{"id":24803896,"url":"https://github.com/ritu456286/smartstockai","last_synced_at":"2026-04-18T12:04:22.076Z","repository":{"id":268695263,"uuid":"905178357","full_name":"ritu456286/SmartStockAI","owner":"ritu456286","description":"SmartStockAI uses AI to predict inventory trends, minimize deadstock risks, and provide actionable insights through advanced models and interactive visualizations.","archived":false,"fork":false,"pushed_at":"2025-02-08T19:08:56.000Z","size":219,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-25T06:45:18.370Z","etag":null,"topics":["bigquery","bigquery-ml","cloud-storage","cloudrun","cloudsql","gemini","google-maps-api"],"latest_commit_sha":null,"homepage":"https://youtu.be/6f0wTAcmsTo","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/ritu456286.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"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}},"created_at":"2024-12-18T10:09:50.000Z","updated_at":"2025-02-08T19:08:59.000Z","dependencies_parsed_at":null,"dependency_job_id":"04fb9941-e6c6-4fc3-9534-5dcf726c8645","html_url":"https://github.com/ritu456286/SmartStockAI","commit_stats":null,"previous_names":["ritu456286/smartstockai"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ritu456286%2FSmartStockAI","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ritu456286%2FSmartStockAI/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ritu456286%2FSmartStockAI/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ritu456286%2FSmartStockAI/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ritu456286","download_url":"https://codeload.github.com/ritu456286/SmartStockAI/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":245414529,"owners_count":20611364,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","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":["bigquery","bigquery-ml","cloud-storage","cloudrun","cloudsql","gemini","google-maps-api"],"created_at":"2025-01-30T06:13:46.695Z","updated_at":"2026-04-18T12:04:21.638Z","avatar_url":"https://github.com/ritu456286.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# SmartStockAI: Predictive Inventory \u0026 Deadstock Management  \r\n\r\n## 🚀 Overview\r\nSmartStockAI is an AI-driven solution that optimizes inventory management by predicting inventory trends, identifying potential deadstock risks, and generating actionable insights to minimize losses. Leveraging **advanced machine learning models** and **large language models (LLMs)**, it transforms traditional inventory management into a dynamic, data-driven process adaptable to changing market conditions and consumer behaviors.\r\n\r\n🏆 **Winner of Google Build and Blog Marathon '24**\r\n\r\n## 🌐 Live Demo\r\n🎥 **[Watch Demo Video](https://www.youtube.com/watch?v=6f0wTAcmsTo)**  \r\n🚀 **Deployment:** Initially deployed on `Cloud Run`, but due to cloud charges, it has been removed. All functionalities are showcased in the demo video.  \r\n📝 **Read the Full Story:** **[Medium Blog](https://medium.com/google-cloud/smartstockai-predictive-inventory-deadstock-management-ea8cb0556081)**\r\n\r\n---\r\n\r\n## 🔥 Features\r\n\r\n✅ **Demand Forecasting** - Uses the **ARIMA_PLUS** model in **BigQuery ML** to predict future sales and identify potential deadstock.  \r\n✅ **Unstructured Data Analysis** - Utilizes **Gemini 2.0 LLM** to extract insights from customer feedback and vendor notes.  \r\n✅ **Actionable Recommendations** - Generates strategies to **reduce waste** and **improve efficiency** in inventory management.  \r\n✅ **Interactive Visualization** - Provides dashboards via **Streamlit** and **Looker Studio** to visualize forecasts and insights.  \r\n\r\n---\r\n\r\n## 🏗️ Architecture\r\n\r\nSmartStockAI integrates multiple **Google Cloud** services for a seamless, scalable solution:\r\n\r\n- **BigQuery ML** - Implements the **ARIMA_PLUS** model for demand forecasting.\r\n- **Gemini 2.0 LLM** - Processes unstructured data to generate insights.\r\n- **Cloud SQL** - Stores structured relational data.\r\n- **BigQuery** - Serves as the core analytics engine for large-scale data processing.\r\n- **Streamlit** - Provides an interactive frontend for data visualization.\r\n- **Looker Studio** - Offers collaborative dashboards for deeper analysis.\r\n\r\n![Architecture Diagram](https://your-architecture-image-link.com) \u003c!-- Add an architecture diagram if available --\u003e\r\n\r\n---\r\n\r\n## 📌 Prerequisites\r\n\r\nBefore implementing SmartStockAI, ensure you have the following:\r\n\r\n### 🔹 **Google Cloud Platform (GCP) Services**\r\n- Cloud Storage  \r\n- Cloud SQL  \r\n- BigQuery  \r\n- BigQuery ML  \r\n- Looker Studio  \r\n\r\n### 🔹 **Machine Learning Models**\r\n- ARIMA_PLUS Model (for demand forecasting)\r\n\r\n### 🔹 **APIs \u0026 Tools**\r\n- **Gemini 2.0 API** (for unstructured data analysis)\r\n- **Streamlit** (for visualization)\r\n\r\n### 🔹 **Required Knowledge**\r\n- SQL Queries  \r\n- Machine Learning Concepts  \r\n- Python Programming  \r\n- Google Cloud Platform (GCP)  \r\n\r\n---\r\n\r\n## ⚡ Getting Started\r\n\r\nFollow these steps to set up and run SmartStockAI:\r\n\r\n1️⃣ **Data Acquisition** - Obtain inventory data (e.g., the Nike Sales dataset from Kaggle).  \r\n2️⃣ **Data Upload** - Upload the dataset to **Google Cloud Storage**.  \r\n3️⃣ **BigQuery Integration** - Enable **BigQuery** and connect it to **Cloud SQL** for real-time data retrieval.  \r\n4️⃣ **Model Implementation** - Apply the **ARIMA_PLUS** model in **BigQuery ML** for demand forecasting.  \r\n5️⃣ **Unstructured Data Processing** - Integrate **Gemini 2.0 LLM** for analyzing customer feedback and vendor notes.  \r\n6️⃣ **Visualization** - Develop interactive dashboards using **Streamlit** and **Looker Studio**.  \r\n\r\n---\r\n\r\n## 📚 Resources\r\n\r\n🔗 **[BigQuery ML ARIMA_PLUS Model](https://cloud.google.com/vertex-ai/docs/tabular-data/forecasting-arima/overview)**  \r\n🔗 **[Google Cloud Storage Documentation](https://cloud.google.com/bigquery/docs/loading-data-cloud-storage-csv)**  \r\n🔗 **[Looker Studio Documentation](https://lookerstudio.google.com/)**  \r\n\r\n---\r\n\r\n## 🙌 Acknowledgments\r\n\r\nA huge thanks to **Code Vipassana** for organizing the in-person event! 🎉\r\n\r\n---\r\n\r\n## 📜 License\r\n\r\nThis project is licensed under the **MIT License**. See the [LICENSE](LICENSE) file for details.\r\n\r\n---\r\n\r\n### 📩 Have Questions?\r\nFeel free to **open an issue** or **reach out** via [LinkedIn/Twitter/GitHub Discussions]!\r\n\r\n---\r\n\r\n⭐ **If you find this project useful, don't forget to give it a star!** ⭐\r\n\r\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fritu456286%2Fsmartstockai","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fritu456286%2Fsmartstockai","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fritu456286%2Fsmartstockai/lists"}