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"""
Credit to Derek Thomas, derek@huggingface.co
"""
import subprocess
# subprocess.run(["pip", "install", "--upgrade", "transformers[torch,sentencepiece]==4.34.1"])
import logging
from pathlib import Path
from time import perf_counter
import gradio as gr
from jinja2 import Environment, FileSystemLoader
import numpy as np
from sentence_transformers import CrossEncoder
from backend.query_llm import generate_hf, generate_openai
from backend.semantic_search import table, retriever
VECTOR_COLUMN_NAME = "vector"
TEXT_COLUMN_NAME = "text"
proj_dir = Path(__file__).parent
# Setting up the logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# Set up the template environment with the templates directory
env = Environment(loader=FileSystemLoader(proj_dir / 'templates'))
# Load the templates directly from the environment
template = env.get_template('template.j2')
template_html = env.get_template('template_html.j2')
# crossEncoder
cross_encoder = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
# Examples
examples = ['My transhipment cargo is missing',
'What are benefits of the AEO Scheme and eligibility criteria?',
'What are penalties for customs offences? ', ]
def add_text(history, text):
history = [] if history is None else history
history = history + [(text, None)]
return history, gr.Textbox(value="", interactive=False)
def bot(history, api_kind):
top_rerank = 15
top_k_rank = 8
query = history[-1][0]
if not query:
gr.Warning("Please submit a non-empty string as a prompt")
raise ValueError("Empty string was submitted")
logger.warning('Retrieving documents...')
# Retrieve documents relevant to query
document_start = perf_counter()
query_vec = retriever.encode(query)
print(query)
query_vec_flat = [arr.flatten() for arr in query_vec]
logger.warning(f'Finished query vec')
doc1 = table.search(query_vec_flat, vector_column_name=VECTOR_COLUMN_NAME).limit(top_k_rank)
logger.warning(f'Finished search')
documents = table.search(query_vec, vector_column_name=VECTOR_COLUMN_NAME).limit(top_rerank).to_list()
documents = [doc[TEXT_COLUMN_NAME] for doc in documents]
logger.warning(f'start cross encoder {len(documents)}')
# Retrieve documents relevant to query
query_doc_pair = [[query, doc] for doc in documents]
cross_scores = cross_encoder.predict(query_doc_pair)
sim_scores_argsort = list(reversed(np.argsort(cross_scores)))
logger.warning(f'Finished cross encoder {len(documents)}')
documents = [documents[idx] for idx in sim_scores_argsort[:top_k_rank]]
logger.warning(f'num documents {len(documents)}')
document_time = perf_counter() - document_start
logger.warning(f'Finished Retrieving documents in {round(document_time, 2)} seconds...')
# Create Prompt
prompt = template.render(documents=documents, query=query)
prompt_html = template_html.render(documents=documents, query=query)
if api_kind == "HuggingFace":
generate_fn = generate_hf
elif api_kind == "OpenAI":
generate_fn = generate_openai
elif api_kind is None:
gr.Warning("API name was not provided")
raise ValueError("API name was not provided")
else:
gr.Warning(f"API {api_kind} is not supported")
raise ValueError(f"API {api_kind} is not supported")
history[-1][1] = ""
for character in generate_fn(prompt, history[:-1]):
history[-1][1] = character
yield history, prompt_html
with gr.Blocks() as demo:
# Beautiful heading with logo
gr.HTML(value="""
<div style="display: flex; align-items: center; justify-content: space-between;">
<h1 style="color: #008000">ADWITYA - <span style="color: #008000">Customs Manual Chatbot</span></h1>
<img src='logo.png' alt="Chatbot" width="50" height="50" />
</div>
""", elem_id="heading")
# Formatted description
gr.HTML(value="""<p style="font-family: sans-serif; font-size: 16px;">A free chat bot developed by National Customs Targeting Center using Open source LLMs.(Dedicated to 75th Batch IRS Probationers)</p>""", elem_id="description")
chatbot = gr.Chatbot(
[],
elem_id="chatbot",
avatar_images=('https://aui.atlassian.com/aui/8.8/docs/images/avatar-person.svg',
'https://huggingface.co/datasets/huggingface/brand-assets/resolve/main/hf-logo.svg'),
bubble_full_width=False,
show_copy_button=True,
show_share_button=True,
)
with gr.Row():
txt = gr.Textbox(
scale=3,
show_label=False,
placeholder="Enter text and press enter",
container=False,
)
txt_btn = gr.Button(value="Submit text", scale=1)
api_kind = gr.Radio(choices=["HuggingFace"], value="HuggingFace")
prompt_html = gr.HTML()
# Turn off interactivity while generating if you click
txt_msg = txt_btn.click(add_text, [chatbot, txt], [chatbot, txt], queue=False).then(
bot, [chatbot, api_kind], [chatbot, prompt_html])
# Turn it back on
txt_msg.then(lambda: gr.Textbox(interactive=True), None, [txt], queue=False)
# Turn off interactivity while generating if you hit enter
txt_msg = txt.submit(add_text, [chatbot, txt], [chatbot, txt], queue=False).then(
bot, [chatbot, api_kind], [chatbot, prompt_html])
# Turn it back on
txt_msg.then(lambda: gr.Textbox(interactive=True), None, [txt], queue=False)
# Examples
gr.Examples(examples, txt)
demo.queue()
demo.launch(debug=True)
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