README.md

---
title: AgriPredict
emoji: 🌾
colorFrom: green
colorTo: yellow
sdk: streamlit
sdk_version: "1.49.1"
app_file: streamlit_app.py
pinned: false
---

# AgriPredict (Refactor)

AgriPredict is a refactored, modular, and production-ready version of the original Streamlit-based price forecasting dashboard.  
This version focuses on better structure, maintainability, and performance.

---

## 📁 Project Structure

```
src/agri_predict/
│
├── config.py        # Environment variables, MongoDB client setup
├── constants.py     # Shared constant mappings (states, markets, crops etc.)
├── features.py      # Feature engineering functions
├── data.py          # Data access, scraping, cleaning, preprocessing
├── models.py        # Model training, forecasting, grid search
├── plotting.py      # Plot utilities, downloads
└── utils.py         # Authentication + general helpers

streamlit_app.py     # Streamlit UI entrypoint
requirements.txt     # Python dependencies
```

---

## ▶️ Run Locally

### 1. Create and activate a virtual environment:

```bash
python -m venv .venv
source .venv/bin/activate
```

### 2. Install dependencies:

```bash
pip install -r requirements.txt
```

### 3. Set up environment variables

Create a `.env` file in the **project root**:

```
MONGO_URI=your_mongo_connection_string
```

### 4. Start the Streamlit app:

```bash
streamlit run streamlit_app.py
```

---

## 🚀 Deploy on Hugging Face Spaces

This README already includes the required HF metadata block at the top.  
Hugging Face Spaces will automatically run:

```
streamlit run streamlit_app.py
```

No additional configuration is needed.

---