Build an interactive sales dashboard with Streamlit 1.67, Plotly and pandas: filters, KPI cards, charts and a CSV download, with every step run and shown.

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How Ontology Studio turns a pile of Unity Catalog tables into a governed semantic layer in an afternoon, and why that is the cheapest AI…

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A chart can show that a research phrase is becoming more common. The next questions are harder: which institutions are working on it, and which papers should I read? I built **paper-trends** to connect those steps. It uses publication metadata from OpenAlex to generate interactive reports for academic conferences. You can try the CVPR report without signing in. Click a phrase such as “diffusion models,” then click a related institution to keep exploring. The source code is on GitHub, under the MIT license. ## Following a trend back to its papers The interaction starts with a phrase. Clicking it opens a panel with its yearly trend, related institutions, topics, and papers. Clicking an institution opens another view of its publication trends and characteristic phrases. Here is that interaction in the ECTC example, a conference focused on electronic packaging: The GIF shows an earlier interface snapshot, so individual figures may differ from the current online reports. ## One analysis pipeline, three ways to use it The project has three interfaces that share the same Python analysis code: * A CLI for fetching data and generating reports. * A Streamlit app for changing periods, comparing conferences, filtering papers, and exporting CSV files. * Static HTML reports for sharing a particular analysis. The pipeline is: 1. Retrieve conference records from OpenAlex. 2. Normalize and store the records locally. 3. Calculate phrase trends, topics, and publication statistics. 4. Build the report and its linked detail-panel data. 5. Render the HTML output. Sharing the analysis code keeps the counting rules consistent across the CLI, app, and reports. ## Keeping interactions in a static report The online demo runs on GitHub Pages. Clicking a phrase does not start a Python process or make an OpenAlex API request. The report generator precomputes the data needed by the detail panels. Browser-side JavaScript uses that data to display trends and navigate between related entities. This works well for a fixed report: the expensive analysis happens during generation, while readers can explore the result without installing anything or supplying an API key. There is a tradeoff. The report is a saved snapshot, and precomputed detail data adds to its size. Changing the analysis period or fetching another conference requires the local app or CLI and a new analysis. The public examples cover CVPR and ECTC from 2016 through 2025. The full Streamlit app runs locally, and fetching records requires an OpenAlex API key. ## Defining what a keyword trend means Counting every abstract that mentions a phrase once can include papers where the phrase is only background context. For the phrase trends, the project counts a paper when the normalized phrase appears: * In its title, or * At least twice in its abstract. The report compares shares of papers, which helps account for changes in the total number of publications. This is still a vocabulary-based measure. A paper can discuss a technique without using the exact phrase, and terminology can change over time. A falling phrase share does not necessarily mean the technique is being used less. Topic analysis is a separate view, using TF-IDF and non-negative matrix factorization (NMF). The resulting topics help organize the corpus, but they are not authoritative labels for a research field. ## Conference metadata needs checking One issue during validation was separating a main conference from its workshops. A source-name search could return both. Even records assigned to a main-conference source could contain metadata pointing to workshop proceedings. The fetch pipeline therefore uses source matching, configured DOI-prefix fallbacks, and checks for identifiable workshop records. Those checks use metadata supplied by OpenAlex, including source names and known workshop URL patterns. This improves the dataset, but it does not establish that the result exactly matches an official proceedings list. Coverage still depends on the upstream records. The practical lesson: a plausible paper count is not enough to validate a conference dataset. Inspecting source matches and unusual records can reveal problems that aggregate charts hide. ## Recording when the data was fetched A report’s generation date and its data-fetch date describe different things. The project records a successful fetch timestamp in a sidecar file and associates it with the corpus using a SHA-256 hash. Reports can then show whether that provenance matches the data being analyzed. If the record is missing or does not match, the fetch date is shown as unknown. A file’s modification time is not used as a substitute: copying a dataset should not make it appear freshly fetched. ## Try it * Browse the online reports * Explore the CVPR example * View the code and setup instructions The project supports English and Chinese interfaces and reports. I’d be interested in feedback on the exploration workflow: when you notice a changing research phrase, what would you want to inspect next—institutions, related topics, or individual papers?

Browser-based ontology workbench for OWL ontologies and SKOS vocabularies. Streamlit + rdflib, no Java, no Protégé. OWL-RL reasoning, 22 SKOS validation checks, read-only SPARQL console, bulk opera...

_This is a submission for the Hacktoberfest Open-Source AI Challenge Week 4: Ghost in the Machine_ ## What I Built A basic image text transcriber. ## Code https://github.com/Boxuv/basic-image-transcriber ## How I Built It Gemma 4 ## The Spirit of It Wanted to convert old scanned images of text. As a future update it needs an ability to understand freehand cursive. ## Prize Categories Github Gemma Streamlit

Introduction: Open-source Large Language Models (LLMs) like Google's Gemma series allow developers to build tailored, privacy-preserving educational applications entirely on local hardware. By pairing local LLM inference engines with intuitive Python web frameworks like Streamlit, student developers can create powerful tools for note summarization, practice quiz generation, and concept explanation without exposing sensitive student data or incurring cloud API costs. This article explores the architecture of building an AI-powered "Study Buddy" application using Gemma models, Python, and Streamlit based on hackathon engineering practices.

