Replace App

app.py

@@ -1,63 +1,407 @@
+import copy
+import os
+import random
+import sys
+
+
+import spaces
 import gradio as gr
-from huggingface_hub import InferenceClient
+import librosa
+import numpy as np
+import soundfile as sf
+import torch
+import torch.nn.functional as F
+from accelerate import infer_auto_device_map
+from datasets import Audio
+from models.salmonn import SALMONN
+from safetensors.torch import load, load_model
+from tinydb import TinyDB
+from torch import nn
+from transformers import (
+    AutoModelForCausalLM,
+    AutoProcessor,
+    AutoModel,
+    AutoTokenizer,
+    LlamaForCausalLM,
+    TextIteratorStreamer,
+    WhisperForConditionalGeneration,
+)
+from transformers.generation import GenerationConfig
+
+tokenizer = AutoTokenizer.from_pretrained("WillHeld/via-llama")
+prefix = torch.tensor([128000, 128006, 882, 128007, 271]).to("cuda")
+pre_user_suffix = torch.tensor([271]).to("cuda")
+final_header = torch.tensor([128009, 128006, 78191, 128007, 271]).to("cuda")
+cache = None
+anonymous = False
+
+resampler = Audio(sampling_rate=16_000)
+
+
+qwen_tokenizer = AutoTokenizer.from_pretrained(
+    "Qwen/Qwen-Audio-Chat", trust_remote_code=True
+)
+qwen_model = AutoModelForCausalLM.from_pretrained(
+    "Qwen/Qwen-Audio-Chat",
+    device_map="auto",
+    trust_remote_code=True,
+    torch_dtype=torch.float16,
+).eval()
+
+qwen_model.generation_config = GenerationConfig.from_pretrained(
+    "Qwen/Qwen-Audio-Chat",
+    trust_remote_code=True,
+    do_sample=False,
+    top_k=50,
+    top_p=1.0,
+)
+
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+# salmonn_model = SALMONN(
+#     ckpt="./SALMONN_PATHS/salmonn_v1.pth",
+#     whisper_path="./SALMONN_PATHS/whisper-large-v2",
+#     beats_path="./SALMONN_PATHS/BEATs_iter3_plus_AS2M_finetuned_on_AS2M_cpt2.pt",
+#     vicuna_path="./SALMONN_PATHS/vicuna-13b-v1.1",
+#     low_resource=False,
+#     device="cuda:0",
+# )
+# salmonn_tokenizer = salmonn_model.llama_tokenizer
 
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+diva = AutoModel.from_pretrained("WillHeld/DiVA-llama-3-v0-8b", trust_remote_code=True)
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
 
-    messages.append({"role": "user", "content": message})
+
+@spaces.GPU
+@torch.no_grad
+def salmonn_fwd(audio_input, prompt, do_sample=False, temperature=0.001):
+    if audio_input == None:
+        return ""
+    sr, y = audio_input
+    y = y.astype(np.float32)
+    y /= np.max(np.abs(y))
+    a = resampler.decode_example(
+        resampler.encode_example({"array": y, "sampling_rate": sr})
+    )
+    sf.write("tmp.wav", a["array"], a["sampling_rate"], format="wav")
+    streamer = TextIteratorStreamer(salmonn_tokenizer)
+    with torch.cuda.amp.autocast(dtype=torch.float16):
+        llm_message = salmonn_model.generate(
+            wav_path="tmp.wav",
+            prompt=prompt,
+            do_sample=False,
+            top_p=1.0,
+            temperature=0.0,
+            device="cuda:0",
+            streamer=streamer,
+        )
 
     response = ""
+    for new_tokens in streamer:
+        response += new_tokens
+        yield response.replace("</s>", "")
+
+
+@spaces.GPU
+@torch.no_grad
+def qwen_audio(audio_input, prompt, do_sample=False, temperature=0.001):
+    if audio_input == None:
+        return ""
+    sr, y = audio_input
+    y = y.astype(np.float32)
+    y /= np.max(np.abs(y))
+    a = resampler.decode_example(
+        resampler.encode_example({"array": y, "sampling_rate": sr})
+    )
+    sf.write("tmp.wav", a["array"], a["sampling_rate"], format="wav")
+    query = qwen_tokenizer.from_list_format([{"audio": "tmp.wav"}, {"text": prompt}])
+
+    response, history = qwen_model.chat(
+        qwen_tokenizer,
+        query=query,
+        system="You are a helpful assistant.",
+        history=None,
+    )
+    return response
+
+
+@spaces.GPU
+@torch.no_grad
+def via(audio_input, prompt, do_sample=False, temperature=0.001):
+    if audio_input == None:
+        return ""
+    sr, y = audio_input
+    y = y.astype(np.float32)
