{"id":26239442,"url":"https://github.com/codewithdinesh/trend360","last_synced_at":"2026-04-27T09:01:54.859Z","repository":{"id":273162702,"uuid":"918872921","full_name":"codewithdinesh/trend360","owner":"codewithdinesh","description":null,"archived":false,"fork":false,"pushed_at":"2025-01-19T07:34:58.000Z","size":299,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-12-27T19:21:04.664Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"https://trend360.vercel.app","language":"JavaScript","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/codewithdinesh.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,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2025-01-19T04:49:16.000Z","updated_at":"2025-01-19T07:34:59.000Z","dependencies_parsed_at":null,"dependency_job_id":"04911c7f-ca87-4284-b672-f70940e29b6e","html_url":"https://github.com/codewithdinesh/trend360","commit_stats":null,"previous_names":["codewithdinesh/trend360"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/codewithdinesh/trend360","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/codewithdinesh%2Ftrend360","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/codewithdinesh%2Ftrend360/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/codewithdinesh%2Ftrend360/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/codewithdinesh%2Ftrend360/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/codewithdinesh","download_url":"https://codeload.github.com/codewithdinesh/trend360/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/codewithdinesh%2Ftrend360/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32329466,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-26T23:26:28.701Z","status":"online","status_checked_at":"2026-04-27T02:00:06.769Z","response_time":128,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"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":[],"created_at":"2025-03-13T06:44:49.729Z","updated_at":"2026-04-27T09:01:54.852Z","avatar_url":"https://github.com/codewithdinesh.png","language":"JavaScript","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003cp align=\"center\"\u003e\n  \u003ca href=\"\" rel=\"noopener\"\u003e\n\u003cimg src=\"https://github.com/user-attachments/assets/926f0fbb-a9de-474b-916f-540fbae85985\" width=\"200\" height=\"200\" alt=\"Team Unique Logo\"\u003e\n\n\n  \u003c/a\u003e\n\u003c/p\u003e\n\u003ch3 align=\"center\"\u003eTrend360\u003c/h3\u003e\n\n\u003cdiv align=\"center\"\u003e\n\n[![Status](https://img.shields.io/badge/status-active-success.svg)]()\n[![License](https://img.shields.io/badge/license-MIT-blue.svg)](LICENSE.md)\n\n\u003c/div\u003e\n\n---\n\n\u003cp align=\"center\"\u003e AI Power Automated Research and Trigger Finder.\n    \u003cbr\u003e \n\u003c/p\u003e\n\n## 📝 Table of Contents\n\n- [Problem Statement](#problem_statement)\n- [Idea / Solution](#idea)\n- [Dependencies / Limitations](#limitations)\n- [Future Scope](#future_scope)\n- [Setting up a local environment](#getting_started)\n- [Usage](#usage)\n- [Technology Stack](#tech_stack)\n- [Contributing](../CONTRIBUTING.md)\n- [Authors](#authors)\n- [Acknowledgments](#acknowledgments)\n\n## 🧐 Problem Statement \u003ca name = \"problem_statement\"\u003e\u003c/a\u003e\n\nIDEAL:\nThe ideal scenario is to have a seamless and automated process for gathering and analyzing data from diverse sources, providing marketers with actionable insights to create highly effective, user-centric advertisements. The tool would allow marketers to quickly identify user pain points, analyze competitor strategies, and craft data-driven hooks and CTAs, all within a unified, intuitive interface.\n\nREALITY:\nCurrently, the process of researching and analyzing user pain points and competitor ads is highly manual, time-consuming, and fragmented. Marketers need to access multiple platforms such as Google, YouTube, Reddit, and Quora individually. Extracting and analyzing data from these platforms requires significant effort, often lacking cohesion and actionable outcomes.