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

Leon Explains AI
9 Jan 202468:52

TLDRThis video tutorial demonstrates how to create a multifaceted local chat application capable of handling text, voice, images, and PDFs using open-source tools and local models. The guide covers the setup process, integration of the OpenAI Whisper model for audio data, image processing with the Lava model, and PDF interaction through an embedding database. The application is built using Streamlit, allowing for a user-friendly interface and interactive sessions. The video provides a comprehensive learning experience, inviting users to clone the GitHub repository and customize the app to their preferences.

Takeaways

  • 🌟 Create a multi-model local chat app capable of handling voice, images, PDFs, and text chats using local models for a more private interaction without relying on external APIs.
  • 🎯 Utilize Streamlit for the front-end development, allowing for a simple and efficient user interface with the ability to handle JavaScript and create web pages.
  • 🗣️ Integrate the OpenAI Whisper model locally to process audio data effectively, enabling voice chat functionalities within the application.
  • 🖼️ Implement the Lava model to understand and describe images, enhancing the app's capabilities to interact with visual content.
  • 📄 Use the Chroma database to enable PDF chat by creating an embedding database that allows the app to chat with and understand PDF files.
  • 🔄 Employ session state within Streamlit to store variables and data needed across multiple runs of the Python script, ensuring continuity in user interactions.
  • 📚 Provide a GitHub repository with the complete code for the project, allowing users to clone and modify the app to suit their preferences and improve their understanding.
  • 🔧 Discuss potential areas for improvement, such as refining the front-end design, handling multiple files, and integrating more advanced models for better performance.
  • 🎉 Encourage users to code along with the video for a better learning experience, and to explore and enhance the app by making their own modifications.
  • 🔗 Include a link to the GitHub repository and Streamlit website for users to access resources and further develop their applications.
  • 🚀 Highlight the importance of local models for privacy and the ability to create a complete chat application that covers various types of user interactions.

Q & A

  • What is the main goal of the video?

    -The main goal of the video is to demonstrate how to create a multi-model local chat application that can handle voice, images, PDFs, and text chats using local models and various technologies such as Langchain, Streamlit, and OpenAI's Whisper model.

  • Which technologies are used for the front-end of the application?

    -Streamlit is used as the front-end technology for the application, providing a simple and efficient way to create web interfaces with Python code.

  • How does the application handle audio recordings?

    -The application uses OpenAI's Whisper model to transcribe audio recordings into text, which can then be processed further by the chatbot.

  • What is the role of the Lava model in the application?

    -The Lava model is used for image handling in the application. It provides image embeddings that can be used to describe or analyze images uploaded by the user.

  • How are PDF files processed in the chat application?

    -PDF files are processed by extracting their text content, splitting it into chunks, and then adding the chunks as documents to a vector database, which can be queried for information retrieval and chat purposes.

  • What is the significance of the Chroma database in the application?

    -The Chroma database is used as an embedding database to store and manage the embeddings of PDF documents, allowing for efficient retrieval and interaction with the chatbot.

  • How does the chat application manage multiple chat sessions?

    -The chat application manages multiple chat sessions by saving the chat history under different session keys, which can be loaded and switched between as needed.

  • What is the purpose of the toggle PDF chat feature?

    -The toggle PDF chat feature allows users to switch between normal text chat and a special mode where the chatbot can interact with PDF documents, providing information and answering questions based on the content of the PDFs.

  • How can the chatbot's responses be improved?

    -The chatbot's responses can be improved by fine-tuning the models used, adjusting the parameters for better context understanding, and potentially incorporating additional models for more specialized tasks.

  • What are some potential areas for further development or improvement of the application?

    -Potential areas for further development include enhancing the user interface, improving the handling and display of non-text inputs like images and audio, optimizing the chatbot's response generation, and expanding the application's capabilities to handle more types of input and provide more sophisticated interactions.

Outlines

00:00

📱 Introduction to Multimodel Local Chat App

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.

05:01

💻 Streamlit Setup and Basic Chat Interface

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.

10:02

🗃️ Incorporating Local Models for Chat

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.

15:03

🔄 Managing Chat History and Sessions

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.

20:05

🎙️ Implementing Audio Handling with Whisper Model

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.

25:09

🖼️ Adding Image Handling Capability

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.

