🐍 Python Programs Portfolio

Welcome to the Python Programs Portfolio repository!
This collection showcases multiple Python-based projects for data analysis, visualization, and web applications, organized in dedicated subfolders.

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📦 Projects Included

🤖 AI Chat Bot with Google Gemini API

• Description:
  A terminal-based Python application that integrates with Google's Gemini API for conversational AI interactions
• Location: ai-chat-bot-google-gemini/
• Key Features: Google Gemini API integration, terminal-based interface, conversational AI
• Tech Stack: Python, Google Generative AI

🤖 Gemini Chatbot with Streamlit

• Description:
  A modern web-based conversational AI chatbot built with Streamlit and powered by Google's Gemini API
• Location: gemini-chatbot-streamlit/
• Key Features: Multi-turn conversations, web-based interface, session management, modern UI
• Tech Stack: Streamlit, Google Generative AI, Python
• Live Demo: Try it Live on Streamlit Cloud

📈 Stock Analysis Pro

• Description:
  Professional-grade stock market analysis dashboard with real-time data, interactive charts, and comprehensive financial insights in a modern Robinhood-style interface
• Location: stock-analysis-pro/
• Key Features: Real-time updates, interactive candlestick charts, technical indicators, comprehensive statistics, responsive design
• Tech Stack: Streamlit 1.48+, yfinance, Plotly, Pandas
• Live Demo: Try Stock Analysis Pro

📱 Social Media Post Analysis Application

• Description:
  A powerful Python tool for analyzing social media posting patterns with dynamic author selection and comprehensive data visualization
• Location: social-media-analyzer/
• Key Features: CSV data analysis, dynamic author filtering, posting pattern visualization, statistical insights
• Tech Stack: Python, Pandas, Matplotlib, Streamlit

🎵 Sound Wave Analysis

• Description:
  Advanced audio analysis toolkit for processing sound waves and acoustic data with comprehensive visualization tools
• Location: sound-wave-analysis/
• Key Features: Frequency analysis, amplitude visualization, audio file processing, interactive notebooks, modular analyzer components
• Tech Stack: Python, NumPy, Matplotlib, Scipy, Librosa

🕷️ Python Web Scraper

• Description:
  A powerful and flexible web scraping tool that discovers, categorizes, and downloads digital files from websites with smart filtering
• Location: web-scrapper/
• Key Features: Multi-format support, smart file categorization, domain filtering, progress tracking, comprehensive logging
• Tech Stack: Python, Requests, BeautifulSoup4, tqdm

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� Getting Started

1. Clone the repository

   git clone https://github.com/TorresjDev/Python-Projects.git
   cd Python-Projects

2. Navigate to a specific project

   cd [project-folder-name]

3. Install dependencies

   pip install -r requirements.txt

4. Follow each project's README for specific setup instructions and usage details

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📂 Folder Structure

Python-Projects/
│
├── ai-chat-bot-google-gemini/    # AI chat bot using Google Gemini API
├── gemini-chatbot-streamlit/     # Modern conversational chatbot with Streamlit
├── social-media-analyzer/        # Social media post analysis with dynamic author selection
├── sound-wave-analysis/          # Advanced audio analysis and signal processing
├── stock-analysis-pro/           # Professional stock analysis dashboard (LIVE)
├── web-scrapper/                 # Flexible web scraping tool
└── README.md                     # This documentation

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🌟 Featured Projects

� Live Applications

• Stock Analysis Pro - Professional stock analysis dashboard
• Gemini Chatbot - AI-powered conversational interface

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🛠️ Technologies Used

• Web Frameworks: Streamlit, Flask
• Data Analysis: Pandas, NumPy, Matplotlib, Plotly
• APIs: Google Gemini AI, yfinance, CoinMarketCap
• Machine Learning: Scikit-learn, TensorFlow
• Web Scraping: BeautifulSoup4, Requests
• Audio Processing: Librosa, Scipy
• Development: Python 3.8+, Git, VS Code

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🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the project
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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�🙏 Acknowledgements

Some projects in this repository were completed as assignments for West Texas A&M University, CIDM 4310/5310, under the guidance of Dr. Cheng (Carl) Zhang.

Special thanks to:

• The open-source Python community
• Streamlit for excellent web app framework
• Google for the Gemini API
• Yahoo Finance and yfinance for stock data
• All the amazing Python libraries that make these projects possible

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📝 License

This repository is for educational and demonstration purposes only.

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