+    y /= np.max(np.abs(y))
+    a = resampler.decode_example(
+        resampler.encode_example({"array": y, "sampling_rate": sr})
+    )
+
+    audio = a["array"]
+
+    yield from diva.generate_stream(audio, prompt)
+
+
+def transcribe(audio_input, text_prompt, state, model_order):
+    yield (
+        gr.Button(
+            value="Waiting in queue for GPU time...",
+            interactive=False,
+            variant="primary",
+        ),
+        "",
+        "",
+        "",
+        gr.Button(visible=False),
+        gr.Button(visible=False),
+        gr.Button(visible=False),
+        state,
+    )
+    if audio_input == None:
+        return (
+            "",
+            "",
+            "",
+            gr.Button(visible=False),
+            gr.Button(visible=False),
+            gr.Button(visible=False),
+            state,
+        )
+
+    def gen_from_via():
+        via_resp = via(audio_input, text_prompt)
+        for resp in via_resp:
+            v_resp = gr.Textbox(
+                value=resp,
+                visible=True,
+                label=model_names[0] if not anonymous else f"Model {order}",
+            )
+            yield (v_resp, s_resp, q_resp)
+
+    def gen_from_salmonn():
+        salmonn_resp = salmonn_fwd(audio_input, text_prompt)
+        for resp in salmonn_resp:
+            s_resp = gr.Textbox(
+                value=resp,
+                visible=True,
+                label=model_names[1] if not anonymous else f"Model {order}",
+            )
+            yield (v_resp, s_resp, q_resp)
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
+    def gen_from_qwen():
+        qwen_resp = qwen_audio(audio_input, text_prompt)
+        q_resp = gr.Textbox(
+            value=qwen_resp,
+            visible=True,
+            label=model_names[2] if not anonymous else f"Model {order}",
+        )
+        yield (v_resp, s_resp, q_resp)
+
+    spinner_id = 0
+    spinners = ["◐ ", "◓ ", "◑", "◒"]
+    initial_responses = [("", "", "")]
+    resp_generators = [
+        gen_from_via(),
+        #gen_from_salmonn(),
+        gen_from_qwen(),
+    ]
+    order = -1
+    resp_generators = [
+        resp_generators[model_order[0]],
+        resp_generators[model_order[1]],
+        resp_generators[model_order[2]],
+    ]
+    for generator in [initial_responses, *resp_generators]:
+        order += 1
+        for resps in generator:
+            v_resp, s_resp, q_resp = resps
+            resp_1 = resps[model_order[0]]
+            resp_2 = resps[model_order[1]]
+            resp_3 = resps[model_order[2]]
+            spinner = spinners[spinner_id]
+            spinner_id = (spinner_id + 1) % 4
+            yield (
+                gr.Button(
+                    value=spinner + " Generating Responses " + spinner,
+                    interactive=False,
+                    variant="primary",
+                ),
+                resp_1,
+                resp_2,
+                resp_3,
+                gr.Button(visible=False),
+                gr.Button(visible=False),
+                gr.Button(visible=False),
+                state,
+            )
+    yield (
+        gr.Button(
+            value="Click to compare models!", interactive=True, variant="primary"
         ),
-    ],
+        resp_1,
+        resp_2,
+        resp_3,
+        gr.Button(visible=True),
+        gr.Button(visible=False),
+        gr.Button(visible=True),
+        responses_complete(state),
+    )
+
+
+def on_page_load(state, model_order):
+    if state == 0:
+        gr.Info(
+            "Record what you want to say to your AI Assistant! All Audio recordings are stored only temporarily and will be erased as soon as you exit this page."
+        )
+        state = 1
+        if anonymous:
+            random.shuffle(model_order)
+    return state, model_order
+
+
+def recording_complete(state):
+    if state == 1:
+        gr.Info(
+            "Submit your recording to get responses from all three models! You can also influence the model responses with an optional prompt."
