#Streamlit

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#Streamlit Reel by @darshcoded - Here are 3 unique data science projects you can build in a weekend (2026 World Cup) easy. a World Cup match outcome predictor. predict win, loss, or
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@darshcoded
Here are 3 unique data science projects you can build in a weekend (2026 World Cup) easy. a World Cup match outcome predictor. predict win, loss, or draw using historical FIFA data. tech stack: Python, Pandas, Scikit-learn, and Streamlit to deploy it. medium. a player performance dashboard. pull player stats from Transfermarkt, visualize everything, and cluster players by playing style. tech stack: Python, Pandas, Plotly, and Seaborn for visualization with KMeans for clustering. hard. a real time World Cup sentiment tracker. pull live tweets during matches, run sentiment analysis as goals happen, and visualize how public opinion shifts in real time. tech stack: Python, Tweepy for the Twitter API, HuggingFace Transformers for sentiment analysis, and Plotly Dash for the live dashboard. comment “World cup” for resources to help you out along the way. #machinelearning #datascience #ai #python #cs
#Streamlit Reel by @sandreke99 - 🗣️ Oh, qué será de mi compa sin esos 24k, comenta "inversiones" y te paso el código #inversiones #python #streamlit #programacion #sandreke
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@sandreke99
🗣️ Oh, qué será de mi compa sin esos 24k, comenta “inversiones” y te paso el código #inversiones #python #streamlit #programacion #sandreke
#Streamlit Reel by @welcomeaiengineer - Backend : FastAPI: Used to build very fast and modern APIs in Python. Flask: Used to build simple web apps or APIs in Python. -------------------- Fr
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@welcomeaiengineer
Backend : FastAPI: Used to build very fast and modern APIs in Python. Flask: Used to build simple web apps or APIs in Python. -------------------- Frontend : Streamlit: Used to build quick UI apps for data and AI using Python.
#Streamlit Reel by @data_pumpkin - Here's how to build your own LLM app: Make a simple PDF Q&A chatbot in 6 steps ⬇️ 💡 Problem: Ask questions over any PDF - your resume, research pa
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@data_pumpkin
Here’s how to build your own LLM app: Make a simple PDF Q&A chatbot in 6 steps ⬇️ 💡 Problem: Ask questions over any PDF — your resume, research paper, legal doc, etc. 👩‍💻 What you’ll build: A chatbot that understands your document and gives GPT-powered answers. ⸻ 🔧 Step-by-step: 1. Get OpenAI API access & run a test prompt using openai.ChatCompletion.create(). 2. Load your PDF + chunk it Use PyMuPDF or LangChain’s UnstructuredPDFLoader. 3. Create embeddings & store them Use OpenAI’s text-embedding-3-small + FAISS (vector DB). 4. Build a RAG pipeline → Retrieve top-matching chunks → pass them into your GPT prompt. 5. Add a UI Use Gradio or Streamlit for a clean textbox + output app. 6. Deploy it HuggingFace Spaces or Streamlit Cloud — free & shareable. ⸻ 🚀 Bonus: Use LlamaIndex instead of LangChain if you want something even more minimal for beginners. #llm #openaiapi #langchain #gradio #streamlit #pythonprojects #gptbuilder #ragpipeline #buildinpublic #aiproject #aibuilder #techreels #learnai #aiapp #gptapp #llmproject #100daysofai
#Streamlit Reel by @dianasaurbytes - How I use AI: 3 ways that worked and 3 ways that didn't Part 2 of 6 I use AI all the time for data analytics. It's really good at writing Python. So
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@dianasaurbytes
How I use AI: 3 ways that worked and 3 ways that didn’t Part 2 of 6 I use AI all the time for data analytics. It’s really good at writing Python. Some strategies: - in cursor, you can have one model evaluate another model’s work - always double check the approach the AI took - btw, using Claude Code a lot to build Streamlit apps #streamlit #ai #analytics #datanalytics
#Streamlit Reel by @finance.thomas (verified account) - 1. Build a real-time macro dashboard Trading desks don't guess, they monitor. Use APIs from FRED and central bank feeds to track CPI, yields, inflatio
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@finance.thomas
