Update app.py

app.py

@@ -1,4 +1,4 @@
-import gradio as gr
+from flask import Flask, request, jsonify, send_file
 import spaces
 import torch
 import soundfile as sf
@@ -20,39 +20,44 @@ torch_dtype = torch.float16 if device == "cuda" else torch.float32
 pipe = StableAudioPipeline.from_pretrained("stabilityai/stable-audio-open-1.0", torch_dtype=torch_dtype)
 pipe = pipe.to(device)
 
-# Path to store generated audio files (ensure this folder is accessible and writable)
+# Path to store generated audio files
 OUTPUT_PATH = "./generated_audio"
 os.makedirs(OUTPUT_PATH, exist_ok=True)
 
-# Function to generate audio from prompt
+# Initialize Flask app
+app = Flask(__name__)
+
+# Route to generate audio
 @spaces.GPU
-def generate_audio(prompt: str):
-    # Generate the audio using StableAudioPipeline
-    generator = torch.Generator(device).manual_seed(42)
-    audio_output = pipe(prompt=prompt,
-        negative_prompt='Low Quality',
-        num_inference_steps=int(10),  # Number of diffusion steps
-        audio_end_in_s=1,
-        num_waveforms_per_prompt=1,
-        generator=generator).audios
-    
-    # Convert to numpy and save to a WAV file
-    output_audio = audio_output[0].T.float().cpu().numpy()
-    output_filename = "output.wav"
-    output_path = os.path.join(OUTPUT_PATH, output_filename)
-    sf.write(output_path, output_audio, pipe.vae.sampling_rate)
-
-    # Return the file path so Gradio serves the file
-    return output_path
-
-# Gradio Interface setup
-interface = gr.Interface(
-    fn=generate_audio,
-    inputs=gr.Textbox(label="Enter a text prompt to generate audio"),
-    outputs=gr.File(label="Generated Audio File"),
-    title="StableAudioOpen",
-    description="Generate audio from a text prompt using Hugging Face StableAudio Pipeline."
-)
-
-# Launch the Gradio interface
-interface.launch(share=True)
+@app.route("/generate", methods=["GET"])
+def generate_audio():
+    prompt = request.args.get("prompt")
+    if not prompt:
+        return jsonify({"error": "Missing prompt parameter"}), 400
+
+    try:
+        # Generate the audio using StableAudioPipeline
+        generator = torch.Generator(device).manual_seed(42)
+        audio_output = pipe(
+            prompt=prompt,
+            negative_prompt='Low Quality',
+            num_inference_steps=10,  # Number of diffusion steps
+            audio_end_in_s=1,
+            num_waveforms_per_prompt=1,
+            generator=generator
+        ).audios
+
+        # Convert to numpy and save to a WAV file
+        output_audio = audio_output[0].T.float().cpu().numpy()
+        output_filename = "output.wav"
+        output_path = os.path.join(OUTPUT_PATH, output_filename)
+        sf.write(output_path, output_audio, pipe.vae.sampling_rate)
+
+        # Return the WAV file
+        return send_file(output_path, as_attachment=True)
+    except Exception as e:
+        return jsonify({"error": str(e)}), 500
+
+# Run the Flask app
+if __name__ == "__main__":
+    app.run(host="0.0.0.0", port=7860)