app.py

import gradio as gr
import openai
import requests
import csv
import uuid
import whisper
import azure.cognitiveservices.speech as speechsdk
import base64
import os

from polly_utils import PollyVoiceData, NEURAL_ENGINE
from azure_utils import AzureVoiceData

POLLY_VOICE_DATA = PollyVoiceData()
AZURE_VOICE_DATA = AzureVoiceData()

WHISPER_DETECT_LANG = "Chinese (Mandarin)"
WHISPER_MODEL = whisper.load_model("tiny")
print("WHISPER_MODEL", WHISPER_MODEL)


LOOPING_TALKING_HEAD = "videos/Michelle.mp4"
TALKING_HEAD_WIDTH = "192"
MAX_TALKING_HEAD_TEXT_LENGTH = 100


prompt_templates = {"Default ChatGPT": ""}

def get_empty_state():
    return {"total_tokens": 0, "messages": []}

def download_prompt_templates():
    url = "https://raw.githubusercontent.com/f/awesome-chatgpt-prompts/main/prompts.csv"
    try:
        response = requests.get(url)
        reader = csv.reader(response.text.splitlines())
        next(reader)  # skip the header row
        for row in reader:
            if len(row) >= 2:
                act = row[0].strip('"')
                prompt = row[1].strip('"')
                prompt_templates[act] = prompt

    except requests.exceptions.RequestException as e:
        print(f"An error occurred while downloading prompt templates: {e}")
        return

    choices = list(prompt_templates.keys())
    choices = choices[:1] + sorted(choices[1:])
    return gr.update(value=choices[0], choices=choices)

def on_token_change(user_token):
    openai.api_key = user_token

def on_type_change(type):
    print(type)

def on_prompt_template_change(prompt_template):
    if not isinstance(prompt_template, str): return
    return prompt_templates[prompt_template]

def lan_detector(audio_file):
   print('reading the audio file')
   audio = whisper.load_audio(audio_file)
   audio = whisper.pad_or_trim(audio)
   mel = whisper.log_mel_spectrogram(audio).to(WHISPER_MODEL.device)
   _, probs = WHISPER_MODEL.detect_language(mel)
   if max(probs, key=probs.get) == 'en':
     return True
   return False

# UNCOMMENT TO USE WHISPER
def transcribe(aud_inp):
    if aud_inp is None:
        return ""
    text = WHISPER_MODEL.transcribe(aud_inp)

    print("result.text", text["text"])

    return text["text"]

def create_html_video(file_name, width):
    temp_file_url = "/file=" + tmp_file.value['name']
    html_video = f'<video width={width} height={width} autoplay muted loop><source src={temp_file_url} type="video/mp4" poster="Michelle.png"></video>'
    return html_video

def ToBase64(file):
    with open(file, 'rb') as fileObj:
        image_data = fileObj.read()
        base64_data = base64.b64encode(image_data)
        return base64_data.decode()


def do_html_audio_speak_azure(words_to_speak):

    html_audio = '<pre>no audio</pre>'

    speech_key=os.environ["SPEECH_KEY"]
    service_region=os.environ["SERVICE_REGION"]

    speech_config = speechsdk.SpeechConfig(subscription=speech_key, region=service_region)
    # Note: the voice setting will not overwrite the voice element in input SSML.
    speech_config.speech_synthesis_voice_name = "zh-CN-XiaoxiaoNeural"