1. Build a real-time macro dashboard Trading desks don’t guess, they monitor. Use APIs from FRED and central bank feeds to track CPI, yields, inflation breakevens and policy expectations in one interface. Connect this to a Notion or Streamlit dashboard so you see regime shifts instantly. 2. Use AI to summarize information flow Instead of reading 50 headlines, use an LLM to process Bloomberg, FT or earnings transcripts and extract key changes in tone, guidance and risk factors. Desks care about what changed, not what was said. 3. Monitor cross-asset signals automatically Track S&P futures, 10Y yields, DXY, oil and VIX together. Set volatility or correlation alerts using Python scripts or tools like Zapier. If correlations spike or yields break key levels, you get notified immediately. 4. Build a positioning and sentiment layer Use options data, put-call ratios and implied volatility surfaces to see how markets are positioned. Add basic sentiment scraping from financial news to detect extreme consensus before reversals. 5. Stress test your portfolio daily Run scenario simulations when macro data hits. If rates rise 50 bps or volatility doubles, how does your exposure change. Trading desks think in scenarios, not predictions. Drop “Equity” in comments to know more about market knowledge !
#Streamlit Reel by @dr.james.utley (verified account) - Skip the frontend heartache and use streamlit or gradio for all your UI needs. #ai #buildwithai #streamlit #gradio #ml
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@dr.james.utley
Skip the frontend heartache and use streamlit or gradio for all your UI needs. #ai #buildwithai #streamlit #gradio #ml
#Streamlit Reel by @aiwithapoorvaa - Spend another day with me as an AI Engineer 👩‍💻🏡 Today we are using Fastapi and streamlit 👩‍💻 FastAPI + Streamlit explained (bookmark this) ⬇️ Fa
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@aiwithapoorvaa
Spend another day with me as an AI Engineer 👩‍💻🏡 Today we are using Fastapi and streamlit 👩‍💻 FastAPI + Streamlit explained (bookmark this) ⬇️ FastAPI is used to build high-performance backend APIs • Request/response validation with Pydantic • Auto-generated Swagger docs • Perfect for ML, GenAI, and microservices Streamlit is used to build interactive dashboards fast • No frontend complexity • Ideal for demos, internal tools & ML apps. #artificialintelligence #datascience #aiwithapoorva
#Streamlit Reel by @rodrigotadewald (verified account) - Parei de usar o Streamlit em 2026- não porque ele não funcione, mas porque ele impõe limites claros quando o assunto é design, velocidade e flexibilid
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@rodrigotadewald
Parei de usar o Streamlit em 2026— não porque ele não funcione, mas porque ele impõe limites claros quando o assunto é design, velocidade e flexibilidade. Dá pra construir tudo só em Python, sim, mas no final quase todo dashboard acaba com a mesma cara: visual limitado, experiências parecidas e, em alguns casos, performance que deixa a desejar. A virada de chave foi separar completamente as responsabilidades. Passei a usar IA para criar o front-end do zero — o que chamamos de _Vibe Design_ — e deixar o Python focado apenas no que ele faz melhor: dados e backend. HTML, CSS e JavaScript puros no front (que a IA monta com muita facilidade) e FastAPI para servir dados com performance e controle total. O fluxo é simples e extremamente eficaz: escolher um site com um design forte, pedir para a IA extrair um design system completo (cores, tipografia, componentes, animações), gerar o front do dashboard em arquivos simples e, por fim, conectar tudo a um backend em Python. O resultado são dashboards que carregam quase instantaneamente, têm identidade visual de verdade e escalam muito melhor E você já testou alguma estratégia diferente para fugir das limitações do Streamlit?
#Streamlit Reel by @dailydoseofds_ - Build production-grade LLM web apps in minutes! 🚀 (open-source Streamlit alternative; 19K+ stars) While data scientists are fond of using Jupyter t
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@dailydoseofds_
Build production-grade LLM web apps in minutes! 🚀 (open-source Streamlit alternative; 19K+ stars) While data scientists are fond of using Jupyter to explore data and build models... ...an interactive app is better for those who don't care about the code and are interested in results. Taipy is an open-source Python AI & data web application builder. No need to learn JavaScript, CSS, or HTML. You can think of Taipy as a more robust and richer version of Streamlit, which is capable of building: → Prototypes (like Streamlit) → Robust production-ready data applications The latency difference in practical apps is quite noticeable between Taipy and other apps, as shown in the animation below. Plus, Taipy's VS Code extension provides no-code functionalities to build data apps. Link to the repo in the comments! 👉 Over to you: What other frameworks are you aware of? #python #streamlit #datascience
