread(target=handle_incoming_messages, args=(index, embed_model, tokenizer, chunks), daemon=True).start()
-        st.session_state.listener_started = True
-        st.success("✅ WhatsApp listener started.")
-
-    st.success(f"📚 Loaded {len(chunks)} text chunks from docs/ folder")
+                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.")