README.md

metadata

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):

python -m venv venv
source venv/bin/activate  # On Windows use: venv\Scripts\activate

2. Install required packages:

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:

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