    # 设置输出的音频文件路径和文件名
    audio_config = speechsdk.audio.AudioOutputConfig(filename="audios/tempfile.mp3")

    text = words_to_speak

    # use the default speaker as audio output.
    speech_synthesizer = speechsdk.SpeechSynthesizer(speech_config=speech_config, audio_config=audio_config)

    result = speech_synthesizer.speak_text_async(text).get()
    # Check result
    if result.reason == speechsdk.ResultReason.SynthesizingAudioCompleted:
        print("Speech synthesized for text [{}]".format(text))
        try:
            temp_aud_file = gr.File("audios/tempfile.mp3")
            temp_aud_file_url = "/file=" + temp_aud_file.value['name']
            html_audio = f'<audio autoplay><source src={temp_aud_file_url} type="audio/mp3"></audio>'
        except IOError as error:
            # Could not write to file, exit gracefully
            print(error)
            return None, None
    elif result.reason == speechsdk.ResultReason.Canceled:
        cancellation_details = result.cancellation_details
        print("Speech synthesis canceled: {}".format(cancellation_details.reason))
        if cancellation_details.reason == speechsdk.CancellationReason.Error:
            print("Error details: {}".format(cancellation_details.error_details))
        # The response didn't contain audio data, exit gracefully
        print("Could not stream audio")
        return None, None

    return html_audio, "audios/tempfile.mp3"


def do_html_video_speak_sad_talker(temp_aud_file):
    
    GRADIO_URL=os.environ["GRADIO_URL"]

    img_data = ToBase64("images/Michelle.png")
    audio_data = ToBase64(temp_aud_file)

    response = requests.post(GRADIO_URL+"/run/sad_talker", json={
        "data": [
            "data:image/png;base64,"+img_data,
            {"name":"audio.wav","data":"data:audio/wav;base64,"+audio_data},
            "crop",
            False,
            False,
        ]
    },timeout=3000)
    print(response.text)
    res = response.json()

    data = res["data"]
    print(data)
    video_rul = GRADIO_URL+"/file=" + data[0][0]['name']
    print(video_rul)
    
    html_video = '<pre>no video</pre>'

    # with open('videos/tempfile.mp4', 'wb') as f:
    #     f.write(response_stream.read())
    # temp_file = gr.File("videos/tempfile.mp4")
    # temp_file_url = "/file=" + temp_file.value['name']
    temp_file_url=video_rul
    html_video = f'<video width={TALKING_HEAD_WIDTH} height={TALKING_HEAD_WIDTH} autoplay><source src={temp_file_url} type="video/mp4" poster="Michelle.png"></video>'

    return html_video, "videos/tempfile.mp4"




def submit_message(type_select,user_token, prompt, prompt_template, temperature, max_tokens, context_length, state):
    print(type_select)
    history = state['messages']

    if not prompt:
        return gr.update(value=''), [(history[i]['content'], history[i+1]['content']) for i in range(0, len(history)-1, 2)], f"Total tokens used: {state['total_tokens']}", state
    
    prompt_template = prompt_templates[prompt_template]

    system_prompt = []
    if prompt_template:
        system_prompt = [{ "role": "system", "content": prompt_template }]

    prompt_msg = { "role": "user", "content": prompt }

    if not type_select:
        history.append(prompt_msg)
        history.append({
            "role": "system",
            "content": "Error: Type is not set."
        })
        return '', [(history[i]['content'], history[i+1]['content']) for i in range(0, len(history)-1, 2)], f"Total tokens used: 0", state

    if not user_token:
        history.append(prompt_msg)
        history.append({
            "role": "system",
            "content": "Error: OpenAI API Key is not set."
        })
        return '', [(history[i]['content'], history[i+1]['content']) for i in range(0, len(history)-1, 2)], f"Total tokens used: 0", state
    
    html_video, temp_file, html_audio, temp_aud_file = None, None, None, None
    try:
        if type_select=='TEXT':
            text_history = [x for x in history if x['role'] != 'image' ]
            print(text_history)
            completion = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=system_prompt + text_history[-context_length*2:] + [prompt_msg], temperature=temperature, max_tokens=max_tokens)
            print(prompt_msg,completion.choices[0].message.to_dict())
            history.append(prompt_msg)
            history.append(completion.choices[0].message.to_dict())

