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HaritSetu offers a smart and sustainable alternative — helping farmers list their residues for sale and buyers to reuse them in industries like biofuel, compost, and packaging.\n\nThe platform features:\n- AI-powered price prediction (trained on 1000+ real records)\n- Clean, mobile-friendly UI designed for rural India\n- Multilingual chatbot (KrishiMitra) with Hindi, English, and Hinglish support\n- Filters for buyers to search by location, residue type, and month\n\nDeveloped under the theme **Sustainable Tech and Climate Innovation**, HaritSetu empowers farming communities with technology, encourages zero-waste practices, and promotes green entrepreneurship — ensuring smarter selling with *no smoke in between*.\n\n## Tech Stack\n\nThe HaritSetu platform combines AI, web development, real-time database sync, and chatbot integration to deliver a complete eco-tech solution.\n\n### Frontend\n\n- **React.js** – for creating a responsive and interactive user interface  \n- **Vite** – for lightning-fast builds and modular development  \n- **Tailwind CSS** – for utility-first, mobile-friendly styling  \n- **React Router** – for smooth navigation across pages  \n- **Axios** – to handle API requests from frontend to backend  \n- **LottieFiles** – for lightweight UI animations  \n- **Deployment** – Hosted on **Vercel**\n\n### Backend\n\n- **Flask (Python)** – primary backend for routing, AI model integration, and chatbot logic  \n- **Node.js + Express.js** – used for syncing forms and Firebase integration  \n- **Firebase Admin SDK** – to handle authentication, secure storage, and backend logic  \n- **Firebase Realtime Database** – for dynamic syncing of chatbot and form data  \n- **Flask-CORS** – to handle cross-origin communication between frontend and backend  \n- **RESTful APIs** – for frontend-backend interaction  \n- **Deployment** – Hosted on **Render**\n\n### AI/ML Model\n\n- **Scikit-learn** – for building a regression-based crop residue price prediction model  \n- **Pandas, NumPy** – for preprocessing and data handling  \n- **Matplotlib, Seaborn** – for model evaluation and data visualization  \n- **Training Data** – 1000+ real-world entries  \n- **Security** – Logic to prevent exploitation or unrealistic pricing\n\n### Chatbot (KrishiMitra)\n\n- **Flask-based NLP engine** – custom-built chatbot logic  \n- **Multilingual Support** – understands and responds in English, Hindi, and Hinglish  \n- **Context-Aware Replies** – guides users on registration, pricing, residue types, etc.  \n- **Firebase Integration** – for real-time chatbot storage and conversation memory\n\n### Tools \u0026 Dev Environment\n\n- **Git \u0026 GitHub** – for version control and collaboration  \n- **Postman** – for API testing  \n- **VS Code** – for development  \n\n## AI Model – Smart Pricing with Fairness\n\nHaritSetu uses a regression-based machine learning model to **predict the price of crop residues** based on real-world agricultural factors. The goal is to ensure that farmers receive **fair, data-driven pricing** instead of relying on middlemen or guesswork.\n\n### Key Details:\n- **Algorithm:** Regression using Scikit-learn\n- **Training Data:** 1000+ real entries from open agricultural sources\n- **Input Parameters:** Crop Type, Residue Type, State, District, Month, and Quantity (kg)\n- **Output:** Predicted Price per Kg (INR)\n- **Libraries Used:** Scikit-learn, Pandas, NumPy, Matplotlib, Seaborn\n- **Security Measures:** The model includes logic to avoid unrealistic predictions and manipulation\n\nThis model empowers farmers to **negotiate confidently**, reduces dependency on intermediaries, and ensures more **transparent pricing** in the rural supply chain.\n\n---\n\n## KrishiMitra – The Multilingual Virtual Assistant\n\n**KrishiMitra** is an in-built chatbot designed to provide **simple, real-time support** to both farmers and buyers across language barriers. It helps users understand and navigate the platform without needing external help.\n\n### Core Features:\n- **Built with Flask** – Lightweight and custom-built NLP engine\n- **Multilingual Input** – Supports English, Hindi, and Hinglish\n- **User Tasks Handled:**\n  - How to list a residue\n  - Crop/residue usage and pricing info\n  - Login/registration help\n  - Understanding AI predictions\n- **Firebase Integration** – Stores chat history and enables future mobile scalability\n- **Context-Aware Replies** – Tailored responses depending on user type (farmer/buyer)\n\nKrishiMitra makes HaritSetu **accessible to non-technical rural users** and builds trust through **language, relevance, and instant answers**.\n\n\n## About the Team – Pixel Protocol\n\nWe are **Pixel Protocol** — a bold, all-women tech team from  \n**Indira Gandhi Delhi Technical University for Women (IGDTUW), Delhi**  \ncommitted to turning ideas into impact.\n\nWhat brings us together isn’t just code — it's our shared vision to build technology that **matters**.\n\nFueled by curiosity, compassion, and creativity, we believe in crafting solutions that are not just innovative, but also **inclusive**, **sustainable**, and **rooted in real-world needs**.\n\n**HaritSetu** is more than a project — it’s our mission to bridge the gap between rural challenges and modern technology, and to prove that meaningful change begins with purpose-driven innovation.\n\n##  Quick Start\n\n###  Prerequisites\n\n- [Node.js](https://nodejs.org/) (v16+)\n- [Python](https://www.python.org/) (v3.8+)\n- [Git](https://git-scm.com/)\n\n---\n\n###  Installation\n\n#### 1. Clone the Repository\n\n```bash\ngit clone https://github.com/anchal405/Harit_Setu\ncd haritsetu\n```\n### 2. Frontend Setup\n\nInstall dependencies and start the frontend server:\n\n```bash\nnpm install\nnpm run dev\n```\n#### 3. Backend Setup\n\nNavigate to the backend folder, create a virtual environment, and start the backend server:\n\n```bash\ncd backend\npython -m venv venv\nsource venv/bin/activate  # On Windows: venv\\Scripts\\activate\npip install -r requirements.txt\nuvicorn main:app --reload --port 8000\n```\n#### 4. Environment Variables\n\nCreate a `.env` file in the root directory and add the following:\n\n```env\nVITE_API_URL=http://localhost:8000\nVITE_FLASK_URL=http://localhost:5000\n```\n### 5. Running the Application\n\nOpen two terminals and run the following commands:\n\n**Terminal 1 – Frontend**\n```bash\nnpm run dev\n```\n**Terminal 2 – Backend**\n```bash\ncd backend\nsource venv/bin/activate\nuvicorn main:app --reload --port 8000\n```\n### 6. Deployment\n\n#### Frontend (Vercel)\n\nTo deploy the frontend on Vercel:\n\n```bash\nvercel --prod\n```\n#### Backend (Render)\n\nTo deploy the backend on Render:\n\n```bash\npip freeze \u003e requirements.txt\n\n\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshantikumarigautam%2Fharitsetu","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fshantikumarigautam%2Fharitsetu","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshantikumarigautam%2Fharitsetu/lists"}