new changes

README.md

@@ -5,7 +5,7 @@ colorFrom: indigo
 colorTo: pink
 sdk: streamlit
 sdk_version: 1.34.0
-app_file: ui/app.py
+app_file: ui/gradio_app.py
 pinned: false
 ---
 

requirements.txt

@@ -1,7 +1,8 @@
 audiodiffusion==1.5.6
 huggingface-hub==0.25.2
 matplotlib
-mega.py
+mega.py == 1.0.8
+gradio
 pycryptodome==3.17
 diffusers==0.17.1
 librosa==0.10.1

ui/app.py

@@ -25,14 +25,6 @@ if 'model_name' not in st.session_state:
 if 'song_list' not in st.session_state:
     st.session_state['song_list'] = None
 
-mega = Mega()
-mega_user_name = os.getenv('MEGA_USERNAME')
-mega_user_name = str(mega_user_name)
-mega_password = os.getenv('MEGA_PASSWORD')
-mega_password = str(mega_password)
-print(mega_user_name,mega_password)
-# m = mega.login(mega_user_name,mega_password)
-
 form1 = st.form(key="form1")
 song_default = np.random.choice(["22", "Anti-Hero", "Back-to-december","Blank-Space","Cardigan","Delicate","Lover","Love-Story","Willow","You-Belong-With-Me"])
 similarity_default = np.random.uniform(0.8, 0.99).__round__(2)

ui/gradio_app.py

@@ -0,0 +1,45 @@
+import gradio as gr
+import numpy as np
+import librosa
+import os
+import soundfile as sf
+import generation_utilities
+
+os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
+
+# Default song and similarity values
+song_default = np.random.choice(["22", "Anti-Hero", "Back-to-december","Blank-Space","Cardigan","Delicate","Lover","Love-Story","Willow","You-Belong-With-Me"])
+similarity_default = round(np.random.uniform(0.8, 0.99), 2)
+
+def generate_song(user_id, song_options, similarity):
+    # Load songs
+    song_list = [librosa.load(os.path.join(os.getcwd(), f"input_songs/{song}.mp3"), sr=22050)[0] for song in song_options]
+    
+    # Generate spectrogram and song
+    spectrogram, generated_song, model_name = generation_utilities.generate_songs(song_list, similarity=similarity, quality=500, merging_quality=100)
+
+    # Save generated song and spectrogram
+    sf.write("ui/temp.wav", generated_song, 22050)
+    np.save("ui/temp.npy", spectrogram)
+
+    # Return user info, generated song path, and link to rating page
+    return {
+        "user_id": user_id, 
+        "song_list": song_options, 
+        "similarity": similarity, 
+        "model_name": model_name, 
+        "generated_song": "ui/temp.wav", 
+        "message": "Song generated! [Click here to go to the rating page](ui/gradio_rating.py)"
+    }
+
+# Gradio Interface
+with gr.Blocks() as demo:
+    user_id = gr.Textbox(label="Enter your user ID")
+    song_options = gr.CheckboxGroup(["22", "Anti-Hero", "Back-to-december","Blank-Space","Cardigan","Delicate","Lover","Love-Story","Willow","You-Belong-With-Me"], label="Select songs from library", value=[song_default])
+    similarity = gr.Slider(minimum=0.0, maximum=1.0, value=similarity_default, label="Similarity")
+
+    output = gr.JSON(label="Session Info")
+    generate_button = gr.Button("Generate Song")
+    generate_button.click(fn=generate_song, inputs=[user_id, song_options, similarity], outputs=output)
+
+demo.launch()

ui/gradio_rating.py

@@ -0,0 +1,41 @@
+import gradio as gr
+import numpy as np
+from mega import Mega
+import os
+import glob
+
+# Load files
+spectrogram_path = "ui/temp.npy"
+generated_song_path = "ui/temp.wav"
+
+def rate_song(user_id, rating, model_name, song_name, similarity):
+    # Log in to Mega
+    mega = Mega()
+    mega_user_name = os.environ.get('MEGA_USERNAME')
+    mega_password = os.environ.get('MEGA_PASSWORD')
+    m = mega.login(mega_user_name, mega_password)
+    
+    # Construct file names and paths for uploading
+    dynamic_song_name = f"{user_id}_{model_name}_{song_name}_{similarity}_{rating}.wav"
+    dynamic_spec_name = f"{user_id}_{model_name}_{song_name}_{similarity}_{rating}.npy"
+    folder = m.find('orpheus_data')
+
+    # Upload files
+    m.upload(generated_song_path, folder[0], dest_filename=dynamic_song_name)
+    m.upload(spectrogram_path, folder[0], dest_filename=dynamic_spec_name)
+    
+    return "Files uploaded successfully!"
+
+with gr.Blocks() as rating_demo:
+    song_name = gr.Markdown("# Original Song")
+    gr.Audio(generated_song_path, label="Generated Song", format="wav")
+    rating_slider = gr.Slider(minimum=0, maximum=10, value=3, label="Rating")
+    submit_rating_button = gr.Button("Submit Rating")
+
+    # Outputs
+    upload_status = gr.Textbox(label="Upload Status")
+
+    # Collect session state and submit rating
+    submit_rating_button.click(fn=rate_song, inputs=["user_id", rating_slider, "model_name", "song_name", "similarity"], outputs=upload_status)
+
+rating_demo.launch()