+        )
+        state = 2
+    return (
+        gr.Button(
+            value="Click to compare models!", interactive=True, variant="primary"
+        ),
+        state,
+    )
+
+
+def responses_complete(state):
+    if state == 2:
+        gr.Info(
+            "Give us your feedback! Mark which model gave you the best response so we can understand the quality of these different voice assistant models."
+        )
+        state = 3
+    return state
+
+
+def clear_factory(button_id):
+    def clear(audio_input, text_prompt, model_order):
+        if button_id != None:
+            sr, y = audio_input
+            db.insert(
+                {
+                    "audio_hash": hash(str(y)),
+                    "text_prompt": text_prompt,
+                    "best": model_shorthand[model_order[button_id]],
+                }
+            )
+        if anonymous:
+            random.shuffle(model_order)
+        return (
+            model_order,
+            gr.Button(
+                value="Record Audio to Submit!",
+                interactive=False,
+            ),
+            gr.Button(visible=False),
+            gr.Button(visible=False),
+            gr.Button(visible=False),
+            None,
+            gr.Textbox(visible=False),
+            gr.Textbox(visible=False),
+            gr.Textbox(visible=False),
+        )
+
+    return clear
+
+
+theme = gr.themes.Soft(
+    primary_hue=gr.themes.Color(
+        c100="#82000019",
+        c200="#82000033",
+        c300="#8200004c",
+        c400="#82000066",
+        c50="#8200007f",
+        c500="#8200007f",
+        c600="#82000099",
+        c700="#820000b2",
+        c800="#820000cc",
+        c900="#820000e5",
+        c950="#820000f2",
+    ),
+    secondary_hue="rose",
+    neutral_hue="stone",
 )
 
+db = TinyDB("user_study.json")
+
+model_names = ["Llama 3 DiVA", "SALMONN", "Qwen Audio"]
+model_shorthand = ["via", "salmonn", "qwen"]
+with gr.Blocks(theme=theme) as demo:
+    state = gr.State(0)
+    model_order = gr.State([0, 1, 2])
+    with gr.Row():
+        audio_input = gr.Audio(
+            sources=["microphone"], streaming=False, label="Audio Input"
+        )
+    with gr.Row():
+        prompt = gr.Textbox(
+            value="",
+            label="Text Prompt",
+            placeholder="Optional: Additional text prompt to influence how the model responds to your speech. e.g. 'Respond in a Haiku style.'",
+        )
+
+    with gr.Row():
+        btn = gr.Button(value="Record Audio to Submit!", interactive=False)
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            out1 = gr.Textbox(visible=False)
+            best1 = gr.Button(value="This response is best", visible=False)
+        with gr.Column(scale=1):
+            out2 = gr.Textbox(visible=False)
+            best2 = gr.Button(value="This response is best", visible=False)
+        with gr.Column(scale=1):
+            out3 = gr.Textbox(visible=False)
+            best3 = gr.Button(value="This response is best", visible=False)
+
+    audio_input.stop_recording(
+        recording_complete,
+        [state],
+        [btn, state],
+    )
+    audio_input.start_recording(
+        lambda: gr.Button(
+            value="Uploading Audio to Cloud", interactive=False, variant="primary"
+        ),
+        None,
+        btn,
+    )
+    btn.click(
+        fn=transcribe,
+        inputs=[audio_input, prompt, state, model_order],
+        outputs=[btn, out1, out2, out3, best1, best2, best3, state],
+    )
+    best1.click(
+        fn=clear_factory(0),
+        inputs=[audio_input, prompt, model_order],
+        outputs=[model_order, btn, best1, best2, best3, audio_input, out1, out2, out3],
+    )
+    best2.click(
+        fn=clear_factory(1),
+        inputs=[audio_input, prompt, model_order],
+        outputs=[model_order, btn, best1, best2, best3, audio_input, out1, out2, out3],
+    )
+    best3.click(
+        fn=clear_factory(2),
+        inputs=[audio_input, prompt, model_order],
+        outputs=[model_order, btn, best1, best2, best3, audio_input, out1, out2, out3],
