Pixelle-Video, an open‑source AI tool from Alibaba’s AIDC‑AI team, lets users type a single topic and automatically creates a complete short video—including script, images, voice‑over, background music and final MP4—through a fully automated pipeline that runs locally or in the cloud.
This article demonstrates how to integrate Alibaba Cloud Elasticsearch with the Qwen‑VL large model and DashScope Embedding API to extract image features and perform multimodal vector search, covering text‑to‑image, text‑to‑text, image‑to‑image, and image‑to‑text queries, with step‑by‑step code, environment setup, data loading, indexing, and a Streamlit demo.
This tutorial shows how to create a lightweight Retrieval‑Augmented Generation (RAG) system that indexes multiple PDF files, stores their embeddings in a FAISS vector database, and answers user queries through a LangChain agent powered by DashScope embeddings and the DeepSeek‑Chat model, all wrapped in a Streamlit UI.
Learn how to create a functional AI agent from scratch in just five days by leveraging GPT for the brain, n8n for automation, CrewAI for multi‑agent orchestration, Cursor for code generation, and Streamlit for a user interface, complete with step‑by‑step instructions and ready‑to‑run Python code.
Autogen_demo, built with Streamlit and AI technologies, offers an end‑to‑end solution that parses PDF or Word requirements, stores them in a version‑controlled database, and automatically generates comprehensive functional, boundary, and exception test cases, exportable as Excel, Markdown, or JSON, boosting efficiency for developers, testers, and analysts.
This guide walks through the complete process of creating a RAG‑powered question‑answering bot using LangChain, Streamlit, and vector‑store embeddings, covering theory, architecture, data loading, chunking, vector indexing, retrieval, LLM integration, and full code implementation with practical examples.
This article introduces eight Python GUI frameworks—including Tkinter, PyQt5, PySimpleGUI, Kivy, wxPython, Toga, Dear PyGui, and Streamlit—explaining their features, providing code examples, and offering a small exercise to create a simple calculator, helping developers quickly build desktop apps.
This article walks through setting up a local Retrieval‑Augmented Generation (RAG) system using the open‑source DeepSeek‑R1 model run via Ollama, covering installation, model selection, PDF ingestion with LangChain, semantic chunking, FAISS vector store creation, RetrievalQA chain construction, and a Streamlit UI for querying.
This guide walks you through installing Ollama, pulling the open‑source DeepSeek R1 model, and using LangChain and Streamlit to create a locally hosted Retrieval‑Augmented Generation (RAG) system that can answer questions from uploaded PDFs without any cloud API.
This article presents six hands‑on Python generative‑AI projects—ranging from a Llama 2 chatbot built with Streamlit and Replicate to natural‑language‑to‑SQL conversion using LlamaIndex and SQLAlchemy—complete with environment setup, required code snippets, deployment tips and resource links for further exploration.
This article introduces five essential Python libraries—MLflow, Streamlit, FastAPI, XGBoost, and ELI5—that every junior or intermediate machine‑learning engineer and data scientist should master to streamline experiment tracking, build interactive web apps, deploy models efficiently, achieve fast accurate predictions, and improve model interpretability.
This guide explains how to install Ollama, explore its supported open‑source LLMs, use its REST API for generation, chat, and embeddings, and build a Streamlit‑based web chat application that runs locally on your machine.
Streamlit is a free, open‑source Python framework that lets machine‑learning engineers quickly turn scripts into interactive web apps, featuring top‑to‑bottom script execution, widget‑as‑variable handling, caching, GPU support, and seamless integration with tools like Git.
Explore a curated list of popular pure-Python libraries—including Streamlit, Gradio, Dash, Voila, and PyWebIO—that let you quickly transform data scripts into attractive, shareable web interfaces, complete with usage stats, recent updates, and visual examples.
Streamlit is a free, open‑source Python framework that lets machine‑learning engineers quickly turn scripts into interactive apps, offering features such as top‑down script execution, widget‑as‑variable handling, caching, GPU support, and seamless integration with version‑control tools, all without requiring separate frontend development.
This tutorial introduces Streamlit—a Python library for building interactive web apps—covering installation, layout, widgets, data caching, charting, and example projects such as dynamic data exploration and a face‑detection demo, all with complete code snippets.
This tutorial demonstrates how to create a lightweight Streamlit web app in Python that lets users select and run common NLP services—sentiment analysis, named‑entity recognition, and text summarization—by integrating libraries such as TextBlob, spaCy, and Gensim, with clear code examples and visual output.
This tutorial explains how to install Streamlit, use its text, data, chart, and input components, manage layout, control flow, and caching, and demonstrates a full garbage‑classification web app that integrates a third‑party image‑recognition API, providing complete Python code examples.
This article explains how Streamlit, a free open‑source Python framework, simplifies the creation of interactive machine‑learning applications by letting developers write pure Python scripts that automatically rerun on each interaction, cache results, and integrate seamlessly with existing tools and workflows.
Streamlit is a free, open‑source Python framework that lets machine‑learning engineers create interactive web apps without writing HTML, CSS, or JavaScript, by simply running Python scripts, using widgets as variables, and leveraging automatic caching for efficient data reuse.
This article explains how machine‑learning engineers can create fully functional, interactive apps using only Python and the open‑source Streamlit framework, covering its core principles, widget handling, caching, GPU support, deployment workflow, and real‑world examples with code snippets and diagrams.
