How jamipavankumar5435-oss/crop-yield-prediction works
This repository provides crop-yield prediction through two independent user interfaces: a Gradio app and a Streamlit app. Users enter farm, crop, soil, and weather details, select a model, and receive a yield estimate; supported models also produce SHAP feature explanations. Both apps load a shared serialized model package. The source excerpts show the complete prediction workflows, but do not describe how the package was trained or evaluated, so those stages are not represented.
Main entry points: app.py Yield prediction · streamlit_app.py Streamlit form · crop_yield_models.pkl Serialized models
Components 10
User interfaces
- Gradio form
app.py - Streamlit form
streamlit_app.py
Prediction and explanation
- Input preparation
app.py - Yield prediction
app.py - SHAP explanation
app.py - Input preparation
streamlit_app.py - Yield prediction
streamlit_app.py - SHAP explanation
streamlit_app.py
Model assets
- Serialized models
crop_yield_models.pkl
Other components
- Farm user
Connections 15
- Farm user → Gradio form: enters details
app.py - Farm user → Streamlit form: enters details
streamlit_app.py - Gradio form → Input preparation: submits inputs
app.py - Input preparation → Yield prediction: returns features
app.py - Gradio form → Yield prediction: calls prediction
app.py - Yield prediction → Serialized models: loads models
app.py - Yield prediction → SHAP explanation: explains prediction
app.py - Yield prediction → Gradio form: returns result
app.py - SHAP explanation → Gradio form: returns explanation
app.py - Streamlit form → Input preparation: builds features
streamlit_app.py - Streamlit form → Yield prediction: predicts on submit
streamlit_app.py - Yield prediction → Serialized models: loads models
streamlit_app.py - Yield prediction → SHAP explanation: explains prediction
streamlit_app.py - Yield prediction → Streamlit form: shows result
streamlit_app.py - SHAP explanation → Streamlit form: shows explanation
streamlit_app.py