add accordion

app.py

@@ -1,9 +1,30 @@
 import gradio as gr
 import numpy as np
 from audioldm import text_to_audio, build_model
+# from transformers import AutoModelForCausalLM, AutoTokenizer
+# import torch
+
+# tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
+# model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
 
 audioldm = build_model()
+# audioldm=None
+
+# def predict(input, history=[]):
+#     # tokenize the new input sentence
+#     new_user_input_ids = tokenizer.encode(input + tokenizer.eos_token, return_tensors='pt')
+
+#     # append the new user input tokens to the chat history
+#     bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1)
 
+#     # generate a response 
+#     history = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id).tolist()
+
+#     # convert the tokens to text, and then split the responses into lines
+#     response = tokenizer.decode(history[0]).split("<|endoftext|>")
+#     response = [(response[i], response[i+1]) for i in range(0, len(response)-1, 2)]  # convert to tuples of list
+#     return response, history
+  
 def text2audio(text, duration, guidance_scale, random_seed, n_candidates):
     # print(text, length, guidance_scale)
     waveform = text_to_audio(audioldm, text, random_seed, duration=duration, guidance_scale=guidance_scale, n_candidate_gen_per_text=int(n_candidates)) # [bs, 1, samples]
@@ -44,15 +65,17 @@ with iface:
               </p>
             </div>
         """
-    )
+    )  
     with gr.Group():
         with gr.Box():
             ############# Input
             textbox = gr.Textbox(value="A hammer is hitting a wooden surface", max_lines=1)
-            seed = gr.Number(value=42, label="Change this value (any integer number) will lead to a different generation result.")
-            duration = gr.Slider(2.5, 10, value=5, step=2.5, label="Duration (seconds)")
-            guidance_scale = gr.Slider(0, 5, value=2.5, step=0.5, label="Guidance scale (Large => better quality and relavancy to text; Small => better diversity)")
-            n_candidates = gr.Slider(1, 5, value=1, step=1, label="Automatic quality control. This number control the number of candidates (e.g., generate three audios and choose the best to show you). A Larger value usually lead to better quality with heavier computation")
+
+            with gr.Accordion("Click to change detailed configurations", open=False):
+              seed = gr.Number(value=42, label="Change this value (any integer number) will lead to a different generation result.")
+              duration = gr.Slider(2.5, 10, value=5, step=2.5, label="Duration (seconds)")
+              guidance_scale = gr.Slider(0, 5, value=2.5, step=0.5, label="Guidance scale (Large => better quality and relavancy to text; Small => better diversity)")
+              n_candidates = gr.Slider(1, 5, value=3, step=1, label="Automatic quality control. This number control the number of candidates (e.g., generate three audios and choose the best to show you). A Larger value usually lead to better quality with heavier computation")
             ############# Output
             outputs=[gr.Audio(label="Output", type="numpy"), gr.Audio(label="Output", type="numpy")]