+    )
+    audio_input.clear(
+        clear_factory(None),
+        [audio_input, prompt, model_order],
+        [model_order, btn, best1, best2, best3, audio_input, out1, out2, out3],
+    )
+    demo.load(
+        fn=on_page_load, inputs=[state, model_order], outputs=[state, model_order]
+    )
 
-if __name__ == "__main__":
-    demo.launch()
+demo.launch(share=True)

demo.py

@@ -1,407 +0,0 @@
-import copy
-import os
-import random
-import sys
-
-
-import spaces
-import gradio as gr
-import librosa
-import numpy as np
-import soundfile as sf
-import torch
-import torch.nn.functional as F
-from accelerate import infer_auto_device_map
-from datasets import Audio
-from models.salmonn import SALMONN
-from safetensors.torch import load, load_model
-from tinydb import TinyDB
-from torch import nn
-from transformers import (
-    AutoModelForCausalLM,
-    AutoProcessor,
-    AutoModel,
-    AutoTokenizer,
-    LlamaForCausalLM,
-    TextIteratorStreamer,
-    WhisperForConditionalGeneration,
-)
-from transformers.generation import GenerationConfig
-
-tokenizer = AutoTokenizer.from_pretrained("WillHeld/via-llama")
-prefix = torch.tensor([128000, 128006, 882, 128007, 271]).to("cuda")
-pre_user_suffix = torch.tensor([271]).to("cuda")
-final_header = torch.tensor([128009, 128006, 78191, 128007, 271]).to("cuda")
-cache = None
-anonymous = False
-
-resampler = Audio(sampling_rate=16_000)
-
-
-qwen_tokenizer = AutoTokenizer.from_pretrained(
-    "Qwen/Qwen-Audio-Chat", trust_remote_code=True
-)
-qwen_model = AutoModelForCausalLM.from_pretrained(
-    "Qwen/Qwen-Audio-Chat",
-    device_map="auto",
-    trust_remote_code=True,
-    torch_dtype=torch.float16,
-).eval()
-
-qwen_model.generation_config = GenerationConfig.from_pretrained(
-    "Qwen/Qwen-Audio-Chat",
-    trust_remote_code=True,
-    do_sample=False,
-    top_k=50,
-    top_p=1.0,
-)
-
-
-# salmonn_model = SALMONN(
-#     ckpt="./SALMONN_PATHS/salmonn_v1.pth",
-#     whisper_path="./SALMONN_PATHS/whisper-large-v2",
-#     beats_path="./SALMONN_PATHS/BEATs_iter3_plus_AS2M_finetuned_on_AS2M_cpt2.pt",
-#     vicuna_path="./SALMONN_PATHS/vicuna-13b-v1.1",
-#     low_resource=False,
-#     device="cuda:0",
-# )
-# salmonn_tokenizer = salmonn_model.llama_tokenizer
-
-
-diva = AutoModel.from_pretrained("WillHeld/DiVA-llama-3-v0-8b", trust_remote_code=True)
-
-
-
-@spaces.GPU
-@torch.no_grad
-def salmonn_fwd(audio_input, prompt, do_sample=False, temperature=0.001):
-    if audio_input == None:
-        return ""
-    sr, y = audio_input
-    y = y.astype(np.float32)
-    y /= np.max(np.abs(y))
-    a = resampler.decode_example(
-        resampler.encode_example({"array": y, "sampling_rate": sr})
-    )
-    sf.write("tmp.wav", a["array"], a["sampling_rate"], format="wav")
-    streamer = TextIteratorStreamer(salmonn_tokenizer)
-    with torch.cuda.amp.autocast(dtype=torch.float16):
-        llm_message = salmonn_model.generate(
-            wav_path="tmp.wav",
-            prompt=prompt,
-            do_sample=False,
-            top_p=1.0,
-            temperature=0.0,
-            device="cuda:0",
-            streamer=streamer,
-        )
-
-    response = ""
-    for new_tokens in streamer:
-        response += new_tokens
-        yield response.replace("</s>", "")
-
-
-@spaces.GPU
-@torch.no_grad
-def qwen_audio(audio_input, prompt, do_sample=False, temperature=0.001):
-    if audio_input == None:
-        return ""
-    sr, y = audio_input
-    y = y.astype(np.float32)
-    y /= np.max(np.abs(y))
-    a = resampler.decode_example(
-        resampler.encode_example({"array": y, "sampling_rate": sr})
-    )
-    sf.write("tmp.wav", a["array"], a["sampling_rate"], format="wav")
-    query = qwen_tokenizer.from_list_format([{"audio": "tmp.wav"}, {"text": prompt}])
-
-    response, history = qwen_model.chat(
-        qwen_tokenizer,
-        query=query,
-        system="You are a helpful assistant.",
-        history=None,
-    )
-    return response
-
-
-@spaces.GPU
-@torch.no_grad
-def via(audio_input, prompt, do_sample=False, temperature=0.001):
-    if audio_input == None:
