File size: 1,414 Bytes
fb49ad8
 
 
4376882
 
fb49ad8
 
4376882
 
fb49ad8
4376882
64374d2
fb49ad8
5280802
 
1d9e19f
5280802
 
 
1d9e19f
5280802
 
 
1d9e19f
5280802
 
fb49ad8
 
9267364
5280802
b24a219
9267364
 
 
fb49ad8
 
 
 
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
import gradio as gr
from gradio_client import Client

access_token = "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"

# Function to call the Gradio API
def call_gradio_api(user_input):
    
    client = Client("https://traversaal-internal-rag-faiss-gpt3-5.hf.space/", hf_token=access_token)
    result = client.predict(user_input, api_name="/predict")
    return result[0], result[1], result[2]
    
# Interface for the Gradio app

new_prompt = gr.Textbox(
                label="Augmented Prompt", placeholder="Augmented Prompt will appear here"
            )

chat_response = gr.Textbox(
                label="Response", placeholder="Response of the prompt will appear here"
            )

source_list = gr.Textbox(
                label="Sources", placeholder="Document source title and doi will appear here"
            )
    
iface = gr.Interface(
    fn=call_gradio_api, 
    inputs="text",
    outputs = [new_prompt, chat_response, source_list],
    examples=["Make me a 4-hour workshop agenda for handling conflict", "Tell me about the different skills from middle management to executive leadership", "What are some of the major debates among scholars regarding the trait versus process theories of leadership? How have these perspectives evolved over time?"],
    layout="vertical",
    title="CCL Playground",
    description="Enter a query to get response using RAG"
)

# Launch the Gradio app
iface.launch()