\n\nCONSEQUENCES:\nIf this problem is not resolved, marketers will continue to face inefficiencies, leading to increased time and resource costs. This hinders the ability to create impactful ads quickly and stay competitive. Without proper insights, ad campaigns risk poor engagement, resulting in lost revenue opportunities and diminished ROI.\n\n## 💡 Idea / Solution \u003ca name = \"idea\"\u003e\u003c/a\u003e\n\nThe proposed solution is ART Finder, a web application designed to automate and streamline the research phase of ad creation. It utilizes advanced data scraping, transcription, and AI-based analysis to provide marketers with actionable insights. Key features include:\n\nAutomated Research: Scrapes and analyzes data from platforms like Google, YouTube, Reddit, Quora, and app reviews.\nCompetitor Analysis: Examines competitor ads to identify high-performing hooks, CTAs, and strategies.\nActionable Insights: Generates recommendations for hooks, CTAs, and user-centric solutions.\nReference Dashboard: Embeds analyzed ads, clickable links, and visualized insights for validation.\n\n## ⛓️ Dependencies / Limitations \u003ca name = \"limitations\"\u003e\u003c/a\u003e\n\nDependencies:\n\nAssembly AI: For transcription of audio and video data.\nGemini AI and GPT-4o: For context analysis and generating actionable insights.\nSelenium and BeautifulSoup: For web scraping competitor ads and content.\nAstraDB: For scalable database management.\nLangflow and FastAPI: For managing LLM pipelines and APIs.\n\nLimitations:\n\nReal-Time Data Access: The accuracy of scraped data depends on platform availability and access permissions, which may vary.\nScalability of Analysis: Handling extremely large datasets may require optimization for performance.\nRegulatory Constraints: Adhering to data privacy laws like GDPR when scraping and storing data.\nScope of AI Models: Models may occasionally generate insights that require manual verification for accuracy.\n\n## 🚀 Future Scope \u003ca name = \"future_scope\"\u003e\u003c/a\u003e\n\nEnhance the system to include real-time ad performance tracking and dynamic updates.\nExpand the scraping capabilities to support additional platforms and new data types.\nIntegrate predictive analytics to forecast ad campaign success based on historical trends.\nAdd multilingual support for global ad creation.\n\n## 🏁 Getting Started \u003ca name = \"getting_started\"\u003e\u003c/a\u003e\n\nPrerequisites\n\nNode.js installed on your machine.\nPython (v3.9 or higher) for backend services.\nDatabase credentials for AstraDB setup.\nInstalling\nClone the repository:\n\nbash\nCopy\nEdit\ngit clone https://github.com/codewithdinesh/trend360.git\ncd trend360\nInstall dependencies for frontend:\n\nbash\nCopy\nEdit\ncd frontend\nnpm install\nInstall dependencies for backend:\n\nbash\nCopy\nEdit\ncd backend\npip install -r requirements.txt\nStart the frontend and backend servers:\n\nbash\nCopy\nEdit\nnpm run dev  # For frontend\nuvicorn main:app --reload  # For backend\n\n\n## 🎈 Usage \u003ca name=\"usage\"\u003e\u003c/a\u003e\n\nInput a topic and brand guidelines in the dashboard.\nBrowse through the generated insights, hooks, CTAs, and trends.\nValidate findings using embedded media and clickable links.\nDownload reports or share insights with your team.\n\n## ⛏️ Built With \u003ca name = \"tech_stack\"\u003e\u003c/a\u003e\n\nNext.js - Frontend Framework\nShadcn - UI Components\nTailwind CSS - Styling\nAstraDB - Database\nLangflow and FastAPI - Backend Pipelines and APIs\nAssembly and Gemini AI - Transcription and Analysis Models\n\n## ✍️ Authors \u003ca name = \"authors\"\u003e\u003c/a\u003e\n\n@abhishek-03103 , @mayankmehta8 , @codewithdinesh , @sshalgar are the contributors for the project.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcodewithdinesh%2Ftrend360","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcodewithdinesh%2Ftrend360","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcodewithdinesh%2Ftrend360/lists"}