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import gradio as gr
from huggingface_hub import InferenceClient
from query_data import query_data
from create_database import split_text
import os
import shutil
import logging
logging.basicConfig(filename='myapp.log',format='%(asctime)s %(message)s', datefmt='%m/%d/%Y %I:%M:%S %p')
logger = logging.getLogger(__name__)
"""
For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
"""
CHROMA_PATH = "chroma"
DATA_PATH = "./data"
accesstoken = os.environ['HF_TOKEN']
checkpoint = "HuggingFaceH4/zephyr-7b-beta"
client = InferenceClient(checkpoint,token = accesstoken)
def upload_file(file):
if not os.path.exists(DATA_PATH):
os.mkdir(DATA_PATH)
shutil.copy(file,DATA_PATH)
gr.Info("File uploading")
logger.info("### Inference client: "+checkpoint)
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
messages = [{"role": "system", "content": system_message}]
for val in history:
if val[0]:
messages.append({"role": "user", "content": val[0]})
if val[1]:
messages.append({"role": "assistant", "content": val[1]})
messages.append({"role": "user", "content": message})
logger.info(messages)
response = query_data(message)
yield response
"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
with gr.Blocks() as demo:
upload_button = gr.UploadButton("Click the button to upload")
upload_button.upload(upload_file,upload_button)
gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(value="You are a friendly Chatbot that helps searching knowledge into scientific articles.", label="System message"),
gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.95,
step=0.05,
label="Top-p (nucleus sampling)",
)
],
)
if __name__ == "__main__":
demo.launch()
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