Deploy Any Machine Learning App on Hugging Face Spaces in Just 5 Minutes! скачать в хорошем качестве

Deploy Any Machine Learning App on Hugging Face Spaces in Just 5 Minutes!

Deploy Any Machine Learning App on Hugging Face Spaces in Just 3 Minutes! 🚀 In this video, I'll show you how to deploy any machine learning app on Hugging Face Spaces in just 3 minutes! No complicated setup, no servers—just an easy way to host your AI applications online using Streamlit, Gradio, or Docker. ⏳ Timestamps: ⏳ 0:00 - 0:24 Introduction ⏳ 0:24 - 1:38 What is Hugging Face Spaces? ⏳ 1:38 - 4:45 Hands-on Deployment ⏳ 4:45 - 5:42 Testing the Deployed App ⏳ 5:42 - End Conclusion & Next Steps 🔥 What You'll Learn: ✅ What is Hugging Face & Hugging Face Spaces? ✅ How to create a Hugging Face Space ✅ How to deploy a Sentiment Analysis App using Streamlit ✅ How to manage dependencies with requirements.txt ✅ How to make your ML app live instantly 📌 Useful Links: 🔗 Hugging Face Official Website: https://huggingface.co/ 🔗 Hugging Face Spaces: https://huggingface.co/spaces 🔗 Sentiment Analysis Model: https://huggingface.co/tabularisai/mu... 🔗 Streamlit Documentation: https://docs.streamlit.io/ 💻 Code Used in the Video: import streamlit as st from transformers import pipeline Load Sentiment Analysis Model from Hugging Face sentiment_pipeline = pipeline("text-classification", model="tabularisai/multilingual-sentiment-analysis") Streamlit UI st.title('Sentiment Analysis App') user_input = st.text_area('Enter text:') if user_input: result = sentiment_pipeline(user_input)[0] st.write(f"**Sentiment:** {result['label']}") st.write(f"**Confidence:** {result['score']:.2f}") 📢 Got Questions? Drop a comment below and I’d love to help! 🚀 Let me know if you need any tweaks! 😊