6. Get User Input and Generate Itinerary • Create text inputs for the user to enter their travel destination and the number of days they want to travel for.
• When the button is clicked, run the Planner assistant to generate the itinerary and display it using 'st.write()'
TOOLS
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Build AI Travel Planner with User Input and Itinerary Generation
By
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Setting Up a Streamlit App with Title and Description
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4. Set up the Streamlit App • Add a title to the app using 'st.title()'
• Add a description for the app using 'st.caption()' -
Essential Libraries for Building AI Agents with Streamlit and Ollama
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3. Import necessary libraries • Streamlit for building the web app
• phi for building AI agents and tools
• Ollama for running Llama-3
• SerpAPI for web search functionality -
Deploy Llama-3 Locally Using Ollama Desktop App
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2. Deploy Llama-3 locally using Ollama • Download & Install the @ollama desktop app
• Run the following command to download llama-3 instruct model -
Install Python Libraries for AI Development
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1. Install the necessary Python Libraries Run the following commands from your terminal to install the required libraries:
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Build Local AI Travel Agent with Llama-3 Free
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Build an AI Travel Agent using Llama-3 running locally on your computer (100% free and without internet):
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Build LoRA-Powered AI Systems with Predibase and LoRAX
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Apple's innovative architecture for GenAI is built on small language models (#SLMs) and many fine-tuned #LoRA adapters. See how you can build your own LoRA-powered AI systems today with Predibase and #LoRAX:
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Daily Papers Digest Gets New Features Update
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In case you missed it, @_akhaliq
's new Daily papers digest got some nice new features -
Cohere Enterprise AI Models on Oracle AWS Azure Cloud
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Use Cohere's SDKs with our enterprise-grade models hosted on other cloud platforms including @OracleCloud
, @awscloud
, and @Azure
. See https://
docs.cohere.com/docs/cohere-wo
rks-everywhere
… for documentation and availability. -
FactSet Enhances AI Workflows with Databricks LLMOps Framework
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FactSet aims to enhance client workflows and productivity with AI. However, the lack of standardized LLM development and LLMOps frameworks posed challenges.
Databricks helped FactSet evolve to an end-to-end AI framework, balancing cost & flexibility. https://
bit.ly/3KGNcLK