app.py

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], result[3], result[4]
    
# Interface for the Gradio app

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

chat_response_1 = gr.Textbox(
                label="Response based on augmented prompt", placeholder="Response of the prompt will appear here"
            )

chat_response_2 = gr.Textbox(
                label="Response based on original prompt", placeholder="Response of the prompt will appear here"
            )

source_list = gr.JSON(
                label="Sources", placeholder="Document source title and doi will appear here"
            )

questions = gr.JSON(
                label="Questions", placeholder="Related questions"
            )
    
iface = gr.Interface(
    fn=call_gradio_api, 
    inputs="text",
    outputs = [new_prompt, chat_response_1, chat_response_2, source_list, questions],
    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="horizontal",
    title="CCL Playground",
    description="Enter a query to get response using RAG"
)

# Launch the Gradio app
iface.queue().launch()