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 multimodal local chat application capable of handling text, voice, images, and PDFs. The guide covers the setup of a local environment using Python, Streamlit, and various open-source models from Hugging Face. It details the integration of the OpenAI Whisper model for audio processing, Lava and CLIP models for image understanding, and a vector database for PDF text retrieval. The video also explores front-end enhancements with HTML and CSS, offering a comprehensive resource for building a full-fledged, interactive chatbot app.

Takeaways

📌 The video provides a comprehensive guide on creating a multi-model local chat application capable of handling text, audio, images, and PDFs using local models for a seamless experience without relying on external APIs.
🎤 The local chat app integrates the OpenAI Whisper model for processing audio data, allowing users to chat with the model via voice recordings and have them transcribed into text for further interaction.
🖼️ The app also includes image handling capabilities using the Lava model, which can interpret and describe images provided by users, enhancing the interactive experience of the chatbot.
📄 PDF chat functionality is facilitated by using an embedding database to upload and retrieve information from PDF documents, enabling users to discuss and get insights from PDF content.
🔗 The guide walks through setting up the local environment, installing necessary packages, and configuring the development workspace for the project, emphasizing the importance of a well-organized coding environment.
📝 The script explains the integration of Streamlit for creating the front-end web interface, highlighting its advantages for rapid development and ease of use.
🔄 The video emphasizes the importance of session state management for retaining data across multiple runs of the Python script, ensuring a smooth user experience without losing context.
🔧 The guide encourages viewers to code along and experiment with the provided GitHub repository, promoting active learning and personalization of the application.
🚀 The video outlines the potential for further improvements and customizations, such as refining the user interface, adding titles to chat sessions, and handling multiple files for transcription and summarization.
🎨 The final part of the script discusses enhancing the visual appeal of the chat interface with custom HTML templates and CSS, improving user experience and engagement.
💡 The video serves as a valuable resource for those interested in building a versatile local chatbot application with diverse functionalities and encourages exploration and expansion of the project.

Q & A

What is the main goal of the video?
-The main goal of the video is to guide viewers on how to create a multimodal local chat application that can handle voice, image, and PDF inputs using local models.
Which models are used for handling audio data in the application?
-The OpenAI Whisper model is used locally for handling audio data in the application.
How does the application manage multiple chat sessions?
-The application uses Streamlit's session state to manage multiple chat sessions, allowing users to switch between different chat histories and save them as new sessions.
What is the role of the chroma database in the application?
-The chroma database is used to create and manage PDF chat by storing embeddings of PDF documents, allowing the application to retrieve and interact with the content of the PDFs.
How is image handling implemented in the application?
-Image handling is implemented using the lava multimodal model, which can understand and describe images uploaded by the user.
What is the purpose of the 'track index' function in the application?
-The 'track index' function is used to keep track of the current chat session by setting the index tracker to the session key, ensuring that the user's chat history is correctly displayed and managed.
How does the application handle voice recordings?
-The application records voice input using Streamlit's microphone recorder and then transcribes the audio using the OpenAI Whisper model to chat with the model.
What is the significance of the 'get user template' and 'get bot template' functions in the application?
-The 'get user template' and 'get bot template' functions are used to generate HTML templates for user and bot messages, respectively, with the message content embedded in them. This enhances the visual presentation of the chat history.
How can the chat history be displayed more effectively in the application?
-The chat history can be displayed more effectively by adding a chat container and using Streamlit's reverse function to show the latest messages at the top, eliminating the need for the user to scroll down each time the site is reloaded.
What are some potential areas for improvement in the application?
-Potential areas for improvement include refining the front-end design, adding features to display images and audio files in the chat history, and potentially consolidating the use of different AI models into a single model for more streamlined functionality.

Outlines

00:00

📌 Introduction to Multimodel Local Chat App

The paragraph introduces the concept of creating a multimodel local chat application that can handle voice, image, and PDF data. The goal is to integrate various functionalities such as voice recording, image processing, and PDF chat without relying on external APIs. The project will utilize local models for these tasks, with a focus on learning and improvement through hands-on coding. The speaker also mentions the use of the Open AI Whisper model for audio data and the chroma database for PDF chat, highlighting the potential for customization and enhancement of the app.

