app.py

# -*- coding: utf-8 -*-
"""Copy of Copy of Chatbot with custom knowledge base

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1VSXUmag_76fzebs16YhW_as4mdhHNdkx
"""

#pip install llama-index
#pip install langchain
#pip install gradio
#pip install pandas 
#pip install openpyxl

import pandas as pd
from llama_index import SimpleDirectoryReader, GPTListIndex, readers, GPTSimpleVectorIndex, LLMPredictor, PromptHelper
from langchain import OpenAI
import sys
import os
from IPython.display import Markdown, display
import pandas as pd
from llama_index import SimpleDirectoryReader, GPTListIndex, readers, GPTSimpleVectorIndex, LLMPredictor, PromptHelper
from langchain import OpenAI
from IPython.display import Markdown, display
#import streamlit as st
import gradio as gr
#import gradio
df = pd.read_excel('Shegardi_dataset.xlsx',sheet_name = 'dataset')
#os.environ['OPENAI_API_KEY'] = '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'
os.environ['OPENAI_API_KEY'] = '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'
def construct_index(directory_path):
    # set maximum input size
    max_input_size = 4096
    # set number of output tokens
    num_outputs = 2000
    # set maximum chunk overlap
    max_chunk_overlap = 20
    # set chunk size limit
    chunk_size_limit = 600 

    # define LLM
    llm_predictor = LLMPredictor(llm=OpenAI(temperature=0.5, model_name="text-davinci-003", max_tokens=num_outputs))
    prompt_helper = PromptHelper(max_input_size, num_outputs, max_chunk_overlap, chunk_size_limit=chunk_size_limit)
 
    documents = SimpleDirectoryReader(directory_path).load_data()
    
    index = GPTSimpleVectorIndex(
        documents, llm_predictor=llm_predictor, prompt_helper=prompt_helper
    )

    index.save_to_disk('index.json')

    return index

#construct_index("context_data/data")

#import streamlit as st
# Include other necessary imports here

def is_query_about_cashback(query):
    cashback_keywords = ["cashback", "calculate", "calculation", "reward", "points"]
    return any(word.lower() in query.lower() for word in cashback_keywords)

def ask_ai(query):
    if is_query_about_cashback(query):
        # Extract the required information from the query or ask the user for more information if needed
        segment = input("Enter your card segment: ")
        total_spent = float(input("Enter your total spent amount: "))
        international_transactions = float(input("Enter your international transactions amount: "))
        local_transactions = float(input("Enter your local transactions amount: "))

        cashback = cashback_calculator(segment, total_spent, international_transactions, local_transactions)
        return f"The cashback amount for your card is: {cashback:.2f}"
    else:
        index = GPTSimpleVectorIndex.load_from_disk('index.json')
        response = index.query(query, response_mode="compact")
        return response.response
iface = gr.Interface(fn=ask_ai, inputs=["text", "text", "number", "number", "number"], outputs="text", title="The following is a conversation with a human called Shegardi. Shegardi is helpful, precise, truthful, and very friendly.  Also, Shegardi is an employee of Warba Bank, located in Kuwait. Shegardi will only use the information provided to him.",
                     description="Enter a question and get an answer from Shegardi.")
iface.launch()