File size: 1,810 Bytes
6fa9e8b
 
 
 
 
 
 
e7900ad
6fa9e8b
 
 
 
997b46c
 
 
 
 
 
 
6fa9e8b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
from Linkedin_post import LinkedinAutomate
from llm_automation import llm_auto
from openai import OpenAI
import gradio as gr

greety = """
As a derivate work of [Linkedin Automation System](https://medium.com/@gathnex) by Gathnex,
Follow us on [linkedin](https://www.linkedin.com/company/gathnex/) and [Github](https://github.com/gathnexadmin). A special thanks to the Gathnex team members who made a significant contribution to this project.
"""


def stream(prompt, g, OPENAI_API_KEY, access_token):
    llm = llm_auto(prompt, OPENAI_API_KEY)
    if llm.intent_indentifier() == "#Post":
        url = llm.prompt_link_capturer()
        res = LinkedinAutomate(access_token, url, OPENAI_API_KEY).main_func()
        return llm.posted_or_not(res)
    else:
        return llm.normal_gpt()


css = """
  h1 {
  text-align: center;
}

#duplicate-button {
  margin: auto;
  color: white;
  background: #1565c0;
  border-radius: 100vh;
}

.contain {
  max-width: 900px;
  margin: auto;
  padding-top: 1.5rem;
}

"""

chat_interface = gr.ChatInterface(
    fn=stream,
    additional_inputs_accordion_name = "Credentials",
    additional_inputs=[
        gr.Textbox(label="OpenAI Key", lines=1),
        gr.Textbox(label="Linkedin Access Token", lines=1),
    ],
    stop_btn=None,
    examples=[
        ["explain Large language model"],
        ["what is quantum computing"]
    ],
)

with gr.Blocks(css=css) as demo:
    gr.HTML("<h1><center>Gathnex Linkedin Automation using Generative AI<h1><center>")
    gr.HTML("<h3><center><a href='https://medium.com/@gathnex'>Gathnex AI</a>💬<h3><center>")
    gr.DuplicateButton(value="Duplicate Space for private use", elem_id="duplicate-button")
    chat_interface.render()
    gr.Markdown(greety)
    
if __name__ == "__main__":
    demo.queue(max_size=10).launch()