            state['total_tokens'] += completion['usage']['total_tokens']
            answer = completion.choices[0].message.to_dict()["content"]
            if len(answer) <= MAX_TALKING_HEAD_TEXT_LENGTH:
                # html_video, temp_file = do_html_video_speak(output, translate_to)
                html_audio, temp_aud_file = do_html_audio_speak_azure(answer)
                
                try:
                    html_video, temp_file = do_html_video_speak_sad_talker(temp_aud_file)
                    html_audio = None
                except Exception as e:
                    temp_file = LOOPING_TALKING_HEAD
                    html_video = create_html_video(temp_file, TALKING_HEAD_WIDTH)
            else:
                temp_file = LOOPING_TALKING_HEAD
                html_video = create_html_video(temp_file, TALKING_HEAD_WIDTH)
                html_audio, temp_aud_file = do_html_audio_speak_azure(answer)

        elif type_select=='IMAGE':
            response = openai.Image.create(
                    prompt=prompt,
                    n=1,
                    size="512x512"
                    )
            print("image result ",response)
            image_url = response['data'][0]['url']

            history.append({ "role": "image", "content": prompt })
            history.append({ "role": "image", "content": image_url })

            state['total_tokens'] += 0
            
            temp_file = LOOPING_TALKING_HEAD
            html_video = create_html_video(temp_file, TALKING_HEAD_WIDTH)

    except Exception as e:
        # history.append(prompt_msg)
        history.append({
            "role": "system",
            "content": f"Error: {e}"
        })

    total_tokens_used_msg = f"Total tokens used: {state['total_tokens']}"

    chat_messages = [(history[i]['content'], history[i+1]['content']) for i in range(0, len(history)-1, 2)]
    print(1,chat_messages)
    chat_messages=[]
    for i in range(0, len(history)-1, 2):
        print(history[i])
        if(history[i]['role'] == 'image'):
            picture_name = str(uuid.uuid1())+'.png'
            reponse = requests.get(history[i+1]['content'])
            with open('/home/user/app/'+picture_name,'wb') as f:
                f.write(reponse.content)
            
            image_his = {'name': '/home/user/app/'+picture_name, 'mime_type': 'image/png', 'alt_text': None, 'data': None, 'is_file': True}
            
            chat_messages.append((history[i]['content'],image_his))

        else:
            chat_messages.append((history[i]['content'], history[i+1]['content']))
    print(2,chat_messages)
    return '', chat_messages, total_tokens_used_msg, state, html_video, temp_file, html_audio, temp_aud_file

def clear_conversation():
    return gr.update(value=None, visible=True), None, "", get_empty_state()


css = """
      #col-container {max-width: 80%; margin-left: auto; margin-right: auto;}
      #chatbox {min-height: 400px;}
      #header {text-align: center;}
      #prompt_template_preview {padding: 1em; border-width: 1px; border-style: solid; border-color: #e0e0e0; border-radius: 4px;}
      #total_tokens_str {text-align: right; font-size: 0.8em; color: #666;}
      #label {font-size: 0.8em; padding: 0.5em; margin: 0;}
      .message { font-size: 1.2em; }
      """

with gr.Blocks(css=css) as demo:
    
    state = gr.State(get_empty_state())


    with gr.Column(elem_id="col-container"):
        gr.Markdown("""## OpenAI ChatGPT chat
                    Using the ofiicial API (gpt-3.5-turbo model)
                    """,
                    elem_id="header")

        with gr.Row():
            with gr.Column(scale=1, min_width=TALKING_HEAD_WIDTH, visible=True):
                # speak_text_cb = gr.Checkbox(label="Enable speech", value=False)
                # speak_text_cb.change(update_foo, inputs=[speak_text_cb, speak_text_state],
                #                      outputs=[speak_text_state])

                my_file = gr.File(label="Upload a file", type="file", visible=False)
                tmp_file = gr.File(LOOPING_TALKING_HEAD, visible=False)
                # tmp_file_url = "/file=" + tmp_file.value['name']
                htm_video = create_html_video(LOOPING_TALKING_HEAD, TALKING_HEAD_WIDTH)
                video_html = gr.HTML(htm_video)

