🔗 AI in Agriculture Course - https://www.augmentedstartups.com/ai-in-agriculture-course
App 11.4:Building Drought Impact Detection
Build and deploy an AI-powered FastAPI application integrated with a chatbot! 🚀 This comprehensive tutorial walks you through creating a machine learning backend and deploying it with a frontend built using HTML, CSS, and JavaScript. Here's what you'll learn:
Tutorial Highlights:
1️⃣ FastAPI Development
Setting up endpoints for predictions and chatbot interactions.
Loading and utilizing machine learning models with FastAPI.
Integrating static files and templates using Jinja2.
2️⃣ ML Model Integration
Using a pre-trained Random Forest classifier saved as a .pickle file.
Setting up prediction functions for seamless data processing.
3️⃣ Chatbot Implementation
Creating a ChatGPT 3.5 Turbo-powered chatbot for dynamic interactions.
Defining prompts and handling chat history with LangChain.
Connecting chatbot responses to the FastAPI endpoints.
4️⃣ Demo Walkthrough
Launching the app with Uvicorn.
Testing the prediction endpoint for soil dryness classification.
Interactive chatbot responses tailored for farmers addressing drought issues.
5️⃣ Deploy and Run
Step-by-step guide to run the application locally.
Insights into integrating OpenAI's API for real-time chat capabilities.
Subscribe for more tutorials on AI and backend development! 🌟
🔗 Follow My Links:
Facebook: https://www.facebook.com/AugmentedStartups/
LinkedIn: https://www.linkedin.com/in/riteshkanjee/
X: https://x.com/augmentstartups
#FastAPI #AIChatbot #MachineLearning #LangChain #OpenAI #RandomForest
🔗 AI in Agriculture Course - https://www.augmentedstartups.com/ai-in-agriculture-course
App 11.4:Building Drought Impact Detection
Build and deploy an AI-powered FastAPI application integrated with a chatbot! 🚀 This comprehensive tutorial walks you through creating a machine learning backend and deploying it with a frontend built using HTML, CSS, and JavaScript. Here's what you'll learn:
Tutorial Highlights:
1️⃣ FastAPI Development
Setting up endpoints for predictions and chatbot interactions.
Loading and utilizing machine learning models with FastAPI.
Integrating static files and templates using Jinja2.
2️⃣ ML Model Integration
Using a pre-trained Random Forest classifier saved as a .pickle file.
Setting up prediction functions for seamless data processing.
3️⃣ Chatbot Implementation
Creating a ChatGPT 3.5 Turbo-powered chatbot for dynamic interactions.
Defining prompts and handling chat history with LangChain.
Connecting chatbot responses to the FastAPI endpoints.
4️⃣ Demo Walkthrough
Launching the app with Uvicorn.
Testing the prediction endpoint for soil dryness classification.
Interactive chatbot responses tailored for farmers addressing drought issues.
5️⃣ Deploy and Run
Step-by-step guide to run the application locally.
Insights into integrating OpenAI's API for real-time chat capabilities.
Subscribe for more tutorials on AI and backend development! 🌟
🔗 Follow My Links:
Facebook: https://www.facebook.com/AugmentedStartups/
LinkedIn: https://www.linkedin.com/in/riteshkanjee/
X: https://x.com/augmentstartups
#FastAPI #AIChatbot #MachineLearning #LangChain #OpenAI #RandomForest