Practice project for learning purpose using AI/ML Tools
1. Plant Disease Detection
Tool
Purpose
Apache Kafka Real-time data streaming for image and message pipelines
TensorFlow Deep learning for image classification
Scikit-learn Data preprocessing and model evaluation
MLflow Model tracking and experiment management
Logging System-wide logging and error tracking
Streamlit Frontend interface for user interaction
Typing Code clarity and reliability using Python type hints
Ollama Local LLM inference for natural language suggestions
LangChain Intelligent reasoning and chaining LLM outputs
CrewAI Agent-based architecture for task delegation and reasoning
Package
Purpose
Why It Matters for You
langchain-ollama Enables LangChain to communicate with local LLMs like Ollama
Run language models on your machine, reducing cost and improving privacy for NLP-based apps
langchain-community Adds community-supported integrations (like tools, chains, agents, and vector stores)
Supercharges LangChain with access to non-core tools (e.g., Pinecone, Weaviate, or custom tools)
langchain Framework for building applications with LLMs through tools, memory, chains, and agents
Core framework to build multi-step intelligent apps like chatbots, summarizers, or code assistants
streamlit Lightweight web app framework for data apps and ML interfaces
Quickly prototype and deploy your app with minimal frontend code — perfect for business demos
pypdf PDF parsing and reading utility
Useful for reading resumes, papers, invoices, or any documents in AI pipelines
pydantic-settings Easy configuration management using .env or environment variables
Manage your API keys, settings, and environment-specific variables like a pro