05:01

🚀 Setting Up the Development Environment

This section outlines the steps for setting up the development environment for the chat app. It begins with the installation of necessary packages and the creation of a virtual environment named 'chatwi'. The speaker emphasizes the importance of using Streamlit as the front-end framework for the app, which simplifies the process of building interactive web applications. The paragraph also discusses the use of session state in Streamlit to manage variables across multiple runs of the Python script, which is crucial for the chat application's functionality.

10:02

💬 Implementing the Chat Interface

The speaker describes the process of building the chat interface using Streamlit. This includes defining a chat container, user input field, and send button. The paragraph details the use of Streamlit's session state to manage the chat history and user inputs. It also addresses a common issue with Streamlit's rerunning script, which can cause the loss of user inputs. The solution involves setting an onchange function for the input field and clearing the input field after the chat message is sent. The speaker provides a hands-on approach to understanding Streamlit's API and its application in creating a dynamic chat interface.

15:03

🗣️ Integrating the Language Model

In this part, the speaker discusses the integration of a language model into the chat application. The focus is on using a local, quantized model for efficient processing. The speaker guides through the process of selecting a suitable model from the Hugging Face hub and loading it into the application. The paragraph also explains the creation of a chat memory function and the use of Streamlit's session state to store and retrieve chat history. The speaker emphasizes the importance of defining clear functions and classes for managing the chat's logic and state.

20:05

📄 Managing Chat History and Sessions

The paragraph delves into the management of chat history and sessions. The speaker explains how to save chat histories using a utility function that converts chat data into a JSON format. The process involves creating time-stamped files for each chat session, allowing users to switch between different chat histories. The speaker also discusses the implementation of a sidebar in Streamlit for selecting chat sessions. The paragraph highlights the importance of proper session state management and the use of external libraries for handling file operations.

25:09

🎙️ Adding Voice Handling Functionality

This section focuses on adding voice handling capabilities to the chat application. The speaker introduces the use of the Open AI Whisper model for converting voice recordings into text. The paragraph details the process of setting up a microphone input in Streamlit and transcribing the recorded audio using the Whisper model. The speaker also explains how to integrate the transcribed text into the chat interface, allowing users to chat using voice commands. The paragraph emphasizes the practical application of AI models in enhancing the interactivity of the chat application.

30:10

🖼️ Implementing Image Handling with Lava

The speaker discusses the implementation of image handling in the chat application using the Lava model. The paragraph explains the process of creating an image handler function that converts image files into base64 encoded strings. The Lava model is then used to generate a description of the image based on the encoded string. The speaker also covers the integration of the image description into the chat interface, enhancing the multimodal capabilities of the application. The paragraph highlights the challenges of working with external models and libraries, and the importance of testing and debugging to ensure seamless integration.

35:11

📄 PDF Handling and Retrieval Q&A

The paragraph covers the implementation of PDF handling in the chat application. The speaker explains the process of creating a vector database for storing PDF embeddings and the use of a retrieval question and answer system from LangChain for interacting with the PDFs. The paragraph details the creation of functions for adding documents to the database, extracting text from PDFs, and splitting the text into chunks for embedding. The speaker also discusses the use of a toggle switch in the application to activate PDF chat mode, allowing users to retrieve information from the PDFs.

40:14

🎨 Enhancing UI with Chat Icons and CSS

In this final section, the speaker focuses on enhancing the user interface of the chat application. The paragraph discusses the addition of chat icons for human and AI messages, and the use of CSS to style the chat interface. The speaker provides a step-by-step guide on how to integrate custom HTML templates for the chat messages, and how to display the latest messages at the top of the chat container for improved user experience. The paragraph concludes with suggestions for further improvements and customization of the chat application.

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 instead of relying on external APIs or services. This ensures faster response times and increased privacy as data stays local to the user's device.