-        return ""
-    sr, y = audio_input
-    y = y.astype(np.float32)
-    y /= np.max(np.abs(y))
-    a = resampler.decode_example(
-        resampler.encode_example({"array": y, "sampling_rate": sr})
-    )
-
-    audio = a["array"]
-
-    yield from diva.generate_stream(audio, prompt)
-
-
-def transcribe(audio_input, text_prompt, state, model_order):
-    yield (
-        gr.Button(
-            value="Waiting in queue for GPU time...",
-            interactive=False,
-            variant="primary",
-        ),
-        "",
-        "",
-        "",
-        gr.Button(visible=False),
-        gr.Button(visible=False),
-        gr.Button(visible=False),
-        state,
-    )
-    if audio_input == None:
-        return (
-            "",
-            "",
-            "",
-            gr.Button(visible=False),
-            gr.Button(visible=False),
-            gr.Button(visible=False),
-            state,
-        )
-
-    def gen_from_via():
-        via_resp = via(audio_input, text_prompt)
-        for resp in via_resp:
-            v_resp = gr.Textbox(
-                value=resp,
-                visible=True,
-                label=model_names[0] if not anonymous else f"Model {order}",
-            )
-            yield (v_resp, s_resp, q_resp)
-
-    def gen_from_salmonn():
-        salmonn_resp = salmonn_fwd(audio_input, text_prompt)
-        for resp in salmonn_resp:
-            s_resp = gr.Textbox(
-                value=resp,
-                visible=True,
-                label=model_names[1] if not anonymous else f"Model {order}",
-            )
-            yield (v_resp, s_resp, q_resp)
-
-    def gen_from_qwen():
-        qwen_resp = qwen_audio(audio_input, text_prompt)
-        q_resp = gr.Textbox(
-            value=qwen_resp,
-            visible=True,
-            label=model_names[2] if not anonymous else f"Model {order}",
-        )
-        yield (v_resp, s_resp, q_resp)
-
-    spinner_id = 0
-    spinners = ["◐ ", "◓ ", "◑", "◒"]
-    initial_responses = [("", "", "")]
-    resp_generators = [
-        gen_from_via(),
-        #gen_from_salmonn(),
-        gen_from_qwen(),
-    ]
-    order = -1
-    resp_generators = [
-        resp_generators[model_order[0]],
-        resp_generators[model_order[1]],
-        resp_generators[model_order[2]],
-    ]
-    for generator in [initial_responses, *resp_generators]:
-        order += 1
-        for resps in generator:
-            v_resp, s_resp, q_resp = resps
-            resp_1 = resps[model_order[0]]
-            resp_2 = resps[model_order[1]]
-            resp_3 = resps[model_order[2]]
-            spinner = spinners[spinner_id]
-            spinner_id = (spinner_id + 1) % 4
-            yield (
-                gr.Button(
-                    value=spinner + " Generating Responses " + spinner,
-                    interactive=False,
-                    variant="primary",
-                ),
-                resp_1,
-                resp_2,
-                resp_3,
-                gr.Button(visible=False),
-                gr.Button(visible=False),
-                gr.Button(visible=False),
-                state,
-            )
-    yield (
-        gr.Button(
-            value="Click to compare models!", interactive=True, variant="primary"
-        ),
-        resp_1,
-        resp_2,
-        resp_3,
-        gr.Button(visible=True),
-        gr.Button(visible=False),
-        gr.Button(visible=True),
-        responses_complete(state),
-    )
-
-
-def on_page_load(state, model_order):
-    if state == 0:
-        gr.Info(
-            "Record what you want to say to your AI Assistant! All Audio recordings are stored only temporarily and will be erased as soon as you exit this page."
-        )
-        state = 1
-        if anonymous:
-            random.shuffle(model_order)
-    return state, model_order
-
-
-def recording_complete(state):
-    if state == 1:
-        gr.Info(
-            "Submit your recording to get responses from all three models! You can also influence the model responses with an optional prompt."
