Import fast model

app.py

@@ -1,9 +1,58 @@
 import gradio as gr
+from huggingface_hub import hf_hub_download
+import importlib.util
+import os
+import sys
 
+# Step 1: Dynamically load the model file
+repo_id = "tlmk22/QuefrencyGuardian"
+model_path = hf_hub_download(repo_id=repo_id, filename="model.py")
+model_dir = os.path.dirname(model_path)
 
-def greet(name):
-    return "Hello " + name + "!!"
+# 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)
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+# 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
+
+
+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
+    predicted_label = map_labels[prediction[0]]
+    return f"Prediction: {predicted_label}"
+
+
+# Step 4: 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,
+    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()