QA-Bot

pip install transformers torch streamlit

🤖 1. Transformers

Library: Part of Hugging Face.

Used for: Natural Language Processing (NLP) tasks like:

• Text classification
• Question answering
• Translation
• Text generation (like ChatGPT!)

from transformers import pipeline

qa = pipeline("question-answering")
qa(question="What is the capital of France?", context="France's capital is Paris.")
# Output: {'answer': 'Paris'}

🔥 2. Torch (PyTorch)

Library: Developed by Facebook AI.

Used for: Deep learning and building neural networks.

Alternative to: TensorFlow.

Works behind the scenes in many models from Hugging Face.

import torch

x = torch.tensor([1.0, 2.0])
y = x * 2
print(y)  # tensor([2., 4.])

🌐 3. Streamlit

Library: For building interactive web apps in Python.

Great for: AI demos, dashboards, data science tools.

import streamlit as st

st.title("Hello Streamlit!")
name = st.text_input("Enter your name")
st.write("Hello", name)

🧠 Hugging Face

Hugging Face is an AI company and open-source platform that focuses on making machine learning models easy to use, especially for natural language processing (NLP).

Think of Hugging Face as:

"GitHub + App Store for AI Models"

It offers:

• Pre-trained models (like ChatGPT, BERT, etc.)
• Datasets
• APIs
• Training tools
• Model hosting and sharing

🔧 Main Tools from Hugging Face

1. 🤗 transformers library

Provides thousands of pre-trained models for:

• Text classification
• Translation
• Question answering
• Text generation
• Works with PyTorch and TensorFlow

from transformers import pipeline
qa = pipeline("question-answering")
qa(question="Where is Hugging Face based?", context="Hugging Face is based in New York.")
# Output: {'answer': 'New York'}

2. 🤗 datasets library

Access to huge open-source datasets used for training and evaluation.

3. 🤗 Model Hub

Website: https://huggingface.co/models

You can search, download, and upload models easily.

4. 🤗 Spaces

Host your ML apps using Streamlit, Gradio, or HTML for free.

✅ Why Use Hugging Face? Saves time: No need to train models from scratch

Easy to integrate: Use models with just a few lines of code

Community-driven: Thousands of models contributed by users and researchers