AI Anytime AIAnytime

Running the CLI

npx https://github.com/google-gemini/gemini-cli

npm install -g @google/gemini-cli

	from dotenv import load_dotenv
	from openai import OpenAI
	from traceloop.sdk import Traceloop
	from traceloop.sdk.decorators import workflow, task
	import os

	load_dotenv()

	OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
	TRACeloop_API_KEY = os.getenv("TRACeloop_API_KEY")

	import re
	from langchain.docstore.document import Document
	from langchain.text_splitter import RecursiveCharacterTextSplitter
	from langchain.embeddings.openai import OpenAIEmbeddings
	from langchain.vectorstores import Qdrant
	from qdrant_client import QdrantClient

	# --- Step 1: Extract text from the document ---
	def extract_text(file_path: str) -> str:
	"""

	FROM ./Llama-3-ELYZA-JP-8B-q4_k_m.gguf
	TEMPLATE """{{ if .System }}<\|start_header_id\|>system<\|end_header_id\|>

	{{ .System }}<\|eot_id\|>{{ end }}{{ if .Prompt }}<\|start_header_id\|>user<\|end_header_id\|>

	{{ .Prompt }}<\|eot_id\|>{{ end }}<\|start_header_id\|>assistant<\|end_header_id\|>

	{{ .Response }}<\|eot_id\|>"""
	PARAMETER stop "<\|start_header_id\|>"
	PARAMETER stop "<\|end_header_id\|>"

	from fastapi import FastAPI, HTTPException
	from pydantic import BaseModel
	from openai import OpenAI
	from dotenv import load_dotenv
	import os

	load_dotenv()

	app = FastAPI()
	client = OpenAI()

	import requests

	# Base endpoint
	base_url = "https://r.jina.ai/"

	# Input URL to be appended
	input_url = "https://www.stateof.ai/"

	# Full URL with the input URL appended after a plus (+) sign
	full_url = base_url + input_url

	import streamlit as st

	# App title
	st.title('EcoOptimizer Carbon Footprint Calculator')

	# Introduction
	st.write('''
	This tool calculates the Software Carbon Intensity (SCI) for EcoOptimizer,
	an AI-powered energy management system for commercial buildings by EcoTech Solutions.
	''')

	training_arguments = TrainingArguments(
	output_dir="./results",
	per_device_train_batch_size=4,
	per_device_eval_batch_size=4,
	gradient_accumulation_steps=2,
	optim="adamw_8bit",
	logging_steps=50,
	learning_rate=1e-4,
	evaluation_strategy="steps",
	do_eval=True,

	## With Evaluation Harness
	!pip install git+https://github.com/EleutherAI/lm-evaluation-harness.git
	!pip install bitsandbytes
	!pip install --upgrade transformers
	!pip install auto-gptq optimum autoawq

	!lm_eval --model hf --model_args pretrained=google/gemma-7b --tasks winogrande,hellaswag,arc_challenge --device cuda:0 --num_fewshot 1 --batch_size 8 --output_path ./eval_harness/gemma-7b

	!lm_eval --model hf --model_args pretrained=google/gemma-7b --tasks winogrande,hellaswag,arc_challenge --device cuda:0 --num_fewshot 5 --batch_size 8 --output_path ./eval_harness/gemma-7b-5shot

	import os
	#set you runpod key as a environment variable
	os.environ['RUNPOD_API_KEY'] = "your_runpod_api_key"

	import runpod
	from IPython.display import display, Markdown

	runpod.api_key = os.getenv("RUNPOD_API_KEY", "your_runpod_api_key")

	if runpod.api_key == "your_runpod_api_key":