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	import streamlit as st
	import torch
	import os
	import sys
	import time
	import json
	from typing import List
	import datasets
	import csv
	from transformers import LlamaTokenizer, LlamaForCausalLM
	import tqdm


	base_model_name = "EthioNLP/Amharic-llama-base-model"
	adapters_name = 'EthioNLP/Amharic-LLAMA-all-data'




	BASE_PROMPT = """Below is an interaction between a human and an AI fluent in English and Amharic, providing reliable and informative answers. The AI is supposed to answer test questions from the human with short responses saying just the answer and nothing else.

	Human: {instruction}

	Assistant [Amharic] : """



	from peft import PeftModel
	from transformers import LlamaForCausalLM, LlamaConfig

	# Function to load the main model for text generation
	def load_model(model_name, quantization):
	model = LlamaForCausalLM.from_pretrained(
	model_name,
	return_dict=True,
	load_in_8bit=quantization,
	device_map='cpu',
	low_cpu_mem_usage=True,
	)
	return model


	# Function to load the PeftModel for performance optimization
	def load_peft_model(model, peft_model):
	peft_model = PeftModel.from_pretrained(model, peft_model,offload_folder='./')
	return peft_model

	# Loading the model from config to load FSDP checkpoints into that
	def load_llama_from_config(config_path):
	model_config = LlamaConfig.from_pretrained(config_path)
	model = LlamaForCausalLM(config=model_config)
	return model



	def main(
	model,
	tokenizer,
	datasource, # List of data sources to use, no default value
	csv_file_path, # Path to the CSV file to save responses, no default value
	max_new_tokens=100, # The maximum numbers of tokens to generate
	seed=42, # seed value for reproducibility
	do_sample=True, # Whether or not to use sampling; use greedy decoding otherwise.
	min_length=None, # The minimum length of the sequence to be generated
	use_cache=True, # [optional] Whether or not the model should use the past last key/values attentions
	top_p=1.0, # [optional] If set to float < 1, only the smallest set of most probable tokens with probabilities that add up to top_p or higher are kept for generation.
	temperature=1.0, # [optional] The value used to modulate the next token probabilities.
	top_k=5, # [optional] The number of highest probability vocabulary tokens to keep for top-k-filtering.
	repetition_penalty=5.0, # The parameter for repetition penalty. 1.0 means no penalty.
	length_penalty=1, # [optional] Exponential penalty to the length used with beam-based generation.
	enable_azure_content_safety=False, # Enable safety check with Azure content safety API
	enable_sensitive_topics=False, # Enable check for sensitive topics using AuditNLG APIs
	enable_saleforce_content_safety=False, # Enable safety check with Salesforce safety T5
	**kwargs # Additional arguments for the model.generate function
	):
	# Note: Ensure that the appropriate tokenizer is used for the language.
	print("*** Ensure that you have replaced the default tokenizer with the appropriate one for your use case.")

	model.eval()

	# Load the dataset from Hugging Face
	dataset = hf_dataset['test']

	# Prepare the CSV file for saving responses
	with open(csv_file_path, mode='w', newline='', encoding='utf-8') as file:
	writer = csv.writer(file)
	writer.writerow(['Instruction', 'Input Text', 'Datasource','response', 'gold_label']) # Column headers

	for item in tqdm.tqdm(dataset): # Change to the desired split if necessary
	instruction = item['instruction'] # Extracting the instruction
	input_text = item['input'] # Extracting the input text
	datasource = item['datasource']
	gold_label=item['output']

	# Combine instruction and input_text for the prompt
	user_prompt = BASE_PROMPT.format(instruction=f"{instruction}\n{input_text}")

	batch = tokenizer(user_prompt, return_tensors="pt")
	batch = {k: v.to(model.device) for k, v in batch.items()} # Ensure tensors are on the same device as the model

	start = time.perf_counter()

	with torch.no_grad():
	outputs = model.generate(
	**batch,
	max_new_tokens=max_new_tokens,
	do_sample=do_sample,
	top_p=top_p,
	temperature=temperature,
	min_length=min_length,
	use_cache=use_cache,
	top_k=top_k,
	repetition_penalty=repetition_penalty,
	length_penalty=length_penalty,
	**kwargs)

	e2e_inference_time = (time.perf_counter() - start) * 1000
	# print(f"Inference time: {e2e_inference_time} ms")

	output_text = tokenizer.decode(outputs[0], skip_special_tokens=True)[len(user_prompt):]
	# print("Model Output: {}".format(output_text))

	# Write the instruction, input text, and output to the CSV file
	writer.writerow([instruction, input_text,datasource, output_text, gold_label])
	torch.cuda.empty_cache()

	# Example of how to use the function




	model = load_model(base_model_name, quantization=True)

	tokenizer = LlamaTokenizer.from_pretrained(adapters_name)
	embedding_size = model.get_input_embeddings().weight.shape[0]

	if len(tokenizer) != embedding_size:
	print("resize the embedding size by the size of the tokenizer")
	model.resize_token_embeddings(len(tokenizer))


	# Load adapter model
	model.load_adapter(adapters_name)

	BASE_PROMPT.format(instruction=f"{instruction}\n{input_text}")




	max_new_tokens=100 # The maximum numbers of tokens to generate
	seed=42 # seed value for reproducibility
	do_sample=True # Whether or not to use sampling; use greedy decoding otherwise.
	min_length=None # The minimum length of the sequence to be generated
	use_cache=True # [optional] Whether or not the model should use the past last key/values attentions
	top_p=1.0 # [optional] If set to float < 1, only the smallest set of most probable tokens with probabilities that add up to top_p or higher are kept for generation.
	temperature=1.0 # [optional] The value used to modulate the next token probabilities.
	top_k=5 # [optional] The number of highest probability vocabulary tokens to keep for top-k-filtering.
	repetition_penalty=5.0 # The parameter for repetition penalty. 1.0 means no penalty.
	length_penalty=1 # [optional] Exponential penalty to the length used with beam-based generation.
	enable_azure_content_safety=False # Enable safety check with Azure content safety API
	enable_sensitive_topics=False # Enable check for sensitive topics using AuditNLG APIs
	enable_saleforce_content_safety=False


	def predict(instruction,input_text=" "):
	user_prompt = BASE_PROMPT.format(instruction=f"{instruction}\n{input_text}")

	batch = tokenizer(user_prompt, return_tensors="pt")
	batch = {k: v.to(model.device) for k, v in batch.items()} # Ensure tensors are on the same device as the model

	start = time.perf_counter()

	# print(batch)

	with torch.no_grad():
	outputs = model.generate(
	**batch,
	max_new_tokens=max_new_tokens,
	do_sample=do_sample,
	top_p=top_p,
	temperature=temperature,
	min_length=min_length,
	use_cache=use_cache,
	top_k=top_k,
	repetition_penalty=repetition_penalty,
	length_penalty=length_penalty)

	e2e_inference_time = (time.perf_counter() - start) * 1000
	# print(f"Inference time: {e2e_inference_time} ms")

	output_text = tokenizer.decode(outputs[0], skip_special_tokens=True)[len(user_prompt):]

	return output_text




	st.title('LLM Interaction Interface')

	user_input = st.text_input("Ask a question:")

	if user_input:
	# This function is supposed to send the question to the LLM and get the response
	response = predict(user_input)
	st.text_area("Response:", value=response, height=300, max_chars=None, help=None)
	# st.json({'value':response},expanded=False)