# 🦥 Sloth Sorter - Local AI File Sorter Have you ever looked at your downloads folder only to be greeted by hundreds of files? Sorting these manually can take some time so I decided to create a tool to help with this. Sloth Sorter is a local AI file organiser built with Python, Streamlit, Ollama and Gemma 3. This was created as part of the Hacktoberfest Open-Source AI Challenge - Week 3: Slow and Steady. ## 💡 Idea The idea is to have the AI do the boring work of sorting files whilst you do more important tasks. Instead of manually opening each file to determine where it goes and what to rename it to, Sloth Sorter will provide suggestions for the correct categories and names. Overall making it easier to sort files and looking into the potential of local AI. ## 🚀 What is Sloth Sorter? Sloth Sorter is a Python app that helps organise files with a local AI model. It has been built to: Scan through selected folder(s) and get file information. Provide suggested categories for files using a local model. Provide suggested meaningful names for files. Review suggested changes. Move files into the correct category folders based on the chosen suggestions. Logging of moves and undo functionality. This app will make organising files easier and not require a paid cloud AI API. ## 🛠️ Technologies Below are the technologies we used: Python - Main logic. Streamlit - UI. Ollama - Local AI model. Gemma 3 - Local AI model. Pytest - Testing. ## 🧠 How does it work? It works by: 1. Selecting a folder. 2. Scanning through files in the folder. 3. Gathering useful information from files. 4. Providing suggested categories and names from the local AI model. 5. Reviewing the suggested changes. 6. Applying chosen changes. With logging of moves to enable undo functionality. The app includes a fallback for categorising files if the AI provides an invalid result. The app uses the AI to provide suggestions and standard Python file operations to make it easy to experiment with. ## 🔓 Why Open Source AI? One of the most interesting parts of this project was using an open weight model running locally. I think open innovation is very important for various reasons. ### 1. More control over data. Using local inference means you don’t need to send any file information to an external AI. Whether this is private depends on how the app is set up. ### 2. Developers can play around with AI apps. You won’t need a paid inference API to play around with an AI powered app. ### 3. Learn how to implement AI. Open weight models allow you to play around with prompts and learn how to implement AI into real apps. You can also see the limitations of the model. ### 4. More flexibility. Apps using a local model can potentially use different compatible models depending on your requirements. This was a good opportunity to look into how local AI can be used to solve a small problem we can all relate to. ## 🦥 Why Slow and Steady? Not the most exciting thing to do but something we all do regularly. Sloth Sorter was built keeping it simple. Automate repetitive tasks, keep the user in control and make everyday tasks easier. I didn’t want to build another chatbot so I looked into how AI can be used practically. ## 📚 What did I learn? I learned how to: Include a local AI model in a Python app. Use Ollama to run the model locally. Create an interactive UI with Streamlit. Combine AI suggestions with deterministic file processing. Deal with name clashes and basic file safety. Write tests to automate the categorisation, scanning, moving and undo functionality. Also a good reminder that you should always review AI suggestions before any changes are made to your files. ## 🔮 Future Improvements I would like to improve the following: Improve categorisation for different file types. Improve name suggestions. Improve the UI for reviewing changes. Improve testing and edge cases. More options for customising categories. ## 🌟 View the Project View the source code here: 🔗 GitHub Repo: https://github.com/Abhinav55849/sloth-sorter I would love any feedback, suggestions or contributions from the open source community. If you think the idea is useful feel free to view the code!

Free BI and data visualization tools verified October 2026: Looker Studio, Metabase (AGPL), Apache Superset, Power BI Desktop, Grafana OSS and Cloud Free, Redash (BSD), Evidence (MIT), Lightdash, Streamlit, Observable, Datawrapper, Flourish, RAWGraphs, OpenSearch Dashboards, Kibana Basic, Databox, Zoho Analytics, Tableau Public — caps, sharing, licences, paid steps. Plotly Chart Studio closed.

What Happened LiteLLM v1.106.0-dev.3 adds provider and model support, including Bedrock Mantle prompt-cache breakpoints, TwelveLabs Pegasus 1.5 inference profiles, MongoDB-key routing for VoyageAI, and Vertex AI inline-tools beta forwarding. Its…

This app was built in Streamlit! Check it out and visit https://streamlit.io for more awesome community apps. 🎈

教材のコードを、自分で説明し、変更できるようになりたい方へ。NumPy・pandas・Matplotlibの操作を、小さな配列や表の入出力から読み解きます。前処理・学習・評価・改善を順につなぎ、Titanicの生存予測をStreamlitアプリとして動かします。関数の引数や戻り値、評価指標の意味、データリークを防ぐ処理まで、コードと理由を対応づけて学びます。Pythonの基本構文を知り、教材を実行