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

pip install telethon

# pip install google-genai

from google import genai

client = genai.Client(api_key="")

javob = client.models.generate_content(
    model="gemini-2.5-flash",
    contents="Salom sen kimsan"
)

print(javob.text)

pip install google-genai

pip install osonbot

from aiogram import Bot, Dispatcher, types
from aiogram.filters import CommandStart
import asyncio

dp = Dispatcher()

@dp.message(CommandStart())
async def start(message: types.Message):
     await message.answer(f"Salom {message.from_user.first_name}")

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

async def main():
     bot = Bot("token")
     await dp.start_polling(bot)

asyncio.run(main())

from telebot import Bot

bot = Bot("token")

@bot.message_handler(commands=['start'])
def start(message):
     bot.send_message(message.chat.id, f"Salom {message.from_user.first_name}")

@bot.message_handler()
def echoo(message):
     bot.send_message(message.chat.id, message.text)

bot.infinity_polling()

from osonbot import Bot

bot = Bot("token")

bot.when("/start", "Salom {first_name}")
bot.when("*", "{message_text}")

bot.run()

pip install jsonbot

JsonBot("token", {
    "/start", {"response": "Salom *{first_name}*", "parse_mode": "MarkdownV2"},
    "/help": {"response": "Sizga qanday yordam beraolaman"},
    "*": {"{message_text}"}
}).run()