#Streamlit

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Reels em Alta

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#Streamlit Reel by @tom.developer (verified account) - If you're a Python programmer, Streamlit is a really useful open-source tool for demoing your projects without building a complex visual interface. 💻
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@tom.developer
If you’re a Python programmer, Streamlit is a really useful open-source tool for demoing your projects without building a complex visual interface. 💻🤔 Using a few lines of Python, you can build a visual interface for your project, making it much easier to demo, especially to non-programmers! 🚀
#Streamlit Reel by @codersheary - Can't build the front end? 🥲 Found this tool called same.new, it is a great tool to build the front end, or copy any UI for you so you can focus on b
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@codersheary
Can’t build the front end? 🥲 Found this tool called same.new, it is a great tool to build the front end, or copy any UI for you so you can focus on building the real thing 👩‍💻 Let’s ship fast 🚀 How do u usually build the front end apart from using Streamlit? #ai #tech #machinelearning #softwareengineer #data #coding #programmers #selftaught
#Streamlit Reel by @laskentatechltd - Streamlit - Build an Interactive Data Visualization App in Python! #python #coding #programming
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@laskentatechltd
Streamlit - Build an Interactive Data Visualization App in Python! #python #coding #programming
#Streamlit Reel by @aman_deep_2025 - The Streamlit library is a Python framework that makes it quick and easy to build interactive web apps for Machine learning . Resources: https://you
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@aman_deep_2025
The Streamlit library is a Python framework that makes it quick and easy to build interactive web apps for Machine learning . Resources: https://youtu.be/yKTEC1Y5bEQ?si=ogYKh-_pmXXOhZvm #AugustVibes, #2025Aesthetic, #InstaGlowUp, #MindfulMoments, #TechTakesOver, #EcoLiving2025, #SelfCareSeason, #WeekendWander, #CreatorsUnite, #HomeGoals2025 #machinelearning #streamlit
#Streamlit Reel by @code.with.ahsan (verified account) - Deploy Your AI Agent App for FREE in Minutes Using Streamlite! You've built an AI agent locally, but how do you share it with your team or get user f
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@code.with.ahsan
Deploy Your AI Agent App for FREE in Minutes Using Streamlite! You've built an AI agent locally, but how do you share it with your team or get user feedback? The answer is Streamlit Cloud. This video shows you how to deploy your AI project to a live, public URL for free. It's the perfect solution for: ✅ Creating a quick Proof of Concept (POC). ✅ Getting fast feedback from users. ✅ Sharing your work without complex DevOps. Whole video link: https://www.youtube.com/watch?v=y7damsm8Qos&t=474s . . . #Streamlit #AIdemo #Deployment #MLOps #GitHub #Developer #POC #AIapp #google #cloud #genai #ai #agent #artificial #intelligence #coding
#Streamlit Reel by @slashdevhq (verified account) - 💸 Day 14 -Build Your Own Finance Hub with Python Tired of juggling spreadsheets to track your money? Let's fix that - with a few lines of Python + S
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@slashdevhq
💸 Day 14 -Build Your Own Finance Hub with Python Tired of juggling spreadsheets to track your money? Let’s fix that - with a few lines of Python + Streamlit 💻 You’ll build a clean dashboard that shows your spending, savings, and net worth - all in one place. Upload your transactions, group by category, and boom - instant insights. Add sliders and filters, and watch your charts update live. It’s like creating your own version of Mint,but smarter, faster, and fully yours. Drop “Python” in the comments and I’ll send you the dashboard code 🔥 #PythonProjects #StreamlitApp #FinanceDashboard #PythonLearning #DataVisualization #CodeWithMe #LearnToCode #AITools #CodingJourney
#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 @googlecloud (verified account) - Gemini and Streamlit will join us for this leg of our summer learning road trip. Get ready to: - Work with the Gemini API - Deploy a Steamlit app inte
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@googlecloud
Gemini and Streamlit will join us for this leg of our summer learning road trip. Get ready to: - Work with the Gemini API - Deploy a Steamlit app integrated with Gemini on Cloud Run - Practice app development in Vertex AI Take this course today at the link in our bio.
