Create your own Local Chatgpt for FREE, Full Guide: PDF, Image, & Audiochat (Langchain, Streamlit)

The paragraph introduces the concept of creating a multimodel local chat application that integrates voice, image, and PDF handling capabilities. The goal is to use local models for these functionalities without relying on external APIs, allowing for offline use. The app will utilize Open AI's Whisper model for audio data and other quantized models for image and PDF processing. The video will guide the viewers through the development process, including setting up the environment, installing necessary packages, and handling multiple chat sessions with local models.

This section focuses on setting up the development environment using Streamlit, a Python library for creating web applications. It explains how to create a basic chat interface using Streamlit's widgets, such as text input and buttons. The paragraph discusses the challenges of handling user input and session state within Streamlit's reactive programming model, where changes in one widget can trigger the rerunning of the entire script. It introduces the concept of using session state to maintain variables across script reruns and provides a solution to handle user input and messages effectively.

The paragraph delves into the integration of local models for chat functionality. It discusses the process of selecting appropriate quantized models from Hugging Face's model hub and loading them into the application. The video outlines the creation of a chat memory function and the use of session state to store and retrieve chat history. It also introduces the concept of creating a chat chain class to manage the conversation flow between the user and the AI model.

This section focuses on managing chat history and sessions within the application. It explains how to save chat histories to files and load them back into the application. The paragraph introduces the creation of a sidebar for selecting chat sessions and discusses the use of session state to track the current chat session. It also covers the implementation of functions to handle the saving and loading of chat histories, ensuring that users can switch between different chat sessions and maintain a record of their conversations.

The paragraph discusses the implementation of audio handling in the chat application. It introduces the use of Open AI's Whisper model for converting voice input to text. The video explains how to integrate a microphone recording feature into the chat interface using Streamlit's audio handling widgets. It also covers the process of transcribing the recorded audio and using the transcribed text as input for the chat model. The paragraph highlights the need to handle audio data correctly to ensure smooth and efficient communication within the chat application.

This section introduces the integration of image handling into the chat application. It discusses the use of the Lava model for understanding image embeddings. The video outlines the process of uploading images, converting them to base64 encoded strings, and using the Lava model to generate descriptions for the images. It also covers the implementation of a function to handle image uploads and the display of image descriptions within the chat interface. The paragraph emphasizes the potential for improving the user experience by allowing users to interact with the chat application through various media types, including images.

The paragraph focuses on integrating PDF chat capabilities into the application. It introduces the concept of using an embedding database, such as Chroma, to manage PDF documents within the chat. The video explains the process of uploading PDF files, extracting text, and creating vector embeddings for these documents. It also covers the implementation of a function to load the vector database and the use of retrieval question and answer mechanisms from LangChain to interact with the PDFs. The paragraph highlights the potential for users to engage in meaningful conversations with the chat model, even when the input comes from PDF documents.

The final section of the video script discusses enhancing the user interface of the chat application. It introduces the use of HTML templates and CSS to improve the visual appearance of chat messages, adding chat icons for both the user and the AI. The video explains how to integrate custom images into the chat interface and use HTML and CSS to style the chat container. It also suggests the possibility of adding more features, such as displaying images and audio files directly in the chat history, and encourages viewers to experiment with the code and make improvements based on their preferences.