Generative AI: Practical LLM, LangChain, Hugging Face

Learn to set up a Windows coding environment for long chain using conda and Anaconda, avoid pip due to numpy issues, and install Python 3.12 via the Anaconda installer.

Create a dedicated conda environment named lang chain in Anaconda Navigator and use the Anaconda prompt to install LangChain and its packages, selecting a compatible Python version.

Create a Hugging Face account and explore its library of pre-trained models for natural language processing, computer vision, and multimodal tasks, then combine them with LangChain to build AI applications.

Create an API access token for Hugging Face from your profile's access tokens, copy and securely save it, then use it with Python and link chain to access models.

Create a text summarizer app using LangChain and Hugging Face in Python, detailing user input (article or pdf), a prompt template, a Hugging Face pipeline, and the resulting summary.

Create your first LangChain app using the prompt template with a Hugging Face Bert model fine-tuned on CNN Daily Mail for text summarization.

Set up a Python environment and notebooks to import LangChain prompt templates and Hugging Face pipelines. Prepare to load the Bart model and implement the summarization function next.

Initialize a Hugging Face BART model to power a text summarizer class, load the model by ID, and run a summarization task using a configurable pipeline.

Learn to declare BART pipeline kwargs by setting max and min summary length, understand do_sample for greedy decoding versus sampling, and prepare a Hugging Face summarizer with a prompt template.

Create a summarization prompt template by defining a summarizer variable to store the template. Pass the user input inside the document placeholder and instruct the model to summarize the document.

Initialize the prompt template with the document input in a LangChain component, passing input variables and template to drive output from a Bert large CNN model.

Define a summarize text function that formats the prompt with the article text, uses the hugging face model pipeline to generate and store the summary, and test the function.

Test the model with sample text by feeding an article into a bert-large cnn model summarizer, using a Summarizer class and a prompt template to produce a concise summary.

Develop a pdf text loading function for the text summarizer gui that opens a pdf, iterates pages, extracts and accumulates content, and returns the text for summarization.

Build and run a text summarizer gui app that loads pdf text, supports input, and outputs summaries using Hugging Face and LangChain models.

Build an interactive assistant app using LangChain and Hugging Face, connect via the Hugging Face API key, summarize text, answer questions, and chain prompt templates for tasks.

Explore the assistant project structure, including the main entry point, hugging face model logic, query and text summarization handlers, and optional UI code using LangChain chains.

Explain how the main function wires libraries to create a gui, initialize the assistant app with a route window, and run the main loop with query and summariser handlers.

Define a query handler for the assistant app, initialize with an interactive assistant, and use a prompt template to generate responses from a language model, with error handling.

Initialize the summary handler with the interactive assistant instance, then call summarize text to generate the model output from input text using the prompt template.

Import torch and transformers, authenticate the hugging face token, check gpu availability, and configure models to load a bert large cnn summarization model or an interaction model.

Select plan t5 large model for interaction and Bert large for summary, configure GPU if available, and handle initialization errors with try-except during model setup.

Create a gui for an assistant app by configuring window geometry, title, and colors, with input fields, buttons, and a summary and query workflow, plus api key handling.

Test the LangChain assistant app by entering the Hugging Face API token, initializing assistants, running queries like capitals, and summarizing article text to demonstrate GUI, model flexibility, and future enhancements.

Develop a text-to-image app using a small stable diffusion pre-trained model, ideal for teaching due to its light footprint, with options for medium or larger models.

Experiment with prompts and resolution settings to generate text-to-image outputs using a hugging face model, exploring 2k or 4k/8k outputs, file naming, and model selection.

Develop a text-to-video app using a Hugging Face model, specifically the animate div lightning model, to generate smooth, high-resolution animations from text.

Import essential libraries from torch, diffusers, and Hugging Face Hub, load safe tensors, configure the Euler discrete scheduler, export GIF animations, test installation, and prepare for text-to-video generation.

Declare cpu as device and use float32; you may switch to float16 or gpu to save memory. Set step size for inference steps and specify motion adopter repository and checkpoint.

Define the checkpoint and generate its file name according to the step size by inspecting the model's files and versions, then implement dynamic naming in code.

Select the base model to generate animations, choosing the epic realism option from the recommended models. Initialize the motion adapter to apply motion to the generated frames.

Initialize the motion adapter, load pre-trained weights from a checkpoint via the state dictionary, and move the adapter to the chosen device to apply motion to generated frames.

Load the animate diffusion pipeline to integrate the motion adopter and base model for animation generation, configure the linear beta scheduler, and apply the discrete scheduler on CPU.

Generate an animation from a text prompt with a pipe function, set the guidance scale, export the first frame as a gif, and preview the next video.

Run the code to generate a gif output, adjust the guidance scale to control text-to-image strength, and observe a finished video output that improves with hardware or cloud resources.