add exemples

app.py

@@ -8,23 +8,14 @@ import sys
 repo_id = "tlmk22/QuefrencyGuardian"
 model_path = hf_hub_download(repo_id=repo_id, filename="model.py")
 model_dir = os.path.dirname(model_path)
-
-# Add downloaded path to sys.path for Python module recognition
 if model_dir not in sys.path:
     sys.path.append(model_dir)
-
-# Load the model dynamically
 spec = importlib.util.spec_from_file_location("model", model_path)
 model_module = importlib.util.module_from_spec(spec)
 spec.loader.exec_module(model_module)
-
-# Load FastModelHuggingFace class
 FastModelHuggingFace = model_module.FastModelHuggingFace
-
-# Step 2: Load the pre-trained model (dynamically from HuggingFace Hub)
 fast_model = FastModelHuggingFace.from_pretrained(repo_id)
 
-# Step 3: Define a prediction function
 map_labels = {0: "chainsaw", 1: "environment"}  # Label mapping
 
 
@@ -33,26 +24,29 @@ def predict_audio(file):
     Predict if a given audio file contains chainsaw activity or not.
     File: Input WAV file (uploaded via Gradio).
     """
-    prediction = fast_model.predict(file, device="cpu")  # Assume CPU inference
+    prediction = fast_model.predict(file, device="cpu")
     predicted_label = map_labels[prediction[0]]
     return f"Prediction: {predicted_label}"
 
 
-# Step 4: Build Gradio Interface
+example_files = [
+    "example1.wav",
+    "example2.wav",
+]
+
+# Build Gradio Interface
 
-# Define Gradio app elements
 drag_and_drop_input = gr.Audio(type="filepath", label="Upload WAV File")
 output_text = gr.Textbox(label="Prediction Result")
 
-# Create Gradio Application
 demo = gr.Interface(
     fn=predict_audio,
     inputs=drag_and_drop_input,
     outputs=output_text,
+    examples=example_files,
     title="Quefrency Guardian: Chainsaw Noise Detector",
     description="Drag and drop a .wav audio file to predict whether it contains chainsaw noise or background environment sounds.",
 )
 
-# Launch App
 if __name__ == "__main__":
     demo.launch()