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# AUTOGENERATED! DO NOT EDIT! File to edit: app.ipynb.
# %% auto 0
__all__ = ['title', 'description', 'query_chat_api', 'inference_chat']
# %% app.ipynb 0
import gradio as gr
import requests
import json
import requests
# %% app.ipynb 1
def query_chat_api(
model_id,
inputs,
temperature,
top_p
):
API_URL = f"https://api-inference.huggingface.co/models/{model_id}"
headers = {"Authorization": "Bearer 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", "x-wait-for-model": "1"}
payload = {
"inputs": inputs,
"parameters": {
"temperature": temperature,
"top_p": top_p,
"do_sample": True,
"max_length": 512,
},
}
response = requests.post(API_URL, json=payload, headers=headers)
if response.status_code == 200:
return response.json()
else:
return "Error: " + response.text
# %% app.ipynb 4
def inference_chat(
model_id,
prompt_template,
text_input,
temperature,
top_p,
history=[],
):
with open(f"prompt_templates/{prompt_template}.json", "r") as f:
prompt_template = json.load(f)
history.append(text_input)
inputs = prompt_template["prompt"].format(human_input=text_input)
output = query_chat_api(model_id, inputs, temperature, top_p)
history.append(" " + output[0]["generated_text"])
chat = [
(history[i], history[i + 1]) for i in range(0, len(history) - 1, 2)
] # convert to tuples of list
return {chatbot: chat, state: history}
# %% app.ipynb 12
title = """<h1 align="center">Chatty Language Models</h1>"""
description = """Explore the effect that different prompt templates have on LLMs"""
# %% app.ipynb 13
with gr.Blocks(
css="""
.message.svelte-w6rprc.svelte-w6rprc.svelte-w6rprc {font-size: 20px; margin-top: 20px}
#component-21 > div.wrap.svelte-w6rprc {height: 600px;}
"""
) as iface:
state = gr.State([])
gr.Markdown(title)
gr.Markdown(description)
with gr.Row():
with gr.Column(scale=1):
model_id = gr.Dropdown(
choices=["google/flan-t5-xl"],
value="google/flan-t5-xl",
label="Model",
interactive=True,
)
prompt_template = gr.Dropdown(
choices=[
"langchain_default",
"openai_chatgpt",
"deepmind_sparrow",
"deepmind_gopher",
"anthropic_hhh",
],
value="langchain_default",
label="Prompt Template",
interactive=True,
)
temperature = gr.Slider(
minimum=0.5,
maximum=3.0,
value=1.0,
step=0.1,
interactive=True,
label="Temperature",
)
top_p = gr.Slider(
minimum=-0,
maximum=1.0,
value=0.95,
step=0.05,
interactive=True,
label="Top-p (nucleus sampling)",
)
with gr.Column(scale=1.8):
with gr.Row():
with gr.Column(
scale=1.5,
):
chatbot = gr.Chatbot(
label="Chat Output",
)
with gr.Column(scale=1):
chat_input = gr.Textbox(lines=1, label="Chat Input")
chat_input.submit(
inference_chat,
[
model_id,
prompt_template,
chat_input,
temperature,
top_p,
state,
],
[chatbot, state],
)
with gr.Row():
clear_button = gr.Button(value="Clear", interactive=True)
clear_button.click(
lambda: ("", [], []),
[],
[chat_input, chatbot, state],
queue=False,
)
submit_button = gr.Button(
value="Submit", interactive=True, variant="primary"
)
submit_button.click(
inference_chat,
[
model_id,
prompt_template,
chat_input,
temperature,
top_p,
state,
],
[chatbot, state],
)
iface.launch()
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