Hugging Face's logo

Spaces:
RamAnanth1
/
chatGPT_voice
Runtime error

App Files Community
3
chatGPT_voice
File size: 4,961 Bytes 
3796a5e
c43093e
 
 
3796a5e
ff1d740
4094da1
ff1d740
91e84de
ff1d740
 
057830c
3796a5e
 
c43093e
 
3796a5e
 
c43093e
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
85df983
c43093e
 
 
 
 
 
0ed9063
c43093e
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
30e5c47
c43093e
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
49e1c3f
c52bc49
62f3a65
49e1c3f
59716dc
3796a5e
c43093e
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
 
import gradio as gr
import os 
import json 
import requests

whisper = gr.Interface.load(name="spaces/sanchit-gandhi/whisper-large-v2")

#input_message.submit([input_message, history], [input_message, chatbot, history])
def translate_or_transcribe(audio, task):
    text_result = whisper(audio, None, task, fn_index=0)
    return text_result
    


#Streaming endpoint
API_URL = "https://api.openai.com/v1/chat/completions" #os.getenv("API_URL") + "/generate_stream"


def predict(inputs, top_p, temperature, openai_api_key, history=[]):  

    payload = {
    "model": "gpt-3.5-turbo",
    "messages": [{"role": "user", "content": f"{inputs}"}],
    "temperature" : 1.0,
    "top_p":1.0,
    "n" : 1,
    "stream": True,
    "presence_penalty":0,
    "frequency_penalty":0,
    }

    headers = {
    "Content-Type": "application/json",
    "Authorization": f"Bearer {openai_api_key}"
    }

    
    history.append(inputs)
    # make a POST request to the API endpoint using the requests.post method, passing in stream=True
    response = requests.post(API_URL, headers=headers, json=payload, stream=True)
    #response = requests.post(API_URL, headers=headers, json=payload, stream=True)
    token_counter = 0 
    partial_words = "" 

    counter=0
    for chunk in response.iter_lines():
        if counter == 0:
          counter+=1
          continue
        counter+=1
        # check whether each line is non-empty
        if chunk :
          # decode each line as response data is in bytes
          if len(json.loads(chunk.decode()[6:])['choices'][0]["delta"]) == 0:
            break
          #print(json.loads(chunk.decode()[6:])['choices'][0]["delta"]["content"])
          partial_words = partial_words + json.loads(chunk.decode()[6:])['choices'][0]["delta"]["content"]
          if token_counter == 0:
            history.append(" " + partial_words)
          else:
            history[-1] = partial_words
          chat = [(history[i], history[i + 1]) for i in range(0, len(history) - 1, 2) ]  # convert to tuples of list
          token_counter+=1
          yield chat, history # resembles {chatbot: chat, state: history}  
        

def reset_textbox():
    return gr.update(value='')

title = """<h1 align="center">🔥ChatGPT API 🚀Streaming🚀</h1>"""
description = """Language models can be conditioned to act like dialogue agents through a conversational prompt that typically takes the form:
```
User: <utterance>
Assistant: <utterance>
User: <utterance>
Assistant: <utterance>
...
```
In this app, you can explore the outputs of a 20B large language model.
"""
#<a href="https://huggingface.co/spaces/ysharma/ChatGPTwithAPI?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>Duplicate Space with GPU Upgrade for fast Inference & no queue<br> 
                
with gr.Blocks(css = """#col_container {width: 700px; margin-left: auto; margin-right: auto;}
                #chatbot {height: 400px; overflow: auto;}""") as demo:
    gr.HTML(title)
    gr.HTML()
    gr.HTML('''<center><a href="https://huggingface.co/spaces/ysharma/ChatGPTwithAPI?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>Duplicate the Space and run securely with your OpenAI API Key</center>''')
    with gr.Column(elem_id = "col_container"):
        openai_api_key = gr.Textbox(type='password', label="Enter your OpenAI API key here")
        chatbot = gr.Chatbot(elem_id='chatbot') #c
        inputs = gr.Textbox(placeholder= "Hi there!", label= "Type an input and press Enter") #t
        state = gr.State([]) #s
        b1 = gr.Button()
    
        #inputs, top_p, temperature, top_k, repetition_penalty
        with gr.Accordion("Parameters", open=False):
            top_p = gr.Slider( minimum=-0, maximum=1.0, value=0.95, step=0.05, interactive=True, label="Top-p (nucleus sampling)",)
            temperature = gr.Slider( minimum=-0, maximum=5.0, value=0.5, step=0.1, interactive=True, label="Temperature",)
            #top_k = gr.Slider( minimum=1, maximum=50, value=4, step=1, interactive=True, label="Top-k",)
            #repetition_penalty = gr.Slider( minimum=0.1, maximum=3.0, value=1.03, step=0.01, interactive=True, label="Repetition Penalty", )
    

    inputs.submit( predict, [inputs, top_p, temperature, openai_api_key, state], [chatbot, state],)
    b1.click( predict, [inputs, top_p, temperature, openai_api_key, state], [chatbot, state],)
    b1.click(reset_textbox, [], [inputs])
    inputs.submit(reset_textbox, [], [inputs])
                    
    #gr.Markdown(description)
                    
    gr.HTML('''
         <p>Note: Please be aware that audio records from iOS devices will not be decoded as expected by Gradio. For the best experience, record your voice from a computer instead of your smartphone ;)</p>

        ''')
    gr.Markdown("![visitor badge](https://visitor-badge.glitch.me/badge?page_id=RamAnanth1.chatGPT_voice)")

    demo.queue().launch(debug=True)