Update files for external import

example_usage_fastmodel.py → examples/example_usage_fastmodel.py

@@ -5,13 +5,13 @@ from codecarbon import EmissionsTracker
 from datasets import load_dataset
 from sklearn.metrics import accuracy_score
 
-from fast_model import FastModel, save_pipeline
+from model import FastModel, save_pipeline
 
 dataset = load_dataset("rfcx/frugalai")
 train_dataset = dataset["train"]
 test_dataset = dataset["test"]
 tracker = EmissionsTracker(allow_multiple_runs=True)
-with open("config.json", "r") as file:
+with open("../config.json", "r") as file:
     config = json.load(file)
 model = FastModel(
     config["audio_processing_params"],
@@ -32,4 +32,4 @@ print(accuracy_score(true_label, predictions))
 print("energy_consumed_wh", emissions_data.energy_consumed * 1000)
 print("emissions_gco2eq", emissions_data.emissions * 1000)
 
-save_pipeline(model, Path("./"))
+save_pipeline(model, Path("../"))

example_usage_fastmodel_hf.py → examples/example_usage_fastmodel_hf.py

@@ -1,12 +1,16 @@
 from datasets import load_dataset
+from huggingface_hub import hf_hub_download
 from sklearn.metrics import accuracy_score
-from transformers import AutoModel
+import importlib.util
 
-from fast_model import FastModelHuggingFace
-
-model_name = "tlmk22/QuefrencyGuardian"
-fast_model = AutoModel.from_pretrained(model_name)
-# fast_model = FastModelHuggingFace.from_pretrained(repo_id)
+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_file)
+fastmodel_module = importlib.util.module_from_spec(spec)
+spec.loader.exec_module(fastmodel_module)
+FastModelHuggingFace = fastmodel_module.FastModelHuggingFace
+fast_model = FastModelHuggingFace.from_pretrained(repo_id)
 
 # Perform predictions for a single WAV file
 map_labels = {0: "chainsaw", 1: "environment"}