Create app.py

app.py

@@ -0,0 +1,46 @@
+import gradio as gr
+import requests
+import os
+
+# Hugging Face API token stored as a secret in the frontend Space
+HF_TOKEN = os.environ.get("HF_TOKEN")  # Set in your HF Space secrets
+BACKEND_SPACE_API = "https://hf.space/embed/CleanSong-AI/Main-tool-backend-main/api/predict/"
+
+def clean_song(file):
+    """
+    Sends the uploaded audio file to the private orchestrator
+    and returns the cleaned audio file.
+    """
+    if file is None:
+        return None
+
+    files = {"data": (file.name, open(file.name, "rb"), "audio/mpeg")}
+    headers = {"Authorization": f"Bearer {HF_TOKEN}"}
+    
+    try:
+        response = requests.post(BACKEND_SPACE_API, files=files, headers=headers)
+        response.raise_for_status()
+    except Exception as e:
+        return f"Error: {e}"
+
+    # HF backend should return processed audio as bytes
+    cleaned_file_bytes = response.content
+
+    # Save locally to return to user
+    output_path = "cleaned_output.mp3"
+    with open(output_path, "wb") as f:
+        f.write(cleaned_file_bytes)
+
+    return output_path
+
+# Gradio interface
+iface = gr.Interface(
+    fn=clean_song,
+    inputs=gr.Audio(type="file", label="Upload Song"),
+    outputs=gr.Audio(type="file", label="Cleaned Song"),
+    title="CleanSong AI - Prototype Frontend",
+    description="Upload a song and get a cleaned version. Early prototype!"
+)
+
+if __name__ == "__main__":
+    iface.launch()