pip install pytubefix

pip install rembg
pip install onnxruntime

pip install pillow stepic

import cv2
import mediapipe as mp 
import numpy as np
import screen_brightness_control as sbc

mp_hands = mp.solutions.hands
mp_drawing = mp.solutions.drawing_utils

cap = cv2.VideoCapture(0)

with mp_hands.Hands(min_detection_confidence=0.7, min_tracking_confidence=0.7) as hands:
    while cap.isOpened():
        ret, frame = cap.read()
        if not ret:
            break
        
        frame = cv2.flip(frame, 1)
        rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
        results = hands.process(rgb_frame)
        
        if results.multi_hand_landmarks:
            for hand_landmarks in results.multi_hand_landmarks:
                mp_drawing.draw_landmarks(frame, hand_landmarks, mp_hands.HAND_CONNECTIONS)
                
                index_tip = hand_landmarks.landmark[mp_hands.HandLandmark.INDEX_FINGER_TIP]
                thumb_tip = hand_landmarks.landmark[mp_hands.HandLandmark.THUMB_TIP]
                
                h, w, _ = frame.shape
                ix, iy = int(index_tip.x * w), int(index_tip.y * h)
                tx, ty = int(thumb_tip.x * w), int(thumb_tip.y * h)
                
                distance = np.sqrt((ix - tx)**2 + (iy - ty)**2)

                brightness = int(np.interp(distance, [20, 200], [0, 100]))
                sbc.set_brightness(brightness)

                cv2.putText(frame, f'Yorug\'lik: {brightness}%', (10, 50),
                            cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
                cv2.line(frame, (ix, iy), (tx, ty), (0, 255, 0), 2)
                cv2.circle(frame, (ix, iy), 10, (0, 255, 0), cv2.FILLED)
                cv2.circle(frame, (tx, ty), 10, (0, 255, 0), cv2.FILLED)

        cv2.imshow('Yoruglikni barmoqlar bilan boshqarish', frame)
        
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break

cap.release()
cv2.destroyAllWindows()

pip install opencv-python

import cv2
import numpy as np
from PIL import Image

cap = cv2.VideoCapture(0)

while True:
    ret, frame = cap.read()
    if not ret:
        break

    hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)

    lower_blue = np.array([100, 150, 50])
    upper_blue = np.array([130, 255, 255])

    lower_green = np.array([40, 70, 70])
    upper_green = np.array([80, 255, 255])

    mask_blue = cv2.inRange(hsv, lower_blue, upper_blue)
    mask_green = cv2.inRange(hsv, lower_green, upper_green)

    kernel = np.ones((5, 5), np.uint8)
    mask_blue = cv2.erode(mask_blue, kernel, iterations=1)
    mask_blue = cv2.dilate(mask_blue, kernel, iterations=2)

    mask_green = cv2.erode(mask_green, kernel, iterations=1)
    mask_green = cv2.dilate(mask_green, kernel, iterations=2)

    bbox_blue = Image.fromarray(mask_blue).getbbox()
    bbox_green = Image.fromarray(mask_green).getbbox()

    # Draw rectangles
    if bbox_blue:
        x1, y1, x2, y2 = bbox_blue
        frame = cv2.rectangle(frame, (x1, y1), (x2, y2), (255, 0, 0), 2)

    if bbox_green:
        x1, y1, x2, y2 = bbox_green
        frame = cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)

    cv2.imshow("Color Detection (Blue & Green)", frame)

    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

pip install opencv-python

from aiogram import Bot, Dispatcher, types
from aiogram.types import Message
from aiogram.filters import Command
import asyncio
import os

BOT_TOKEN = "bot_token"

dp = Dispatcher()

@dp.message(Command(commands=["start"]))
async def start(message: Message):
    await message.answer("👋 Hello! I am an Echo Bot. Send me any message and I will repeat it!")

@dp.message()
async def echo(message: Message):
    await message.answer(message.text)

async def main():
    try:
        bot = Bot(token=BOT_TOKEN)
        await dp.start_polling(bot)
    finally:
        await bot.session.close()

if __name__ == "__main__":
    asyncio.run(main())

pip install aiogram

import instaloader

def InstagramDownloader(shortcode: str):
    L = instaloader.Instaloader()
    post = instaloader.Post.from_shortcode(L.context, shortcode)
    return {'title': post.title, "caption": post.caption, "rasm": post.url, "video": post.video_url, "turi": post.typename}

# https://www.instagram.com/reel/DNehZkaR6LI/?utm_source=ig_web_copy_link&igsh=MzRlODBiNWFlZA== # shortcode (id): DNehZkaR6LI
print(InstagramDownloader("DNehZkaR6LI")) 

pip install instaloader

from langchain_huggingface import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
from langchain.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.chains.conversational_retrieval.base import ConversationalRetrievalChain
from langchain.memory import ConversationBufferMemory
from langchain_google_genai import ChatGoogleGenerativeAI
import os

os.environ["HUGGINGFACEHUB_API_TOKEN"] = "HUGGINGFACE_API_TOKEN"
os.environ["GOOGLE_API_KEY"] = "GOOGLE_API_KEY"

loader = PyPDFLoader("fayl.pdf")
documents = loader.load()

splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
docs = splitter.split_documents(documents)

embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")

vectorstore = FAISS.from_documents(docs, embeddings)

llm = ChatGoogleGenerativeAI(model="gemini-2.5-pro", temperature=0.3)

memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)

qa = ConversationalRetrievalChain.from_llm(
    llm=llm,
    retriever=vectorstore.as_retriever(),
    memory=memory
)

print("📚 PDF Chatbot ready! Type 'exit' to quit.\n")
while True:
    query = input("You: ")
    if query.lower() in ["exit", "quit"]:
        break
    result = qa({"question": query})
    print("Bot:", result["answer"])

pip install -r requirements.txt

import streamlit as st

st.write("Hello World")

pip install streamlit

streamlit run [python_faylingizni_nomi].py