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| !pip install transformers | |
| !pip install requests | |
| !pip install gradio | |
| 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() | |