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"""Main entrypoint for the app."""
import os
import time
from queue import Queue
from timeit import default_timer as timer
import gradio as gr
from anyio.from_thread import start_blocking_portal
from langchain.embeddings import HuggingFaceInstructEmbeddings
from langchain.vectorstores.chroma import Chroma
from langchain.vectorstores.faiss import FAISS
from app_modules.presets import *
from app_modules.qa_chain import QAChain
from app_modules.utils import *
# Constants
init_settings()
# https://github.com/huggingface/transformers/issues/17611
os.environ["CURL_CA_BUNDLE"] = ""
hf_embeddings_device_type, hf_pipeline_device_type = get_device_types()
print(f"hf_embeddings_device_type: {hf_embeddings_device_type}")
print(f"hf_pipeline_device_type: {hf_pipeline_device_type}")
hf_embeddings_model_name = (
os.environ.get("HF_EMBEDDINGS_MODEL_NAME") or "hkunlp/instructor-xl"
)
n_threds = int(os.environ.get("NUMBER_OF_CPU_CORES") or "4")
index_path = os.environ.get("FAISS_INDEX_PATH") or os.environ.get("CHROMADB_INDEX_PATH")
using_faiss = os.environ.get("FAISS_INDEX_PATH") is not None
llm_model_type = os.environ.get("LLM_MODEL_TYPE")
chat_history_enabled = os.environ.get("CHAT_HISTORY_ENABLED") == "true"
show_param_settings = os.environ.get("SHOW_PARAM_SETTINGS") == "true"
streaming_enabled = True # llm_model_type in ["openai", "llamacpp"]
start = timer()
embeddings = HuggingFaceInstructEmbeddings(
model_name=hf_embeddings_model_name,
model_kwargs={"device": hf_embeddings_device_type},
)
end = timer()
print(f"Completed in {end - start:.3f}s")
start = timer()
print(f"Load index from {index_path} with {'FAISS' if using_faiss else 'Chroma'}")
if not os.path.isdir(index_path):
raise ValueError(f"{index_path} does not exist!")
elif using_faiss:
vectorstore = FAISS.load_local(index_path, embeddings)
else:
vectorstore = Chroma(embedding_function=embeddings, persist_directory=index_path)
end = timer()
print(f"Completed in {end - start:.3f}s")
start = timer()
qa_chain = QAChain(vectorstore, llm_model_type)
qa_chain.init(n_threds=n_threds, hf_pipeline_device_type=hf_pipeline_device_type)
end = timer()
print(f"Completed in {end - start:.3f}s")
def qa(chatbot):
user_msg = chatbot[-1][0]
q = Queue()
result = Queue()
job_done = object()
def task(question, chat_history):
start = timer()
ret = qa_chain.call({"question": question, "chat_history": chat_history}, q)
end = timer()
print(f"Completed in {end - start:.3f}s")
print_llm_response(ret)
q.put(job_done)
result.put(ret)
with start_blocking_portal() as portal:
chat_history = []
if chat_history_enabled:
for i in range(len(chatbot) - 1):
element = chatbot[i]
item = (element[0] or "", element[1] or "")
chat_history.append(item)
portal.start_task_soon(task, user_msg, chat_history)
content = ""
count = 2 if len(chat_history) > 0 else 1
while count > 0:
while q.empty():
print("nothing generated yet - retry in 0.5s")
time.sleep(0.5)
for next_token in qa_chain.streamer:
if next_token is job_done:
break
content += next_token or ""
chatbot[-1][1] = remove_extra_spaces(content)
if count == 1:
yield chatbot
count -= 1
chatbot[-1][1] += "\n\nSources:\n"
ret = result.get()
titles = []
for doc in ret["source_documents"]:
page = doc.metadata["page"] + 1
url = f"{doc.metadata['url']}#page={page}"
file_name = doc.metadata["source"].split("/")[-1]
title = f"{file_name} Page: {page}"
if title not in titles:
titles.append(title)
chatbot[-1][1] += f"1. [{title}]({url})\n"
yield chatbot
with open("assets/custom.css", "r", encoding="utf-8") as f:
customCSS = f.read()
with gr.Blocks(css=customCSS, theme=small_and_beautiful_theme) as demo:
user_question = gr.State("")
with gr.Row():
gr.HTML(title)
gr.Markdown(description_top)
with gr.Row().style(equal_height=True):
with gr.Column(scale=5):
with gr.Row():
chatbot = gr.Chatbot(elem_id="inflaton_chatbot").style(height="100%")
with gr.Row():
with gr.Column(scale=2):
user_input = gr.Textbox(
show_label=False, placeholder="Enter your question here"
).style(container=False)
with gr.Column(
min_width=70,
):
submitBtn = gr.Button("Send")
with gr.Column(
min_width=70,
):
clearBtn = gr.Button("Clear")
if show_param_settings:
with gr.Column():
with gr.Column(
min_width=50,
):
with gr.Tab(label="Parameter Setting"):
gr.Markdown("# Parameters")
top_p = gr.Slider(
minimum=-0,
maximum=1.0,
value=0.95,
step=0.05,
# interactive=True,
label="Top-p",
)
temperature = gr.Slider(
minimum=0.1,
maximum=2.0,
value=0,
step=0.1,
# interactive=True,
label="Temperature",
)
max_new_tokens = gr.Slider(
minimum=0,
maximum=2048,
value=2048,
step=8,
# interactive=True,
label="Max Generation Tokens",
)
max_context_length_tokens = gr.Slider(
minimum=0,
maximum=4096,
value=4096,
step=128,
# interactive=True,
label="Max Context Tokens",
)
gr.Markdown(description)
def chat(user_message, history):
return "", history + [[user_message, None]]
user_input.submit(
chat, [user_input, chatbot], [user_input, chatbot], queue=True
).then(qa, chatbot, chatbot)
submitBtn.click(
chat, [user_input, chatbot], [user_input, chatbot], queue=True
).then(qa, chatbot, chatbot)
def reset():
return "", []
clearBtn.click(
reset,
outputs=[user_input, chatbot],
show_progress=True,
)
demo.title = "Chat with AI Books"
demo.queue(concurrency_count=1).launch()
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