PR #64: Youtube Relevance Finder

Improve Gemini error handling and document fallback behaviorYoutube relevance finder
Merge pull request #64 from asabo-dev/youtube-relevance-finder

Author: iamwatchdogs

Youtube_video_finder_using_geminillm/README.md

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+# macOS
+.DS_Store
+
+# Environment variables
+.env
+.env.*
+
+# Python
+__pycache__/
+*.py[cod]
+
+# Virtual environments
+venv/
+.venv/
+
+# IDEs
+.vscode/
+.idea/
+
+# Logs
+*.log

youtube-relevance-finder/.gitignore

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+# YouTube Relevance Finder with Gemini AI (Enhanced Version)
+
+This Python application searches YouTube for recent videos based on a user query
+and ranks them by relevance using Google’s Gemini AI model and the YouTube Data API.
+
+---
+
+## 🔍 Features
+
+- Searches YouTube for videos published within the last 14 days
+- Filters videos by duration (10–60 minutes)
+- Uses Gemini AI to score video title relevance to a search query
+- Gracefully falls back to a default score if Gemini API calls fail
+- Prints ranked video titles with relevance scores and publication dates
+
+---
+
+## 🆕 Differences from the Original Implementation
+
+This version introduces several improvements compared to the original source code:
+
+- **Graceful Gemini API fallback**  
+  When the Gemini API is unavailable, rate-limited, or returns an unexpected
+  response, the application assigns a default relevance score instead of failing.
+
+- **Cleaner error handling**  
+  SDK and API-related errors are handled internally and surfaced as clear,
+  user-friendly warning messages.
+
+- **Improved project structure**  
+  The application logic is organized into dedicated classes for:
+  - Time utilities
+  - YouTube video extraction and filtering
+  - Gemini-based scoring
+  - Video ranking and processing
+
+- **Explicit documentation of limitations**  
+  Known API constraints and fallback behavior are documented to reflect
+  real-world usage conditions.
+
+These changes make the project more robust and suitable for learning and experimentation.
+
+---
+
+## 🛠️ Setup
+
+### 1. Clone the repository
+
+```bash
+git clone https://github.com/yourusername/your-repo-name.git
+cd your-repo-name
+
+2. Install dependencies
+pip install google-api-python-client google-generativeai
+
+3. Set up environment variables
+Create a .env file or export in your terminal:
+export YT_API_KEY=your_youtube_api_key
+export GEMINI_API_KEY=your_gemini_api_key
+
+🚀 Usage
+Run the script:
+python app.py
+
+You will be prompted to enter a search query.
+The script will then display a list of the top relevant YouTube videos.
+
+📄 Example Output
+Enter your search: Brazilian Jiu Jitsu
+Filtered 5 videos based on criteria.
+[Warning] Gemini API call failed. Falling back to default relevance score.
+
+#1
+Title: The New Face of Brazilian Jiu-Jitsu
+Score: 5.0
+Published: 2026-01-08T16:16:56Z
+
+📌 Notes & Known Limitations
+Valid API keys are required for:
+YouTube Data API v3
+Google Gemini API
+Gemini API usage is subject to quota limits and model availability
+When Gemini scoring fails, a default relevance score is applied so the
+application can continue running without interruption
+This fallback behavior is intentional and documented for learning purposes
+
+🎯 Purpose of This Project
+This project was contributed as part of an open-source learning journey to demonstrate:
+API integration with third-party services
+Defensive programming and graceful error handling
+Clean project organization and documentation
+Real-world constraints when working with LLM APIs