---
title: Soil Resistivity Prediction
emoji: 🚗
colorFrom: blue
colorTo: green
sdk: streamlit
sdk_version: "1.29.0"
app_file: app.py
pinned: false
---

# Resistivity Prediction App

This is a Streamlit web application for predicting resistivity based on input features. The app uses a trained deep learning model with attention mechanism and provides SHAP value explanations for predictions.

## Setup Instructions

1. Create a virtual environment (recommended):
```bash
python -m venv venv
source venv/bin/activate  # On Windows use: venv\Scripts\activate
```

2. Install required packages:
```bash
pip install -r requirements.txt
```

3. Place the following files in the same directory:
- `model.pth` (trained model file)
- `data.xlsx` (dataset file with features and target)

## Running the App

To run the app, use the following command:
```bash
streamlit run app.py
```

The app will be available at http://localhost:8501 by default.

## Usage

1. Enter values for each feature using the input fields
2. Click the "Predict" button
3. View the prediction result and SHAP value explanation

## Files Description

- `app.py`: Main Streamlit application file
- `predict.py`: Contains model architecture and prediction functions
- `requirements.txt`: List of required Python packages
- `model.pth`: Trained model weights (not included, must be added)
- `data.xlsx`: Dataset file (not included, must be added)

## Model Architecture

The model uses a TabularTransformer architecture with:
- Feature embedding layer
- Multi-head attention mechanism
- Fully connected layers for prediction

## Requirements

- Python 3.8+
- Required packages listed in requirements.txt