#Streamlit Reel by @tech_jroshan - 🚨 Built a Complete Spam Detection System using ML + LSTM + Attention! 🚀 Check it out on Hugging Face Spaces 👉 https://lnkd.in/grXhnBR2 🧠 This p
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@tech_jroshan
🚨 Built a Complete Spam Detection System using ML + LSTM + Attention! 🚀 Check it out on Hugging Face Spaces 👉 https://lnkd.in/grXhnBR2 🧠 This project predicts whether an email is Spam or Not Spam using both: ✅ Machine Learning (Ensemble: RandomForest + GradientBoost + LogisticRegression) ✅ Deep Learning (BiLSTM + Attention Layer) 🛠️ Tech Stack Frontend: Streamlit / HTML (Flask) Backend: Flask REST API + Dockerized ML: TF-IDF + StackingClassifier DL: BiLSTM + Custom Attention Layer (Keras) CI/CD: GitHub Actions Deployment: Docker & Hugging Face Spaces 🔍 Key Features Text-based input → Classifies Spam or Not Spam 📥 BiLSTM with Attention: Improves contextual understanding TF-IDF + Ensemble Model: Boosts accuracy Handles Overfitting: Dropout, EarlyStopping, ReduceLROnPlateau Performance Metrics: Accuracy, Precision, ROC-AUC 📊 Fully containerized with Docker 🐳 Gradio UI for interactive prediction 🤖 🔗 Test the API POST http://localhost:5000/api/predicit Input: { "text": "Congratulations, you've won!" } Output: { "prediction": "Spam", "confidence": 0.97 } 📦 Try it Out Locally git clone https://lnkd.in/ga3gUqZt cd Email-spam-detection python -m venv venv venv/Scripts/activate pip install -r requirements.txt streamlit run streamlit_app.py 🧪 Gradio UI Preview python app.py → http://127.0.0.1:7860 → or deployed link from Hugging Face 💬 I'd love feedback from the community! 🔁 Like, Share, or Fork if you're interested in NLP, Spam Detection, or ML+DL hybrid systems! 🚀 Join Groups for latest Update and Notes:- https://lnkd.in/dYh-u4wP Top 10 Machine Learning questions:- 🌈 https://lnkd.in/gcewTQdC 🔁 follow for more Data Science | AI/ML | Interview Preparation | Data Analysis Content #machinelearning #deeplearning #nlp #spamdetection #streamlit #flask #docker #githubactions #gradio #huggingface #python #tensorflow #evaluationmatrix #classificationmatrix #DetectionEmailSpam #Testing #EmailSpamDetection
#Streamlit Reel by @priyal.py - 1. Personalized Workout & Nutrition Recommendation System Collects wearable fitness tracker data + food logs to suggest daily workouts and meals. Stac
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@priyal.py
1. Personalized Workout & Nutrition Recommendation System Collects wearable fitness tracker data + food logs to suggest daily workouts and meals. Stack: Fitbit API / Apple HealthKit (data), PyTorch (recommendation model), FastAPI (backend), Streamlit (UI), MLflow (tracking). 2. AI-Powered Meeting Minutes Generator Records meeting audio, transcribes it, and generates structured summaries + action points. Stack: Whisper (speech-to-text), BART/T5 (summarization), Hugging Face Transformers, FastAPI (API), React (frontend). 3. Smart Hiring Interview Analyzer Analyzes candidate interview videos for sentiment, speech clarity, and confidence scoring. Stack: OpenCV (video), Whisper (speech-to-text), PyTorch (sentiment + emotion detection), FastAPI (API), Streamlit (dashboard). 4. Intelligent Traffic Management System Uses live camera feeds to detect congestion and optimize traffic light timings. Stack: YOLOv8 (object detection), OpenCV (video processing), Kafka (streaming), FastAPI (API), Grafana (monitoring). 5. AI Tutor for Code Debugging Users paste buggy code → system explains errors and suggests fixes automatically. Stack: Tree-Sitter (code parsing), LLM (OpenAI GPT / CodeLlama), LangChain (prompt orchestration), Streamlit (UI). 6. Mental Health Chat Companion A chatbot that detects emotional states in conversation and gives wellness recommendations. Stack: Emotion classification with RoBERTa, RAG pipeline with domain knowledge, LangChain + FAISS, Gradio (UI). 7. Energy Consumption Predictor for Smart Homes Forecasts household electricity usage and suggests optimization strategies. Stack: Smart meter datasets, LSTMs/Transformers (forecasting), Flask (API), Streamlit (interactive dashboard), Airflow (automation). 8. AI-Driven Career Path Advisor Takes a resume and recommends next career roles, required skills, and learning paths. Stack: BERT embeddings for skill extraction, Scikit-learn / PyTorch (matching), Streamlit (UI), Hugging Face Hub (deployment). #datascience #machinelearning #womeninstem #learningtogether #progresseveryday #tech #consistency
#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 @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

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