30:10

📄 PDF Chat and Vector Database Integration

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.

35:11

🖌️ Enhancing UI with Chat Icons and CSS

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.

Mindmap

Keywords

💡Local Chat App

A local chat app refers to a software application that enables communication between users on the same network or device without the need for an internet connection. In the context of the video, the local chat app is being developed to handle various types of data, including voice, images, PDFs, and text, using local models. This means that the processing and handling of data occur on the user's own device, ensuring privacy and reducing reliance on external servers.

💡Langchain

Langchain is an open-source library used for building conversational interfaces and integrating with various AI models. It provides functionalities for text processing, dialogue management, and state handling. In the video, Langchain is utilized to create a local chat application that can interact with different types of data inputs, such as audio, images, and text documents.

💡Streamlit

Streamlit is an open-source Python library used to create interactive web applications quickly. It allows developers to create apps with minimal web development knowledge by focusing on Python code. In the video, Streamlit is used as the front-end framework to build the user interface for the local chat app, enabling features like text input, button interaction, and dynamic content updates.

💡Open AI Whisper Model

The Open AI Whisper Model is an AI model developed by Open AI for speech recognition tasks. It can transcribe spoken words into text and supports multiple languages. In the video, the Whisper Model is used locally to handle audio data within the chat application, allowing users to communicate via voice recordings and have them transcribed into text for聊天 purposes.

💡Quantized Models

Quantized models are a type of machine learning model that has been optimized to reduce its size and computational requirements without significantly sacrificing accuracy. This is achieved through a process called quantization, which reduces the precision of the model's parameters. In the context of the video, quantized models are used to ensure that the chat application can run efficiently on local devices with limited resources, such as RAM or CPU power.

💡Chroma Database

The Chroma Database is a tool used for managing and querying embeddings, which are numerical representations of words or documents in a vector space. It enables efficient retrieval of similar documents based on their embeddings. In the video, the Chroma Database is used to index PDF documents and allow the chat application to retrieve and discuss information from these documents during conversations.

💡Lava Multimodel Model

The Lava Multimodel Model is an AI model capable of understanding and processing images. It uses a combination of vision and language models to interpret and generate descriptions for visual content. In the video, the Lava Multimodel Model is integrated into the chat application to provide image handling capabilities, allowing users to upload images and receive descriptive responses from the model.

💡Session State

Session State refers to the management of data or information that is retained across multiple interactions or requests within an application. It allows for the continuity of user experience by remembering previous inputs, outputs, and other relevant data. In the context of the video, Session State is used to store variables and data that are needed for multiple runs of the Python script, ensuring that the chat history and user inputs are preserved even as the application reruns.

💡Audio Handling

Audio handling refers to the process of recording, processing, and interpreting audio data within an application. This involves capturing sound input, converting it into a usable format, and often transcribing it into text for further analysis or response generation. In the video, audio handling is a key feature of the chat application, enabling users to communicate via voice recordings that are then transcribed and responded to by the AI model.

💡PDF Chat

PDF Chat is a feature that allows users to interact with PDF documents within a chat application. This involves uploading PDF files, extracting their content, and enabling the AI to discuss or answer questions related to the document's information. In the video, PDF Chat is achieved by using a vector database to index and retrieve information from PDFs, allowing the chat application to engage in discussions about the content of these documents.

Highlights

Create a multi-model local chat app with voice, image, and PDF handling capabilities.

Use local models for processing audio, images, and text without relying on external APIs.

Incorporate the OpenAI Whisper model for local audio data handling.

Utilize the chroma database to enable PDF chat functionality.

Employ the lava multimodel model for understanding and processing images.

Streamlit used as the front-end framework for building the chat application.

Session state used to store variables and data across multiple runs of the Python script.

Code along with the tutorial for the best learning experience and room for improvement.

GitHub repository provided for cloning and further development of the app.

Local models used for audio and image processing to save memory and resources.

Create chat sessions and manage chat history with timestamped saved sessions.

Audio recordings can be transcribed and summarized for easy access to content.

Images can be uploaded and described in detail by the model.

PDF documents can be uploaded, their text extracted, and searched for specific information.

Toggle functionality to switch between normal chat and PDF chat.

Improvements and customizations can be made by users for a personalized experience.

Potential to combine different AI models into one for streamlined functionality.