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| import re | |
| #from dotenv import load_dotenv | |
| import json | |
| import gradio as gr | |
| import random | |
| import time | |
| import requests | |
| from transformers import BertModel, BertTokenizerFast, AdamW | |
| #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) and ("answer" in query_bot)) and (query_bot['score'] > 0.1): | |
| bot_message = query_bot['answer'] | |
| else: | |
| bot_message = random.choice(["I'm having trouble with this question, please try rewording and make sure it is relevant to the document.", "Hm, I'm having trouble finding the answer to that. Can you reword the question?", "Sorry, I can't find the answer to this question."]) | |
| chat_history.append((message, bot_message)) | |
| time.sleep(2) | |
| return "", context, chat_history | |
| msg.submit(respond, [msg, context, chatbot], [msg, context, chatbot]) | |
| demo.launch() | |