#Streamlit Reel by @inixindo.id - Bikin web apps canggih itu mudah kok, inxpeople… ASAL kamu paham dasar Flask/Django, bisa hubungkan database, dan ngerti cara pakai fitur pintar berba
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@inixindo.id
Bikin web apps canggih itu mudah kok, inxpeople… ASAL kamu paham dasar Flask/Django, bisa hubungkan database, dan ngerti cara pakai fitur pintar berbasis AI atau automasi. Tapi tenang… semua itu bisa kamu pelajari di Webinar: Build Smart Web Apps with Python 🚀🐍 Di webinar ini kamu akan belajar: 🔹 Basic Python – Fondasi utama untuk mulai ngoding web apps 🔹 Exploring Python Libraries – Kenalan dengan pustaka populer yang mempercepat kerja developer 🔹 Python for Data – Analisis & visualisasi data 🔹 Machine Learning with Python – Dasar ML + contoh penerapannya 🔹 Demo: Build Web Apps with Streamlit – Praktik bikin aplikasi web interaktif langsung! Bonusnya? ✨ Sertifikat 🎁 Hadiah menarik untuk peserta yang beruntung Tertarik ikut Build Smart Web Apps with Python? 📅 Catat tanggalnya 🔗 Daftar sekarang melalui website inixindo.id Bersama Inixindo — continuous learning, keep up to date. 💙 #Inixindo #WebinarInixindo #PythonIndonesia #WebDevelopment #Streamlit #MachineLearning #BelajarCoding #developerid
#Streamlit Reel by @koshurai.official - 🚀 Ever wondered if AI could finally make meetings, podcasts, and calls truly intelligible? I just built a Streamlit app that uses AssemblyAI's Speak
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@koshurai.official
🚀 Ever wondered if AI could finally make meetings, podcasts, and calls truly intelligible? I just built a Streamlit app that uses AssemblyAI’s Speaker Diarization API to transform messy audio into structured, speaker-labeled transcripts — in real time. No more guessing who said what. No more endless rewinds. Every speaker’s voice is tagged and timestamped, making insights from conversations instantly actionable. 💡 How it works: Upload an audio file or paste a public URL. The AI automatically separates Speaker 1, Speaker 2, … and generates a clean transcript. You can listen to the original audio while seeing the transcript evolve. Imagine the possibilities: ✅ Automated podcast transcription ✅ Customer support call analysis ✅ Meeting summarization for product teams ✅ AI-assisted research interviews This is Koshur AI’s leap toward making conversations fully analyzable by AI, bridging the gap between human communication and actionable insights. Curious to see it in action? The app even plays your audio while showing the speaker-separated transcript — like watching AI understand your conversation in real-time. 🎧 💬 Question for the LinkedIn community: Where would you apply speaker diarization to extract value from your meetings, calls, or podcasts? #AssemblyAI #SpeakerDiarization #SpeechAI #Streamlit #Python #DataScience #AI #MachineLearning #Innovation #KoshurAI #Podcasts #Meetings

✨ #Streamlit Discovery Guide

Instagram hosts 16K posts under #Streamlit, creating one of the platform's most vibrant visual ecosystems. This massive collection represents trending moments, creative expressions, and global conversations happening right now.

The massive #Streamlit collection on Instagram features today's most engaging videos. Content from @darshcoded, @finance.thomas and @aiwithapoorvaa and other creative producers has reached 16K posts globally. Filter and watch the freshest #Streamlit reels instantly.

What's trending in #Streamlit? The most watched Reels videos and viral content are featured above. Explore the gallery to discover creative storytelling, popular moments, and content that's capturing millions of views worldwide.

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Analysis of 12 reels

🔥 Highly Competitive

💡 Top performing posts average 253.0K views (2.5x above average). High competition - quality and timing are critical.

Focus on peak engagement hours (typically 11 AM-1 PM, 7-9 PM) and trending formats

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💡 Top performing content gets over 10K views - focus on engaging first 3 seconds

✨ Many verified creators are active (25%) - study their content style for inspiration

📹 High-quality vertical videos (9:16) perform best for #Streamlit - use good lighting and clear audio

✍️ Detailed captions with story work well - average caption length is 766 characters

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