app.py

import os
import logging
import traceback
from typing import List, Tuple

import gradio as gr
from dotenv import load_dotenv

from langchain.document_loaders import TextLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.chat_models import ChatOpenAI
from langchain.chains import RetrievalQA
from langchain.prompts import PromptTemplate

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Load environment variables
load_dotenv()

class RAGChatbot:
    def __init__(self, document_path):
        """
        Initialize RAG Chatbot with document vectorization
        
        :param document_path: Path to the input document
        """
        self.openai_api_key = os.getenv('OPENAI_API_KEY')
        
        if not self.openai_api_key:
            raise ValueError("OpenAI API Key is not set. Please add it to environment variables.")
        
        self.document_path = document_path
        self.vectorstore = self._load_or_create_vector_store()
        self.qa_system = self._create_qa_system()

    def _load_or_create_vector_store(self):
        """
        Load existing FAISS index or create a new one
        
        :return: FAISS vector store
        """
        try:
            embeddings = OpenAIEmbeddings(openai_api_key=self.openai_api_key)
            
            # Check if index exists
            if os.path.exists('faiss_index'):
                logger.info("Loading existing vector store...")
                return FAISS.load_local('faiss_index', embeddings)
            
            # Create new vector store
            logger.info("Creating new vector store...")
            loader = TextLoader(self.document_path, encoding='utf-8')
            documents = loader.load()
            
            text_splitter = RecursiveCharacterTextSplitter(
                chunk_size=3000,
                chunk_overlap=600,
                separators=["\n\n\n", "\n\n", "\n", ".", " ", ""]
            )
            texts = text_splitter.split_documents(documents)
            
            vectorstore = FAISS.from_documents(texts, embeddings)
            
            # Ensure faiss_index directory exists
            os.makedirs('faiss_index', exist_ok=True)
            vectorstore.save_local('faiss_index')
            
            return vectorstore
        
        except Exception as e:
            logger.error(f"Vector store creation error: {e}")
            logger.error(traceback.format_exc())
            raise

    def _create_qa_system(self):
        """
        Create Question-Answering system with custom prompt
        
        :return: RetrievalQA chain
        """
        custom_prompt = PromptTemplate(
            input_variables=["context", "question"],
            template="""You are an expert AI assistant for Beeline Uzbekistan tariffs. 
            Provide clear, precise answers based on the context. 
            Respond in the language of the question.
            
            Context: {context}
            Question: {question}
            
            Comprehensive Answer:"""
        )
        
        llm = ChatOpenAI(
            model_name="gpt-3.5-turbo",
            openai_api_key=self.openai_api_key,
            temperature=0.1
        )
        
        return RetrievalQA.from_chain_type(
            llm=llm,
            chain_type="stuff",
            retriever=self.vectorstore.as_retriever(
                search_type="mmr",
                search_kwargs={"k": 4, "fetch_k": 10}
            ),
            chain_type_kwargs={"prompt": custom_prompt}
        )

    def chat(self, message: str, history: List[Tuple[str, str]]) -> str:
        """
        Main chat method with multilingual support
        
        :param message: User input message
        :param history: Chat history
        :return: Bot response
        """
        # Handle initial greeting
        if message.lower() in ['init', 'start', 'begin']:
            return "Assalomu alaykum! 📱 Beeline tarifları haqida qanday ma'lumot kerak? (Hello! What Beeline tariff information do you need?)"
        
        # Multilingual greeting handling
        greetings = {
            'uz': ['salom', 'assalomu alaykum', 'hammaga salom'],
            'ru': ['привет', 'здравствуйте', 'hi', 'hello'],
            'en': ['hi', 'hello', 'hey']
        }
        
        for lang, greeting_list in greetings.items():
            if message.lower() in greeting_list:
                return {
                    'uz': "Salom! Sizga qanday yordam bera olaman? 🤖",
                    'ru': "Привет! Чем могу помочь? 🤖",
                    'en': "Hello! How can I assist you today about Beeline tariffs? 🤖"
                }[lang]
        
        try:
            # Query the document
            response = self.qa_system.run(message)
            
            # Add conversational touch
            response += "\n\n📞 Yana bir nima so'rashingizni xohlar edingizmi? (Would you like to ask anything else?)"
            
            return response
        
        except Exception as e:
            logger.error(f"Chat processing error: {e}")
            logger.error(traceback.format_exc())
            return "Kechirasiz, so'rovingizni qayta ishlashda xatolik yuz berdi. Iltimos, qaytadan urinib ko'ring. (Sorry, there was an error processing your request. Please try again.)"

def create_demo() -> gr.Interface:
    """
    Create Gradio interface for the chatbot
    
    :return: Gradio demo
    """
    # Initialize chatbot with document
    chatbot = RAGChatbot('12.txt')
    
    with gr.Blocks() as demo:
        gr.Markdown("# 📱 Beeline Uzbekistan Tariff Assistant")
        
        chatbot_interface = gr.Chatbot(
            height=600, 
            show_copy_button=True,
            avatar_images=["🤔", "🤖"]  # User and Bot avatars
        )
        
        with gr.Row():
            msg = gr.Textbox(
                show_label=False, 
                placeholder="Beeline tariffları haqida so'rang... (Ask about Beeline tariffs...)", 
                container=False
            )
            submit = gr.Button("Yuborish (Send)", variant="primary")
            clear = gr.Button("Yangi suhbat (New Chat)")
        
        def respond(message, chat_history):
            bot_message = chatbot.chat(message, chat_history)
            chat_history.append((message, bot_message))
            return "", chat_history
        
        def init_chat():
            initial_greeting = chatbot.chat("init", [])
            return [("", initial_greeting)]
        
        # Event handlers
        submit.click(respond, [msg, chatbot_interface], [msg, chatbot_interface])
        msg.submit(respond, [msg, chatbot_interface], [msg, chatbot_interface])
        clear.click(fn=init_chat, inputs=None, outputs=chatbot_interface)
        demo.load(init_chat, inputs=None, outputs=chatbot_interface)
    
    return demo

# Main execution
demo = create_demo()
if __name__ == "__main__":
    demo.launch(debug=True)