Create app.py

app.py

@@ -0,0 +1,60 @@
+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 }
+
+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()
+