Update read me and delete notebooks

README.md

@@ -20,14 +20,46 @@ tags:
 An efficient model to detect chainsaw activity in forest soundscapes using spectral and cepstral audio features. The model is designed for environmental conservation and is based on a LightGBM classifier, capable of low-energy inference on both CPU and GPU devices. This repository provides the complete code and configuration for feature extraction, model implementation, and deployment.
 
 ## Installation
-
-To use the model, clone the repository and install the dependencies:
-
-```bash
+You can install and use the model in two different ways:
+### Option 1: Clone the repository
+To download the entire repository containing the code, model, and associated files, follow these steps:
+``` bash
 git clone https://huggingface.co/tlmk22/QuefrencyGuardian
 cd QuefrencyGuardian
 pip install -r requirements.txt
 ```
+Once installed, you can directly import the files into your existing project and use the model.
+### Option 2: Dynamically load from the Hub
+If you only want to download the required files to use the model (without cloning the full repository), you can use the `hf_hub_download` function provided by Hugging Face. This method downloads only what is necessary directly from the Hub.
+Here's an example:
+``` python
+from huggingface_hub import hf_hub_download
+import importlib.util
+
+# Specify the repository
+repo_id = "tlmk22/QuefrencyGuardian"
+
+# Download the Python file containing the model class
+model_path = hf_hub_download(repo_id=repo_id, filename="model.py")
+
+# Dynamically load the class from the downloaded file
+spec = importlib.util.spec_from_file_location("model", model_path)
+model_module = importlib.util.module_from_spec(spec)
+spec.loader.exec_module(model_module)
+
+# Import the FastModelHuggingFace class
+FastModelHuggingFace = model_module.FastModelHuggingFace
+
+# Load the pre-trained model
+fast_model = FastModelHuggingFace.from_pretrained(repo_id)
+
+# Perform predictions
+result = model.predict("path/to/audio.wav")
+map_labels = {0: "chainsaw", 1: "environment"}
+print(f"Prediction Result: {map_labels[result[0]]}")
+```
+Depending on your needs, you can either clone the repository for a full installation or use Hugging Face's dynamic download functionalities for lightweight and direct usage.
+
 
 ## Model Overview
 
@@ -45,39 +77,12 @@ Key model parameters are included in `model/lgbm_params.json`.
 
 ## Usage
 
-### Load the Model and Parameters
-
-```python
-import json
-from fast_model import FastModel
-
-# Load parameters
-with open("model/features.json", "r") as f:
-    features = json.load(f)
-
-with open("model/lgbm_params.json", "r") as f:
-    lgbm_params = json.load(f)
-
-# Initialize the model
-model = FastModel(
-    feature_params=features,
-    lgbm_params=lgbm_params,
-    model_file="model/model.txt",  # Path to the serialized model file
-    device="cuda", 
-)
-
-# Predict on a Dataset
-from datasets import load_dataset
-dataset = load_dataset("rfcx/frugalai")
-predictions = model.predict(dataset["test"])
-print(predictions)
-```
+Two example scripts demonstrating how to use the repository or the model downloaded from Hugging Face are available in the `examples` directory.
 
 ### Performance
 
-- **Accuracy**: 95% on the test set with a 4.5% FPR at the default threshold.
-- **Low-Energy Mode**: Using only 1 second of audio inference reduces energy consumption by 50%, at the cost of ~1% accuracy.
-- **Environmental Impact**: Inference energy consumption is **0.21 Wh**, tracked using CodeCarbon.
+- **Accuracy**: Achieved 95% on the test set with a 4.5% FPR at the default threshold during the challenge, where this model won first place.
+- **Environmental Impact**: Inference energy consumption was measured at **0.21 Wh**, tracked using CodeCarbon. This metric is dependent on the challenge's infrastructure, as the code was run within a Docker container provided by the platform.
 
 ### License
 

examples/example_usage_fastmodel_hf.py

@@ -14,7 +14,7 @@ fast_model = FastModelHuggingFace.from_pretrained(repo_id)
 
 # Perform predictions for a single WAV file
 map_labels = {0: "chainsaw", 1: "environment"}
-wav_prediction = fast_model.predict("wav_example/chainsaw.wav", device="cpu")
+wav_prediction = fast_model.predict("chainsaw.wav", device="cpu")
 print(f"Prediction : {map_labels[wav_prediction[0]]}")
 
 # Example: predicting on a Hugging Face dataset