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import re
import os
#from dotenv import load_dotenv
import json
import gradio as gr
import random
import time
import requests
from transformers import BertModel, BertTokenizerFast, AdamW
import tensorflow as tf
#load_dotenv(override=True)
#if not os.getenv("HF_API_KEY"):
# raise ValueError("HF_API_KEY must be set")
#hf_key = os.getenv('HF_API_KEY')
API_URL = "https://api-inference.huggingface.co/models/t4ai/distilbert-finetuned-t3-qa"
#headers = {"Authorization": "Bearer " + hf_key }
headers = {}
def query_model(payload):
response = requests.post(API_URL, headers=headers, json=payload)
return response.json()
# contruct UI using Gradio
_booted = False
with gr.Blocks() as demo:
with gr.Row():
with gr.Column(scale=1):
context = gr.Textbox(label="Document Text", lines=25)
with gr.Column(scale=2):
chatbot = gr.Chatbot(label="T3Soft Bot", value=[(None, "Welcome! I am your QA assistant."), (None, "Please paste your document content in the panel to the left."), (None, "Then submit questions below!")])
msg = gr.Textbox(label="Ask your question")
clear = gr.ClearButton([msg, chatbot])
_chatbot = chatbot
def respond(message, context, chat_history):
if(len(context) == 0):
bot_message = "Hm, I don't see any document text, please paste in the box on the left."
else:
query_bot = query_model({"inputs": {"question": message, "context": context}})
if(len(query_bot)):
bot_message = query_bot['answer']
else:
bot_message = "I'm having trouble with this question, please try again."
chat_history.append((message, bot_message))
time.sleep(2)
return "", context, chat_history
msg.submit(respond, [msg, context, chatbot], [msg, context, chatbot])
demo.launch()
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