Update README.md

README.md

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-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 🌱 CropSense — Crop Recommendation System
+
+A Flask web application that predicts the most suitable crop to grow based on soil nutrient parameters. Built with Scikit-learn and deployed with a clean, responsive UI.
+
+[![Python](https://img.shields.io/badge/Python-3.8%2B-blue?logo=python)](https://python.org)
+[![Flask](https://img.shields.io/badge/Flask-2.3%2B-black?logo=flask)](https://flask.palletsprojects.com)
+[![Scikit-learn](https://img.shields.io/badge/Scikit--learn-1.3%2B-orange?logo=scikit-learn)](https://scikit-learn.org)
+[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](LICENSE)
+
+---
+
+Live Demo: https://crop-predict-wgrr.onrender.com/
+
+Alternate Link: https://lovnishverma-croppredict.hf.space/
+
+## 📌 Overview
+
+CropSense takes 11 soil parameters as input and recommends the most suitable crop using a **Logistic Regression** model trained on the [Crop dataset](https://raw.githubusercontent.com/lovnishverma/datasets/refs/heads/main/Crop.csv). It also returns the **top-3 predictions with confidence percentages**.
+
+This project was developed as a teaching artifact for the **IndiaAI Foundations of Artificial Intelligence** programme at **NIELIT Ropar**, demonstrating the end-to-end ML workflow: data → model → deployment.
+
+---
+
+## 🗂️ Project Structure
+
+```
+crop-predict/
+├── app.py                          # Flask application
+├── classification.ipynb            # Model training notebook (Colab)
+├── crop_recommendation_model.joblib  # Saved trained model
+├── requirements.txt
+├── static/
+│   └── css/                        # Stylesheets
+└── templates/
+    └── index.html                  # Frontend UI
+```
+
+---
+
+## 🧪 Input Features
+
+The model uses 11 soil parameters:
+
+| Feature | Description          | Unit   |
+|---------|----------------------|--------|
+| N       | Nitrogen             | kg/ha  |
+| P       | Phosphorus           | kg/ha  |
+| K       | Potassium            | kg/ha  |
+| ph      | pH Level             | —      |
+| EC      | Electrical Conductivity | dS/m |
+| S       | Sulfur               | mg/kg  |
+| Cu      | Copper               | mg/kg  |
+| Fe      | Iron                 | mg/kg  |
+| Mn      | Manganese            | mg/kg  |
+| Zn      | Zinc                 | mg/kg  |
+| B       | Boron                | mg/kg  |
+
+**Sample values** (pomegranate): `175, 36, 216, 5.9, 0.15, 0.28, 15.69, 114.20, 56.87, 31.28, 28.62`
+
+---
+
+## ⚙️ ML Pipeline
+
+```
+Dataset (Crop.csv)
+    ↓
+Feature/Target Split (X = 11 soil params, y = crop label)
+    ↓
+Train-Test Split (80/20, random_state=42)
+    ↓
+Logistic Regression (sklearn)
+    ↓
+Evaluation (accuracy_score, classification_report, confusion matrix)
+    ↓
+Model Serialization (joblib → .joblib file)
+    ↓
+Flask REST API (/predict)
+```
+
+See [`classification.ipynb`](classification.ipynb) for the full training walkthrough.
+
+---
+
+## 🚀 Getting Started
+
+### 1. Clone the repository
+
+```bash
+git clone https://github.com/lovnishverma/crop-predict.git
+cd crop-predict
+```
+
+### 2. Install dependencies
+
+```bash
+pip install -r requirements.txt
+```
+
+### 3. Run the app
+
+```bash
+python app.py
+```
+
+Open your browser at **http://127.0.0.1:5000**
+
+> **Note:** The pre-trained `crop_recommendation_model.joblib` is already included. If you want to retrain, open `classification.ipynb` in Google Colab and run all cells — it will regenerate the `.joblib` file.
+
+---
+
+## 🌐 API Reference
+
+### `POST /predict`
+
+Accepts `multipart/form-data` with all 11 soil parameters.
+
+**Request (curl example):**
+
+```bash
+curl -X POST http://127.0.0.1:5000/predict \
+  -F "N=175" -F "P=36" -F "K=216" -F "ph=5.9" -F "EC=0.15" \
+  -F "S=0.28" -F "Cu=15.69" -F "Fe=114.20" -F "Mn=56.87" \
+  -F "Zn=31.28" -F "B=28.62"
+```
+
+**Response:**
+
+```json
+{
+  "crop": "pomegranate",
+  "top3": [
+    { "crop": "pomegranate", "prob": 87.3 },
+    { "crop": "mango",       "prob": 7.1  },
+    { "crop": "grapes",      "prob": 3.2  }
+  ]
+}
+```
+
+---
+
+## 📦 Requirements
+
+```
+flask>=2.3
+scikit-learn>=1.3
+pandas>=2.0
+joblib>=1.3
+numpy>=1.24
+```
+
+---
+
+## 📊 Dataset
+
+- **Source:** [`lovnishverma/datasets`](https://github.com/lovnishverma/datasets) → `Crop.csv`
+- **Features:** 11 soil nutrient columns
+- **Target:** `label` — crop name (e.g. pomegranate, wheat, rice, maize, etc.)
+
+---
+
+## 🙋 Author
+
+**Lovnish Verma**
+Project Engineer, NIELIT Ropar (Deemed University), Punjab
+
+[![GitHub](https://img.shields.io/badge/GitHub-lovnishverma-black?logo=github)](https://github.com/lovnishverma)
+[![LinkedIn](https://img.shields.io/badge/LinkedIn-lovnishverma-blue?logo=linkedin)](https://linkedin.com/in/lovnishverma)
+[![HuggingFace](https://img.shields.io/badge/HuggingFace-lovnishverma-yellow?logo=huggingface)](https://huggingface.co/lovnishverma)
+[![Portfolio](https://img.shields.io/badge/Portfolio-lovnishverma.in-green)](https://lovnishverma.in)
+
+---
+
+## 📄 License
+
+This project is licensed under the MIT License.
+
+---
+
+> Built for IndiaAI students at NIELIT Ropar · No brainrot. Just facts. 🌾
+