Delete the second example

README.md

@@ -106,7 +106,7 @@ Two example scripts demonstrating how to use the repository or the model downloa
 
 ### Performance
 
-- **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.
+- **Accuracy**: Achieved 95% on the test set with a 4.5% FPR at the default threshold during the challenge.
 - **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 executed within a Docker container provided by the platform.
 
 ---

examples/example_usage_fastmodel_hf.py

@@ -1,26 +0,0 @@
-from datasets import load_dataset
-from huggingface_hub import hf_hub_download
-from sklearn.metrics import accuracy_score
-import importlib.util
-
-repo_id = "tlmk22/QuefrencyGuardian"
-model_file = "model.py"
-model_path = hf_hub_download(repo_id=repo_id, filename=model_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)
-FastModelHuggingFace = model_module.FastModelHuggingFace
-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("chainsaw.wav", device="cpu")
-print(f"Prediction : {map_labels[wav_prediction[0]]}")
-
-# Example: predicting on a Hugging Face dataset
-dataset = load_dataset("rfcx/frugalai")
-test_dataset = dataset["test"]
-true_label = dataset["test"]["label"]
-
-predictions = fast_model.predict(dataset["test"])
-print(accuracy_score(true_label, predictions))