ahmedbesbes

import streamlit as st

tabs = st.tabs(["metrics", "plots", "reports"])

tab_metrics = tabs[0]

with tab_metrics:
    st.metric("Precision", 0.85, delta=0.2)
    st.metric("Recall", 0.60, delta=-0.1)

ahmedbesbes

import streamlit as st

tabs = st.tabs(["metrics", "plots", "reports"])

ahmedbesbes

import os
import tweepy
from dotenv import load_dotenv

load_dotenv()

bearer_token = os.environ["BEARER_TOKEN"]

class MyStreamer(tweepy.StreamingClient):
    def on_tweet(self, tweet):

ahmedbesbes

extracted_pages = []

for page in tweepy.Cursor(api.search_tweets, 
                            "Ukraine", 
                            lang="en",
                            count=50).pages(5):
    extracted_pages.append(page)
    
    
extracted_tweets_from_pages = []

ahmedbesbes

import os
import tweepy
from dotenv import load_dotenv

consumer_key = os.environ["API_KEY"]
consumer_secret = os.environ["API_KEY_SECRET"]
access_token = os.environ["ACCESS_TOKEN"]
access_token_secret = os.environ["ACCESS_TOKEN_SECRET"]

auth = tweepy.OAuth1UserHandler(

ahmedbesbes

import os
import tweepy
from dotenv import load_dotenv

consumer_key = os.environ["API_KEY"]
consumer_secret = os.environ["API_KEY_SECRET"]
access_token = os.environ["ACCESS_TOKEN"]
access_token_secret = os.environ["ACCESS_TOKEN_SECRET"]

auth = tweepy.OAuth1UserHandler(

ahmedbesbes

import os
import tweepy
from dotenv import load_dotenv

consumer_key = os.environ["API_KEY"]
consumer_secret = os.environ["API_KEY_SECRET"]
access_token = os.environ["ACCESS_TOKEN"]
access_token_secret = os.environ["ACCESS_TOKEN_SECRET"]

auth = tweepy.OAuth1UserHandler(

ahmedbesbes

from flask import Flask

app = Flask(__name__)


@app.route("/")
def hello_world():
    return "<p>Hello, World!</p>"

ahmedbesbes

FROM python:3.7-slim-buster

WORKDIR /usr/src/app

COPY requirements.txt ./

RUN pip install --no-cache-dir -r requirements.txt

COPY . .

ahmedbesbes

import random
import numpy as np
import torch

random.seed(seed)
torch.manual_seed(0)
np.random.seed(0)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False