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print("No coffee ! Only Data ! ")

Sourav Kumar souravs17031999

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print("No coffee ! Only Data ! ")
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@souravs17031999
souravs17031999 / user.py
Created August 19, 2021 16:43
from flask import Flask, json, jsonify, request, Response, Blueprint
app = Flask(__name__)
user = Blueprint('user', __name__)
userInfoMap = {"1": {"firstname": "sourav", "lastname": "kumar"},
"2": {"firstname": "elon", "lastname": "musk"},
"3": {"firstname": "jenny", "lastname": "cooper"}}
@souravs17031999
souravs17031999 / basic-flask-app.py
Created August 19, 2021 15:09
from flask import Flask, jsonify, request
app = Flask(__name__)
userInfoMap = {"1": {"firstname": "sourav", "lastname": "kumar"},
"2": {"firstname": "elon", "lastname": "musk"},
"3": {"firstname": "jenny", "lastname": "cooper"}}
@app.route('/user/fetch-info')
@souravs17031999
souravs17031999 / events.js
Created July 5, 2021 06:16
<html>
<head>
<title>
EVENTS PROPOGATION
</title>
<style>
#first{
height: 150px;
width: 150px;
background-color: red;
@souravs17031999
souravs17031999 / arguments.txt
Created March 21, 2020 07:10
positional arguments:
| arguments | details |
| ------------- | ------------- |
| dir_path | Output working directory |
| query | Query string to search for |
| API | API KEY FOR GOOGLE CUSTOM SEARCH API |
| CX | CUSTOM SEARCH ENGINE ID |
| imgSize | Size of image [icon/small/medium/large/xlarge/xxlarge/huge] |
| imgType | Type of image[clipart/face/lineart/stock/photo/animated] |
| imgColorType | Color of image[color/gray/mono] |
@souravs17031999
souravs17031999 / images_download.py
Last active March 21, 2020 07:53
# function for downloading images using Google API
def download_images(dir_path, query, API, CX, imgSize, imgType, imgColorType, counter=0):
# check if given directory already exists
if not os.path.isdir(dir_path):
print("Image scraping using Google Custom Image Search Engine API....")
# lists of links / urls to be stored
urls = []
for i in range(0, 100, 10):
# Making GET requests to google API
source = requests.get(f'https://www.googleapis.com/customsearch/v1?key={API}&cx={CX}&q={query}&searchType=image&imgSize={imgSize}&imgType={imgType}&imgColorType={imgColorType}&num=10&start={i}').text
@souravs17031999
souravs17031999 / privatedata.py
Created August 24, 2019 05:50
# Convert to integers and privately share the dataset
private_test_loader = []
for data, target in test_loader:
private_test_loader.append((
data.fix_prec().share(alice, bob, crypto_provider=crypto_provider),
target.fix_prec().share(alice, bob, crypto_provider=crypto_provider)
))
@souravs17031999
souravs17031999 / training.py
Created August 24, 2019 05:27
def train_and_test(e):
epochs = e
train_losses , test_losses = [] , []
valid_loss_min = np.Inf
model.train()
print("training started...")
for epoch in range(epochs):
running_loss = 0
batch = 0
#scheduler.step()
@souravs17031999
souravs17031999 / optimizer.py
Created August 24, 2019 05:23
model = Net().to(device)
optimizer = optim.SGD(model.parameters(), lr=args.lr)
@souravs17031999
souravs17031999 / model1.py
Last active August 24, 2019 05:20
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.fc1 = nn.Linear(784, 500)
self.fc2 = nn.Linear(500, 10)
def forward(self, x):
x = x.view(-1, 784)
x = self.fc1(x)
x = F.relu(x)
x = self.fc2(x)
@souravs17031999
souravs17031999 / visualize3.py
Created August 24, 2019 05:16
# plotting the images of loaded batch with given fig size and frame data
import torchvision
import matplotlib.pyplot as plt
import numpy as np
grid = torchvision.utils.make_grid(images.get(), nrow = 20, padding = 2)
plt.figure(figsize = (20, 20))
plt.imshow(np.transpose(grid, (1, 2, 0)))
print('labels:', labels.get())