Sharath S Hebbar SharathHebbar

ValueError: Your setup doesn't support bf16/gpu. You need torch>=1.10, using Ampere GPU with cuda>=11.0

Change bf16 to fp16 for non Ampere GPUs

! pip show datasets

Name: datasets
Version: 2.16.1
Summary: HuggingFace community-driven open-source library of datasets
Home-page: https://github.com/huggingface/datasets
Author: HuggingFace Inc.

!pip install git+https://github.com/huggingface/transformers.git -q -U # transformers version:  4.37.0
!pip install git+https://github.com/huggingface/accelerate.git -q -U # accelerate version:  0.27.0
!pip install bitsandbytes # bitsandbytes version:  0.42.0
!pip install git+https://github.com/huggingface/peft.git -q -U # peft version: 0.7.2

R-3 F-3 M-3

Segments	Scores	Descriptions
Best Customer

	from pyspark.sql import SparkSession

	# Initialize Spark Session
	spark = SparkSession.builder \
	.appName("LocalSparkSQL") \
	.config("spark.sql.shuffle.partitions", "4") \
	.getOrCreate()


	# Read the CSV (with the first row as data)
	df = spark.read.format("csv").option("header", "false").load("/path/to/csvfile")

	# Extract the first row as the header
	new_header = df.first()

	# Create a new DataFrame without the first row
	df_without_first_row = df.filter(df["_c0"] != new_header["_c0"])

	import os
	from langchain.document_loaders import PyPDFLoader
	from langchain.text_splitter import RecursiveCharacterTextSplitter
	from langchain.embeddings import AzureOpenAIEmbeddings
	from langchain.vectorstores import Chroma
	from langchain.chains import RetrievalQA
	from langchain.llms import AzureOpenAI

	# Step 1: Load PDF Document
	def load_pdf(pdf_path):

	import os
	from langchain.document_loaders import PyPDFLoader
	from langchain.text_splitter import RecursiveCharacterTextSplitter
	from langchain.embeddings import AzureOpenAIEmbeddings
	from langchain.vectorstores import Chroma

	# Step 1: Load PDF Document
	def load_pdf(pdf_path):
	loader = PyPDFLoader(pdf_path)
	documents = loader.load()


	from flask import Flask, render_template, request, redirect, url_for
	from langchain import LLMChain
	from langchain.llms import OpenAI
	from langchain.prompts import PromptTemplate
	import spacy

	app = Flask(__name__)

	# Load spaCy's English model for entity recognition


	<!DOCTYPE html>
	<html lang="en">
	<head>
	<meta charset="UTF-8">
	<meta name="viewport" content="width=device-width, initial-scale=1.0">
	<title>Chat, Summarize & Translate Bot</title>
	<script src="https://cdn.tailwindcss.com"></script>
	<style>
	.entity {