demo-crunchybot / app.py
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add custom prompts
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import gradio as gr
import os
from gcp import download_credentials
from utils import make_invisible, make_visible, create_folders
from backend_functions import get_answer, init_greeting, export_dataframe
from dotenv import load_dotenv
load_dotenv()
download_credentials()
create_folders()
with gr.Blocks() as main_app:
with gr.Tab('Chatbot'):
user_id = gr.State('') # id used to find the chat into the database
with gr.Column():
with gr.Row():
chat = gr.Chatbot(label="Chatbot Crunchyroll")
output_video = gr.Video(interactive=False, label='Video', autoplay=True, height=400)
with gr.Column():
with gr.Row():
options_audio = gr.Radio(["XTTS", "Elevenlabs"], value="Elevenlabs", label="Audio Generation")
options_prompt = gr.Radio(["Default", "Custom"], value="Default", label="Prompts")
output_audio = gr.Audio(interactive=False, label='Audio', autoplay=False)
messages = gr.State([])
with gr.Row():
text = gr.Textbox(label='Write your question')
with gr.Column():
with gr.Row():
button_text = gr.Button(value='Submit text')
clear_button = gr.ClearButton([chat, messages])
with gr.Tab('Prompts'):
general_prompt = gr.Text(
placeholder='Ingrese el prompt general del bot', label='General prompt'
)
standalone_prompt = gr.Text(
placeholder='Ingrese el prompt usado para encontrar el contexto', label='Standalone prompt'
)
_ = gr.Markdown(
"```\n"
"Recuerde dejar estos formatos en los prompts: \n"
"----------------------- General --------------------------\n"
"=========\n"
"Contexto:\n"
"CONTEXTO\n"
"=========\n"
"\n"
"----------------------- Standalone -----------------------\n"
"You are a standalone question-maker. Given the following chat history and follow-up message, rephrase "
"the follow-up phrase to be a standalone question (sometimes the follow-up is not a question, so create "
"a standalone phrase), in spanish. In the standalone message you must include all the information at the "
"moment that is known about the customer, all the important nouns and what they are looking for. In cases "
"where you think is usefully, include what is the best recommendation for the customer. To give you "
"context, the conversation is about (INGRESE INFORMACIÓN DE LA MARCA, EL NOMBRE Y DE MANERA MUY GENERAL "
"QUE ES LO QUE VENDE).\n"
"There might be moments when there isn't a question in those cases return a standalone phrase: for example "
"if the user says 'hola' (or something similar) then the output would be 'el usuario está saludando', or "
"if the user says 'gracias' or 'es muy util' (or something similar) then the output would be a phrase "
"showing that the user is grateful and what they are grateful for, or if the user say 'si' then it would "
"be a phrase encapsulating the relationship to its previous question or phrase.\n"
"Your response cannot be more than 100 words.\n"
"Chat History:\n"
"\n"
"HISTORY\n"
"Follow-up message: QUESTION\n"
"Standalone message:\n", line_breaks=True
)
with gr.Tab('Times'):
columns = ["User Message", "Chatbot Response", "Standalone Question", "Create Embedding", "Query Pinecone",
"Context Prompt", "Final Response GPT", "Create Clean Message", "Create Audio", "Create Video", "Final Time"]
table_times = gr.DataFrame(headers=columns, visible=False, interactive=False)
with gr.Column():
with gr.Row(visible=False) as row_export_csv:
export_button = gr.Button(value="Export CSV")
csv = gr.File(interactive=False, visible=False)
text.submit(
fn=get_answer,
inputs=[text, chat, messages, output_audio, output_video, table_times, options_audio, options_prompt, general_prompt, standalone_prompt],
outputs=[chat, output_audio, output_video, table_times]
).then(
lambda: None, None, [text]
).then(
fn=make_visible,
inputs=None,
outputs=row_export_csv
)
button_text.click(
fn=get_answer,
inputs=[text, chat, messages, output_audio, output_video, table_times, options_audio, options_prompt, general_prompt, standalone_prompt],
outputs=[chat, output_audio, output_video, table_times]
).then(
lambda: None, None, [text]
).then(
fn=make_visible,
inputs=None,
outputs=row_export_csv
)
export_button.click(
fn=export_dataframe,
inputs=table_times,
outputs=csv
)
main_app.load(init_greeting, inputs=[chat, messages], outputs=[chat, messages])
main_app.launch(debug=True, auth=(os.environ.get('SPACE_USERNAME'), os.environ.get('SPACE_PASSWORD')))