streamlit AI technology page Top Builders

Explore the top contributors showcasing the highest number of streamlit AI technology page app submissions within our community.

Streamlit: Effortless Front-Ends for Your Data Apps

Streamlit is a pioneering technology provider that specializes in turning data scripts into shareable web apps with minimal effort. Launched in 2018, Streamlit has gained popularity for its ease of use and efficiency, empowering data scientists and developers to create and deploy data-driven applications swiftly.

General
Author	Streamlit
Repository	https://github.com/streamlit/streamlit
Type	Framework for ML and data science apps

Key Features

Transforms Python scripts into interactive apps with simple annotations, dramatically reducing development time.
Facilitates real-time interactivity directly from Python code without requiring front-end expertise.
Supports hot-reloading, allowing instant app updates as the underlying code changes.
Provides built-in support for a wide array of widgets, enabling the addition of interactive features without additional coding.

Start building with Streamlit's products

Streamlit offers a range of features designed to simplify the process of app creation and deployment, enhancing productivity in data science and machine learning fields. Explore how you can leverage Streamlit to turn your data projects into interactive applications. Don’t forget to check out the innovative projects built with Streamlit at various tech meetups!

List of Streamlit's products

Streamlit Library

The Streamlit Library allows developers to quickly convert Python scripts into interactive web apps. This library is packed with easy-to-use functionalities that make it straightforward to add widgets, charts, maps, and media files, transforming complex data science projects into user-friendly applications.

Streamlit Sharing provides the hosting infrastructure to share Streamlit apps with the world. It simplifies deployment, enabling users to go from script to app in minutes on a secure and scalable platform.

Streamlit for Teams

Streamlit for Teams is designed for collaboration and enterprise usage, offering additional features like integration with existing databases, advanced security protocols, and customized control for managing user access and data privacy.

System Requirements

Streamlit is compatible with Linux, macOS, and Windows systems, requiring Python 3.6 or later. It typically runs with minimal hardware requirements, though performance scales with available resources. For optimal performance, a modern processor and sufficient RAM are recommended, with a stable internet connection for deploying apps using Streamlit Sharing. Modern browsers with JavaScript support are required to view and interact with the apps.

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streamlit AI technology page Hackathon projects

Discover innovative solutions crafted with streamlit AI technology page, developed by our community members during our engaging hackathons.

movie-travel-SanFrancisco-Edition

Using LangGrpah and OpenAI latest models and function calling capability, we created an agent that interacts with the user over a very interesting dataset maintained by the city of San Francisco government about all the locations in the city that have been used by film and TV projects since the early 20th century. Our goal is to inform tourists and locals alike about all the interesting history associated with different landmarks and streets in the city as it relates to visual arts ad culture. We envision users asking the Agent questions about different categories of films shot in SF; Asking the agent to provide an itinerary customized to the user's geo-location and interests. As of now, we have a working prototype that offers information based on the dataset. However, the geo-location feature has not been completely implemented yet.

Research Alchemy

Research Alchemy is an AI-powered platform designed to transform the way scientific research is conducted, offering tools for researchers, scientists, and analysts to streamline complex tasks. With three core features—data preprocessing, hypothesis generation, and an AI-powered research dashboard—it simplifies and enhances the research process through a user-friendly interface. 1. Data Preprocessing & Cleaning Tool: Managing large datasets can be time-consuming, with tasks like cleaning, normalization, and feature selection often slowing progress. Research Alchemy automates these processes, detecting outliers, handling missing data, and suggesting feature transformations. It also offers anomaly detection and clustering capabilities, paired with interactive visualizations for deeper data insights. 2. Hypothesis Generation and Testing Tool: By leveraging AI, this feature aids researchers in developing potential research hypotheses based on datasets or custom topics. Using advanced NLP capabilities, it analyzes data samples and research literature to suggest 3-5 testable hypotheses and experiments. This tool reduces cognitive load and saves time, enabling researchers to explore new directions with ease. 3. AI-Powered Research Dashboard: The interactive dashboard serves as a comprehensive research environment, integrating AI models and data analysis tools. Users can build predictive models, analyze complex datasets, and access insights seamlessly. The dashboard also provides access to external AI tools and resources, making it a versatile companion for data-driven research. Research Alchemy acts as a digital research assistant, enabling users to maximize their data’s potential, foster innovation, and speed up scientific discoveries. It’s ideal for anyone conducting academic research, industry analysis, or exploratory data analysis, making research faster, smarter, and more efficient.