💡Langchain

Langchain is an open-source library used in the development of the local chat app. It provides functionalities for building language models and integrating them into applications. In the video, Langchain is used to create a local model for handling text-based interactions within the chat app, enabling features such as multi-turn conversations and retrieval of information from a vector database.

💡Streamlit

Streamlit is an open-source Python library used to create custom web applications quickly. It allows developers to create interactive web apps with minimal effort by providing a simple interface to create widgets and display data. In the video, Streamlit is used as the front-end framework for the local chat app, enabling the creation of interactive user interfaces for chat sessions, file uploads, and other functionalities.

💡Whisper Model

The Whisper model is an open-source speech recognition model developed by OpenAI. It is designed to transcribe audio into text with high accuracy and can handle multiple languages. In the video, the Whisper model is used locally to process voice inputs from users, converting spoken words into text that can be understood and responded to by the chat app.

💡Image Handling

Image handling refers to the process of managing and processing images within a software application. This includes tasks such as uploading, analyzing, and describing images. In the context of the video, image handling is a feature of the local chat app that allows users to upload images and receive descriptions or analyses from the app using AI models like the Lava model.

💡PDF Chat

PDF Chat is a feature that enables interaction with PDF documents within a chat application. This involves extracting text from PDF files, understanding the content, and allowing users to ask questions or receive summaries about the document. In the video, PDF Chat is achieved by using a vector database to store and retrieve information from PDFs, enabling users to engage in conversations about the content of the documents.

💡Multimodal

Multimodal refers to the ability of a system or application to handle and integrate multiple types of data or inputs. In the context of the video, the local chat app is described as multimodal because it can process and understand various data formats, including text, voice, images, and PDFs, and respond accordingly.

💡Quantized Models

Quantized models are machine learning models that have been optimized to reduce their size and computational requirements while maintaining a high level of accuracy. This process involves reducing the precision of the model's parameters, which allows the model to run more efficiently on devices with limited resources. In the video, quantized models are used to enable the local chat app to run large language models without the need for powerful hardware, making it accessible on local devices.

💡Session State

Session state refers to the management and preservation of data or variables across multiple runs or interactions within an application. This is crucial for maintaining continuity in user interactions, such as retaining chat history or user preferences. In the context of the video, session state is used to store information like user inputs, chat histories, and other data that needs to persist across different sessions of using the local chat app.

💡Vector Database

A vector database is a type of database that stores and retrieves data based on vector representations of information, often used for tasks like semantic search and content-based retrieval. In the video, a vector database is used to store embeddings of text documents, allowing the local chat app to perform retrieval-based question answering by comparing the similarity of user queries to the stored document vectors.

💡Lava Multimodel Model

The Lava Multimodel Model is an AI model capable of understanding and processing multiple types of data, such as text and images. It is used to provide a comprehensive understanding of inputs and generate appropriate responses. In the video, the Lava Multimodel Model is integrated into the local chat app to enable it to describe images uploaded by users, enhancing the app's multimodal capabilities.

Highlights

Create a multimodel local chat app with voice, image, and PDF handling capabilities.

Use local models for processing without relying on external APIs.

Incorporate audio recording and transcription using the OpenAI Whisper model.

Handle image inputs by describing images with the Lava model.

Chat with PDF files by creating a vector database for document embeddings.

Utilize Streamlit for the front-end development of the chat app.

Manage multiple chat sessions with session state and chat history.

Code along with the tutorial for the best learning experience.

Improve the app by cloning the GitHub repository and making personal adjustments.

Use the Hugging Face Transformers library to load quantized models for efficient memory usage.

Implement a toggle to switch between normal chat and PDF chat modes.

Employ the Langchain library for creating the retrieval QA and conversational chains.

Enhance the user interface with custom CSS and HTML templates for chat messages.

Ensure user privacy by not relying on external servers for data processing.

Provide an accessible and interactive learning resource for building AI applications.

Explore the potential of combining different AI models for a comprehensive user experience.

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