deshraj

#!/usr/bin/env python3
"""
# 🧠 Memory Agent with Session-Based Short-Term Memory

A demonstration of using Strands Agents' memory capabilities to store and retrieve information with session-based short-term memory.

## What This Example Shows

This example demonstrates:
- Creating an agent with memory capabilities

deshraj

import logging
from dotenv import load_dotenv

from strands import Agent
from strands_tools import mem0_memory, use_llm

logger = logging.getLogger(__name__)

# Load environment variables from .env file if it exists
load_dotenv()

deshraj

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https://www.youtube.com/watch?v=8VfLJ9DBHAA
https://www.youtube.com/watch?v=rDZfeYuZatY
https://www.youtube.com/watch?v=O0kpIEZ1Mdw
https://www.youtube.com/watch?v=SZx-uNEYu3M

deshraj

https://isha.sadhguru.org/in/en/wisdom/article/divorce-remarriage
https://isha.sadhguru.org/in/en/wisdom/article/parenting-for-teenager
https://isha.sadhguru.org/in/en/wisdom/article/find-fulfillment-in-life
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https://isha.sadhguru.org/in/en/wisdom/article/children-connect-nature
https://isha.sadhguru.org/in/en/wisdom/article/family-matters
https://isha.sadhguru.org/in/en/wisdom/article/how-should-parents-influence-lives
https://isha.sadhguru.org/in/en/wisdom/article/why-youth-turning-to-alcohol-drugs

deshraj

import functools

def my_collate(batch, use_shared_memory=False):
    r"""Puts each data field into a tensor with outer dimension batch size"""

    error_msg = "batch must contain tensors, numbers, dicts or lists; found {}"
    elem_type = type(batch[0])
    if isinstance(batch[0], torch.Tensor):
        out = None
        if use_shared_memory:

deshraj

from common.submit import save_reconstructions

# Run your model on the test data
reconstructions = run_model(my_model, test_data_loader)
# Save the reconstructions to disk under the recons_dir directory
save_reconstructions(reconstructions, recons_dir)

deshraj

from common import transforms, mri_data as data

# Define the data transform class
class DataTransform:
    def __call__(self, kspace, seed, target):
        # Preprocess the data here
        masked_kspace = transforms.apply_mask(kspace)
        image = transforms.ifft2(masked_kspace)
        cropped_image = transforms.center_crop(transforms.complex_abs(image))
        return cropped_image, target

deshraj

from django.contrib.auth.models import User
from django.contrib.postgres.fields import JSONField
from django.db import models


class TimeStampedModel(models.Model):
     created_on = models.DateTimeField(auto_now_add=True)

     class Meta:
         abstract = True

deshraj

#!/usr/bin/python
#based on the ideas from http://synack.me/blog/implementing-http-live-streaming
# Run this script and then launch the following pipeline:
# gst-launch videotestsrc pattern=ball ! video/x-raw-rgb, framerate=15/1, width=640, height=480 !  jpegenc ! multipartmux boundary=spionisto ! tcpclientsink port=9999

from Queue import Queue
from threading import Thread
from socket import socket
from select import select
from wsgiref.simple_server import WSGIServer, make_server, WSGIRequestHandler

deshraj

NPTEL Setup on Server

Prerequisites for installing NPTEL on a server:

1. Ubuntu 14.10 OS Environment
2. Apache or any other web server installed on that system

Steps for setting up on the server:

1. First we need to make sure that we are working with the root user otherwise there are a lot of permission issues that arises when we start working.