EdGen

This project focuses on providing farmers in underserved communities with an intelligent AI-powered chatbot that addresses both general and complex queries related to plant care and farming. The system uses a Strategic agent approach: A local model (LLaMA 3.2 3B) running on an edge device to answer general, everyday plant care questions. A larger, cloud-hosted model (LLaMA 3.1 70 B) accessible via API for handling more complex or specialized queries. An intelligent agent is designed to assess the complexity of each query and assign a score. Based on the score, the agent dynamically routes the query to the appropriate model, optimizing for response time, computational resources, and accuracy. Farmers will interact with the system through a simple web or mobile chat interface (powered by Streamlit), enabling them to ask questions about plant health, harvest cycles, and more.

EnglishCoach2

This AI-powered platform is designed to enhance English communication skills, with a focus on personalized feedback for writing and speaking improvement. The platform is ideal for students preparing for exams such as IELTS, PTE, and DET, as well as anyone looking to refine their English proficiency. Users can follow a 30-day structured writing plan, where they submit essays and receive in-depth feedback on areas like grammar, coherence, sentence structure, vocabulary usage, and writing style. For speaking practice, users can record their audio on various topics and receive real-time, AI-generated feedback on pronunciation, fluency, intonation, and word usage. The platform also transcribes audio submissions, allowing users to review their spoken content and identify areas for improvement. A key feature is the ability to track progress over time, with the platform maintaining previous conversations and interactions, providing continuity and enabling users to monitor their improvement journey. By offering both text and audio responses, this platform serves as a comprehensive tool to improve English communication skills, making it easier for students to gain confidence and fluency in both academic and professional settings.

LlamaRural

LlamaRural is a web application developed with Streamlit that empowers users to analyze mobile service coverage in rural areas of Peru. Utilizing official data from the main operators (Telefónica, América Móvil, Entel, and Bitel) the application generates interactive visualizations and detailed analyses of the telecommunications infrastructure available at any specified location. Users can enter geographic coordinates to explore nearby base stations within an adjustable radius. The application features interactive maps with clusters by operator, statistical graphs, and detailed metrics, enabling users to evaluate the availability of different technologies (2G, 3G, 4G, and 5G), connection speeds, and services (voice, SMS, MMS) in each area. In addition to coverage analysis, LlamaRural includes an innovative chatbot, Optimus Llama Chatbot, which interacts with users to provide real-time assistance. Leveraging advanced language models, the chatbot answers questions related to mobile coverage, assists users in troubleshooting connectivity issues, and offers guidance on available services. With location data capabilities, it delivers tailored responses that help users make informed decisions regarding infrastructure deployment and service planning in rural regions. LlamaRural is designed to facilitate decision-making in coverage analysis and infrastructure development, offering an intuitive interface that allows users to easily visualize and comprehend the distribution and characteristics of mobile coverage throughout Peru.

Llamario

Llamario is an advanced AI-driven application designed for content creation and image processing, utilizing cutting-edge models like Llama 3.2 11b and Llama 3-8b. It offers image recognition capabilities, analyzing uploaded images to generate detailed descriptions and insights. Beyond image analysis, Llamario excels in generating well-structured, high-quality written content for a variety of topics, from personal finance to technology trends. Its user-friendly interface and robust AI make it an ideal tool for both content creators and casual users seeking AI-generated insights and creativity. It uses llama 3.2-11b for getting breif description about images, and then uses llama3-8b model to get detailed description on that small description.