app.py

import gradio as gr
import requests
import pandas as pd
from langchain.chat_models import ChatOpenAI
from langchain.document_loaders import CSVLoader
from langchain_together import TogetherEmbeddings
from langchain.vectorstores import Chroma
from langchain_core.vectorstores import InMemoryVectorStore
from langchain import PromptTemplate
from langchain import LLMChain
from langchain_together import Together
import os

os.environ['TOGETHER_API_KEY'] = "c2f52626b97118b71c0c36f66eda4f5957c8fc475e760c3d72f98ba07d3ed3b5"

# Initialize global variable for vectorstore
vectorstore = None
embeddings = TogetherEmbeddings(model="togethercomputer/m2-bert-80M-8k-retrieval")
llama3 = Together(model="meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo", max_tokens=1024)

def update_csv_files():
    login_url = "https://livesystem.hisabkarlay.com/auth/login"
    payload = {
        "username": "user@123",
        "password": "user@123",
        "client_secret": "kNqJjlPkxyHdIKt3szCt4PYFWtFOdUheb8QVN8vQ",
        "client_id": "5",
        "grant_type": "password"
    }
    response = requests.post(login_url, data=payload)

    if response.status_code == 200:
        access_token = response.json()['access_token']
    else:
        return f"Failed to log in: {response.status_code}"

    report_url = "https://livesystem.hisabkarlay.com/connector/api/profit-loss-report"
    headers = {"Authorization": f"Bearer {access_token}"}
    response = requests.get(report_url, headers=headers)
    profit_loss_data = response.json()['data']
    keys = list(profit_loss_data.keys())
    del keys[23]
    del keys[20]
    del keys[19]
    data_dict = {}
    for key in keys:
        data_dict[key] = profit_loss_data.get(key)
    df = pd.DataFrame(data_dict, index=[0])
    df.to_csv('profit_loss.csv', index=False)

    report_url = "https://livesystem.hisabkarlay.com/connector/api/purchase-sell"
    response = requests.get(report_url, headers=headers)
    sell_purchase_data = response.json()
    sell_purchase_data = dict(list(sell_purchase_data.items())[2:])
    df = pd.json_normalize(sell_purchase_data)
    df.to_csv('purchase_sell_report.csv', index=False)

    report_url = "https://livesystem.hisabkarlay.com/connector/api/trending-products"
    response = requests.get(report_url, headers=headers)
    trending_product_data = response.json()['data']
    df = pd.DataFrame(trending_product_data)
    df.columns = ['Product Units Sold', 'Product Name', 'Unit Type', 'SKU (Stock Keeping Unit)']
    df.to_csv('trending_product.csv', index=False)

    return "CSV files updated successfully!"

def initialize_embedding():
    global vectorstore

    file_paths = ["profit_loss.csv", "purchase_sell_report.csv", "trending_product.csv"]
    documents = []
    for path in file_paths:
        loader = CSVLoader(path, encoding="windows-1252")
        documents.extend(loader.load())

    vectorstore = InMemoryVectorStore.from_texts(
        [doc.page_content for doc in documents],
        embedding=embeddings,
    )
    return "Embeddings initialized successfully!"

def qa_chain(query):
    if vectorstore is None:
        return "Please initialize the embeddings first."
    
    retriever = vectorstore.as_retriever()
    retrieved_documents = retriever.invoke(query)
    return retrieved_documents

def generate_response(query, history):
    if vectorstore is None:
        return "Please initialize the embeddings first.", history

    retrieved_documents = qa_chain(query)
    chat_template = """
You are a highly intelligent and professional AI assistant.

Generate the response according to the user's query:

- If the user enters a greeting (e.g., "Hi", "Hello", "Good day"), give the following response:
    "Welcome to HisabKarLay, your business partner! You may choose from the following services 👇:

    1. Reports
    2. Forecasts
    3. Best Selling Items
    4. Chat with AI Agent
    5. Chat with our Customer Care Team
    6. Share your Feedback
    7. Checkout Latest Offers

    🔆 Suggestion: To make a selection, send the relevant number like 1

    ⭕ Note: If at any stage you wish to go back to the previous menu, type back, and to go to the main menu, type main menu.

    ⭕ Note: If you want to change the language, type and send 'change language.'

    💁🏻♂️ Help: If you need any help, you can call us at +923269498569."

- If the user enters a specific number (1-7), give the following responses:
    - If the user enters only 1, give the following response:
        If you are interested in insights related to your business, please find the available reports below:
            -> Profit Loss Report: Detailed analysis of your financial performance.
            -> Stock Report: Overview of your current inventory status.
            -> Sales Report: Summary of sales activities.
            -> Purchase Report: Insights into procurement activities.
            -> Trending Item Report: Highlights of popular products in demand.
    - If the user enters only 2, give the following response:
        For strategic planning and inventory management, consider the following forecasts:
            -> Sales Forecast: Projected sales for upcoming periods.
            -> Product Sales Forecast: Expected sales performance of specific products.
    - If the user enters only 3, give the following response:
        -> You have expressed interest in identifying the best-selling item. Please allow me to provide you with detailed insights.
    - If the user enters only 4, give the following response:
        -> Feel free to ask any questions regarding the status of your business. I’m here to assist you.
    - If the user enters only 5, give the following response:
        For inquiries or further assistance, please send your query to:
            -> Contact Number: +923269498569
    - If the user enters only 6, give the following response:
        Your feedback is invaluable to us. Kindly share your thoughts and suggestions at:
            -> Contact Number: +923269498569
    - If the user enters only 7, give the following response:
        -> Check out our latest offers and promotions to maximize your business potential.

- **Fallback**: If the query doesn't match a greeting or a specific command (1-7), provide a professional and clean response based on the user's question.

    When answering based on retrieved documents, make sure to exclude unnecessary metadata (like document IDs) and display only the relevant content. For example, extract the actual report details such as sales, purchases, and other key information without showing raw document metadata.


    Ensure the information is formatted clearly and no irrelevant document information (such as IDs or metadata) is displayed.

Context: {retrieved_documents}
Question: {query}
"""
    prompt = PromptTemplate(
        input_variables=['retrieved_documents', 'query'],
        template=chat_template
    )
    
    Generated_chat = LLMChain(llm=llama3, prompt=prompt)
    response = Generated_chat.invoke({'retrieved_documents': retrieved_documents, 'query': query})

    # Ensure history is always a list of two-element lists [query, response]
    history.append([query, response['text']])
    
    # Return the updated history and the new response for display
    return history, history

def gradio_app():
    with gr.Blocks() as app:
        gr.Markdown("# Embedding and QA Interface")

        # Chatbox elements
        chatbot = gr.Chatbot(label="Chat")
        query_input = gr.Textbox(label="Enter your query")
        generate_response_btn = gr.Button("Generate Response")

        # Status output textboxes for CSV update and embedding initialization
        update_csv_status = gr.Textbox(label="CSV Update Status", interactive=False)
        initialize_status = gr.Textbox(label="Embedding Initialization Status", interactive=False)

        # Buttons for CSV update and embedding initialization
        update_csv_button = gr.Button("Update CSV Files")
        initialize_button = gr.Button("Initialize Embedding")

        # Button click actions
        update_csv_button.click(update_csv_files, outputs=update_csv_status)
        initialize_button.click(initialize_embedding, outputs=initialize_status)

        # Chatbot functionality with history
        history = gr.State([])  # Chat history state
        generate_response_btn.click(generate_response, inputs=[query_input, history], outputs=[chatbot, history])

    app.launch()

# Run the Gradio app
gradio_app()