-        )
-        state = 2
-    return (
-        gr.Button(
-            value="Click to compare models!", interactive=True, variant="primary"
-        ),
-        state,
-    )
-
-
-def responses_complete(state):
-    if state == 2:
-        gr.Info(
-            "Give us your feedback! Mark which model gave you the best response so we can understand the quality of these different voice assistant models."
-        )
-        state = 3
-    return state
-
-
-def clear_factory(button_id):
-    def clear(audio_input, text_prompt, model_order):
-        if button_id != None:
-            sr, y = audio_input
-            db.insert(
-                {
-                    "audio_hash": hash(str(y)),
-                    "text_prompt": text_prompt,
-                    "best": model_shorthand[model_order[button_id]],
-                }
-            )
-        if anonymous:
-            random.shuffle(model_order)
-        return (
-            model_order,
-            gr.Button(
-                value="Record Audio to Submit!",
-                interactive=False,
-            ),
-            gr.Button(visible=False),
-            gr.Button(visible=False),
-            gr.Button(visible=False),
-            None,
-            gr.Textbox(visible=False),
-            gr.Textbox(visible=False),
-            gr.Textbox(visible=False),
-        )
-
-    return clear
-
-
-theme = gr.themes.Soft(
-    primary_hue=gr.themes.Color(
-        c100="#82000019",
-        c200="#82000033",
-        c300="#8200004c",
-        c400="#82000066",
-        c50="#8200007f",
-        c500="#8200007f",
-        c600="#82000099",
-        c700="#820000b2",
-        c800="#820000cc",
-        c900="#820000e5",
-        c950="#820000f2",
-    ),
-    secondary_hue="rose",
-    neutral_hue="stone",
-)
-
-db = TinyDB("user_study.json")
-
-model_names = ["Llama 3 DiVA", "SALMONN", "Qwen Audio"]
-model_shorthand = ["via", "salmonn", "qwen"]
-with gr.Blocks(theme=theme) as demo:
-    state = gr.State(0)
-    model_order = gr.State([0, 1, 2])
-    with gr.Row():
-        audio_input = gr.Audio(
-            sources=["microphone"], streaming=False, label="Audio Input"
-        )
-    with gr.Row():
-        prompt = gr.Textbox(
-            value="",
-            label="Text Prompt",
-            placeholder="Optional: Additional text prompt to influence how the model responds to your speech. e.g. 'Respond in a Haiku style.'",
-        )
-
-    with gr.Row():
-        btn = gr.Button(value="Record Audio to Submit!", interactive=False)
-
-    with gr.Row():
-        with gr.Column(scale=1):
-            out1 = gr.Textbox(visible=False)
-            best1 = gr.Button(value="This response is best", visible=False)
-        with gr.Column(scale=1):
-            out2 = gr.Textbox(visible=False)
-            best2 = gr.Button(value="This response is best", visible=False)
-        with gr.Column(scale=1):
-            out3 = gr.Textbox(visible=False)
-            best3 = gr.Button(value="This response is best", visible=False)
-
-    audio_input.stop_recording(
-        recording_complete,
-        [state],
-        [btn, state],
-    )
-    audio_input.start_recording(
-        lambda: gr.Button(
-            value="Uploading Audio to Cloud", interactive=False, variant="primary"
-        ),
-        None,
-        btn,
-    )
-    btn.click(
-        fn=transcribe,
-        inputs=[audio_input, prompt, state, model_order],
-        outputs=[btn, out1, out2, out3, best1, best2, best3, state],
-    )
-    best1.click(
-        fn=clear_factory(0),
-        inputs=[audio_input, prompt, model_order],
-        outputs=[model_order, btn, best1, best2, best3, audio_input, out1, out2, out3],
-    )
-    best2.click(
-        fn=clear_factory(1),
-        inputs=[audio_input, prompt, model_order],
-        outputs=[model_order, btn, best1, best2, best3, audio_input, out1, out2, out3],
-    )
-    best3.click(
-        fn=clear_factory(2),
-        inputs=[audio_input, prompt, model_order],
-        outputs=[model_order, btn, best1, best2, best3, audio_input, out1, out2, out3],
-    )
-    audio_input.clear(
-        clear_factory(None),
-        [audio_input, prompt, model_order],
-        [model_order, btn, best1, best2, best3, audio_input, out1, out2, out3],
-    )
-    demo.load(
-        fn=on_page_load, inputs=[state, model_order], outputs=[state, model_order]
-    )
-
-demo.launch(share=True)