                # my_aud_file = gr.File(label="Audio file", type="file", visible=True)
                tmp_aud_file = gr.File("audios/tempfile.mp3", visible=False)
                tmp_aud_file_url = "/file=" + tmp_aud_file.value['name']
                htm_audio = f'<audio><source src={tmp_aud_file_url} type="audio/mp3"></audio>'
                audio_html = gr.HTML(htm_audio)
            with gr.Column(scale=6):
                chatbot = gr.Chatbot(elem_id="chatbox")
            with gr.Column(scale=3):
                gr.Markdown("Enter your OpenAI API Key. You can get one [here](https://platform.openai.com/account/api-keys).", elem_id="label")
                user_token = gr.Textbox(value='', placeholder="OpenAI API Key", type="password", show_label=False)
                prompt_template = gr.Dropdown(label="Set a custom insruction for the chatbot:", choices=list(prompt_templates.keys()))
                prompt_template_preview = gr.Markdown(elem_id="prompt_template_preview")
                with gr.Accordion("Advanced parameters", open=False):
                    temperature = gr.Slider(minimum=0, maximum=2.0, value=0.7, step=0.1, label="Temperature", info="Higher = more creative/chaotic")
                    max_tokens = gr.Slider(minimum=100, maximum=4096, value=1000, step=1, label="Max tokens per response")
                    context_length = gr.Slider(minimum=1, maximum=10, value=2, step=1, label="Context length", info="Number of previous messages to send to the chatbot. Be careful with high values, it can blow up the token budget quickly.")
        with gr.Row():
            with gr.Column(min_width=TALKING_HEAD_WIDTH, visible=True):
                type_select = gr.Dropdown(show_label=False, choices= ["TEXT", "IMAGE"],value="TEXT",interactive=True)
            with gr.Column(scale=6):
                input_message = gr.Textbox(show_label=False, placeholder="Enter text and press enter", visible=True).style(container=False)
            with gr.Column(scale=3):
                btn_submit = gr.Button("Submit")
                total_tokens_str = gr.Markdown(elem_id="total_tokens_str")
        with gr.Row():
            audio_comp = gr.Microphone(source="microphone", type="filepath", label="Just say it!",
                                    interactive=True, streaming=False)
            audio_comp.change(transcribe, inputs=[audio_comp], outputs=[input_message])
        with gr.Row():
            btn_clear_conversation = gr.Button("🔃 Start New Conversation")


    # gr.HTML('''<br><br><br><center>You can duplicate this Space to skip the queue:<a href="https://huggingface.co/spaces/anzorq/chatgpt-demo?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a><br>
    #         <p><img src="https://visitor-badge.glitch.me/badge?page_id=anzorq.chatgpt_api_demo_hf" alt="visitors"></p></center>''')

    type_select.change(on_type_change,inputs=[type_select], outputs=[])

    btn_submit.click(submit_message, [type_select,user_token, input_message, prompt_template, temperature, max_tokens, context_length, state], [input_message, chatbot, total_tokens_str, state, video_html, my_file, audio_html, tmp_aud_file])
    input_message.submit(submit_message, [type_select,user_token, input_message, prompt_template, temperature, max_tokens, context_length, state], [input_message, chatbot, total_tokens_str, state, video_html, my_file, audio_html, tmp_aud_file])
    
    btn_clear_conversation.click(clear_conversation, [], [input_message, chatbot, total_tokens_str, state])
    prompt_template.change(on_prompt_template_change, inputs=[prompt_template], outputs=[prompt_template_preview])
    user_token.change(on_token_change, inputs=[user_token], outputs=[])

    
    demo.load(download_prompt_templates, inputs=None, outputs=[prompt_template], queur=False)


demo.queue(concurrency_count=10)
demo.launch(
    # auth=("admin", "IBTGeE3NrPsrViDI"),
    height='800px')