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https://github.com/mj-awad17/agriculture-yield-prediction-app
🌾 Agricultural Yield Prediction App: Predict crop yield using climate, soil, weather, and irrigation data. Built with Streamlit and powered by the IBM API using the Grantie-3b model. Enter parameters or use custom prompts for tailored predictions. 🌱
https://github.com/mj-awad17/agriculture-yield-prediction-app
agriculture agriculture-app predic prediction-model python streamlit streamlitapp
Last synced: 23 days ago
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🌾 Agricultural Yield Prediction App: Predict crop yield using climate, soil, weather, and irrigation data. Built with Streamlit and powered by the IBM API using the Grantie-3b model. Enter parameters or use custom prompts for tailored predictions. 🌱
- Host: GitHub
- URL: https://github.com/mj-awad17/agriculture-yield-prediction-app
- Owner: mj-awad17
- Created: 2024-08-25T00:01:52.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2024-08-26T10:07:56.000Z (5 months ago)
- Last Synced: 2024-11-03T08:42:03.805Z (2 months ago)
- Topics: agriculture, agriculture-app, predic, prediction-model, python, streamlit, streamlitapp
- Language: Python
- Homepage: https://huggingface.co/spaces/Abbas0786/Agricultural-Yield-Prediction
- Size: 69.3 KB
- Stars: 0
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# 🌾 Agricultural Yield Prediction App
Welcome to the **Agricultural Yield Prediction App**! This application leverages the power of the [**Granite-3B**](https://huggingface.co/ibm-granite/granite-3b-code-base) to predict agricultural yield based on various factors such as climate zone, region, soil type, and more. The app is built with **Streamlit**, providing an interactive and user-friendly interface for making predictions.
## 🚀 Demo
👉 [**Try the live demo here!**](https://huggingface.co/spaces/Abbas0786/Agricultural-Yield-Prediction)
## 📋 Features
- 🌎 **Climate Zone and Region:** Specify the climate zone and region of your farm to get tailored predictions.
- 🌱 **Soil and Crop Type:** Input soil type and crop variety to enhance prediction accuracy.
- 🌧️ **Weather Conditions:** Include historical weather data, current weather conditions, and soil moisture levels.
- 💧 **Irrigation and Fertilization:** Specify irrigation methods and fertilizer details.
- 🖊️ **Custom Prompts:** Use custom prompts for personalized predictions.
- 📊 **Yield Units and Prediction Period:** Define the units for yield (e.g., tons per acre) and the prediction period (e.g., weekly, monthly).
## 🛠️ Technologies Used
- **Streamlit:** A framework for building interactive web apps with Python.
- **IBM API:** Used for generating yield predictions using the Granite-3b-code-base model.
- **Python:** Backend logic and API integration.
- **Environment Variables:** For secure API key management.
## 📦 Installation
Follow these steps to set up the app on your local machine:
1. **Clone the repository:**
```bash
git clone https://github.com/yourusername/agricultural-yield-prediction-app.git
```
2. **Navigate to the project directory:**
```bash
cd agricultural-yield-prediction-app
```
3. **Install the required Python packages:**
```bash
pip install streamlit groq
```
4. **Set up your API key:**
Replace `'your_ibm_api_key_here'` in the script with your actual Ibm API key:
```python
os.environ["IBM_API_KEY"] = "your_ibm_api_key_here"
```
5. **Run the app:**
```bash
streamlit run app.py
```
## 🖥️ How to Use
1. **Open the app using the demo link provided above or run it locally using Streamlit.**
2. **Choose the input method:** Use a custom prompt or fill in parameters for a structured input.
3. **Enter the required details:** Depending on your chosen method, fill in climate zone, region, soil type, weather conditions, and other relevant fields.
4. **Click the "Predict Yield" button** to generate the yield prediction.
5. **Clear inputs** using the "Clear" button if you want to start over.
## 📷 Screenshot
## 🔑 API Key Setup
To use this app, you need a Groq API key:
1. Sign up on [IBM](https://cloud.ibm.com/) to get your API key.
2. Replace `"your_ibm_api_key_here"` in the script with your actual API key.
## 📝 License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more details.
## 🌟 Acknowledgements
- Thanks to [IBM-Watsonai](https://ibm.com/) challange for providing the API for yield prediction.
- Thanks to the [Streamlit](https://streamlit.io/) team for creating such an intuitive tool for building web apps.
## 📬 Contact
For any questions or feedback, please feel free to reach us!
- [Muhammad Jawad](https://www.linkedin.com/in/muhammad-jawad-86507b201/)
- [Muhammad Bilal](https://www.linkedin.com/in/muhammad-bilal-a75782280/)
- [Ghulam Abbas](https://www.linkedin.com/in/ghulam-abbas-310b7a302/)
- [Alisha Ashraf](https://www.linkedin.com/in/alisha-ashraf-b73404301/)
- [Hamid Raza](https://www.linkedin.com/in/hamid-raza-302baa286/)