{"id":32512845,"url":"https://github.com/barbaraeguche/pyrocast","last_synced_at":"2026-04-08T16:02:03.584Z","repository":{"id":275411754,"uuid":"926004981","full_name":"barbaraeguche/pyrocast","owner":"barbaraeguche","description":"🚒 a proactive wildfire prediction \u0026 analysis built with react \u0026 flask.","archived":false,"fork":false,"pushed_at":"2025-10-17T00:59:55.000Z","size":123188,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-10-27T22:47:00.464Z","etag":null,"topics":["ai","flask","ml","pandas","react","scikit-learn","vite"],"latest_commit_sha":null,"homepage":"https://pyrocast.vercel.app","language":"TypeScript","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/barbaraeguche.png","metadata":{"files":{"readme":"readme.md","changelog":null,"contributing":null,"funding":null,"license":"license","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2025-02-02T10:04:01.000Z","updated_at":"2025-10-17T01:44:32.000Z","dependencies_parsed_at":null,"dependency_job_id":"a6a08a21-15af-4c54-8df3-33638af833df","html_url":"https://github.com/barbaraeguche/pyrocast","commit_stats":null,"previous_names":["barbaraeguche/wild-watch","barbaraeguche/pyrocast"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/barbaraeguche/pyrocast","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/barbaraeguche%2Fpyrocast","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/barbaraeguche%2Fpyrocast/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/barbaraeguche%2Fpyrocast/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/barbaraeguche%2Fpyrocast/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/barbaraeguche","download_url":"https://codeload.github.com/barbaraeguche/pyrocast/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/barbaraeguche%2Fpyrocast/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31562697,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-08T14:31:17.711Z","status":"ssl_error","status_checked_at":"2026-04-08T14:31:17.202Z","response_time":54,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["ai","flask","ml","pandas","react","scikit-learn","vite"],"created_at":"2025-10-27T22:46:59.294Z","updated_at":"2026-04-08T16:02:03.565Z","avatar_url":"https://github.com/barbaraeguche.png","language":"TypeScript","funding_links":[],"categories":[],"sub_categories":[],"readme":"# pyrocast 🚒\na real-time wildfire analysis and prediction dashboard that helps monitor, analyze, and predict wildfire incidents in \nquebec using machine learning built for [**conuhacks-ix**](https://drive.google.com/file/d/1THw9CLQd4ynGZGwxXhtVPb1ZpVTQxTZ8/view) as part of the [**sap**](https://www.sap.com/canada/index.html) task.  \n\n## tech stack ✨\n- **frontend:** vite + react with tailwind css and framer motion for smooth animations\n- **backend:** flask with scikit-learn for ml predictions\n- **data processing:** pandas for data manipulation and analysis\n- **deployment:** frontend hosted on vercel; backend deployed on render.\n\n## features 👾\n- **smart file upload:** handles multiple csv files with validation and progress tracking\n- **resource management:** tracks firefighting resources and optimizes their allocation\n- **incident analysis:** comprehensive reporting of fire incidents with severity levels\n- **risk prediction:** machine learning-based prediction of future fire risks using environmental data\n- **geospatial visualization:** displays incident locations and resources on interactive maps using latitude and longitude coordinates\n- **cost analysis:** detailed breakdown of operational and damage costs\n- **interactive dashboard:** real-time visualization of fire incident data and predictions\n\n## 💭 what I learned\n- **ml implementation:** building and deploying a random forest classifier for fire risk prediction\n- **resource optimization:** developing algorithms for efficient firefighting resource allocation\n- **data processing:** handling and analyzing complex environmental and incident data\n- **google maps integration:** implementing the google maps api to create interactive location-based features\n\n## limitations 🚨\n- **data dependencies:** requires specific csv file formats for proper functionality\n- **resource constraints:** fixed resource allocation limits for firefighting units\n- **model scope:** predictions limited to available environmental parameters\n\n## improvements 🌱\n- **real-time updates:** implement websocket for live data updates\n- **advanced ml models:** explore deep learning models for improved predictions\n- **resource scheduling:** add dynamic resource allocation optimization\n- **historical analysis:** include trend analysis and seasonal pattern detection\n\n## .env file 📄\nthis project requires `.env` files for both the server and client, located in their respective folders. rename the \n`.env.example` file in each folder to `.env`, and update it with the necessary values. ensure these files are configured\nproperly and not committed to version control.\n\n## running the project 🏁\nto get the project up and running on your local machine, follow these steps:\n\n- **ensure [python](https://www.python.org/downloads/) and [node.js](https://nodejs.org/en) are installed.**\n1. **clone the repository:**\n```bash\ngit clone https://github.com/barbaraeguche/pyrocast.git\n```\n\n2. **navigate to the project directory:**\n```bash\ncd pyrocast\n```\n\n3. **run the backend:**\n    1. **navigate to server directory:**\n   ```bash\n   cd server\n   ```\n    2. **install and activate virtual environment:**\n   ```bash\n   python3 -m venv venv\n   source ./venv/bin/activate\n   pip install -r requirements.txt\n   ```\n    3. **run the flask app:**\n   ```bash\n   python3 app.py\n   ```\n    4. open [http://127.0.0.1:5000](http://127.0.0.1:5000) with your browser.\n\n4. **run the frontend:**\n    1. **navigate to client directory:**\n   ```bash\n   cd client\n   ```\n    2. **install dependencies:**\n   ```bash\n   pnpm install\n   ```\n    3. **start the development server:**\n   ```bash\n   pnpm run dev\n   ```\n    4. open [http://localhost:5173/](http://localhost:5173/) with your browser.\n\n## preview 📸\nhttps://github.com/user-attachments/assets/46a4d3d0-05e3-4b80-a85d-f1a3e45a0eca\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbarbaraeguche%2Fpyrocast","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbarbaraeguche%2Fpyrocast","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbarbaraeguche%2Fpyrocast/lists"}