Update app.py

app.py

@@ -18,3 +18,39 @@ from azure.storage.filedatalake import DataLakeServiceClient
 from llama_index.indices.vector_store.base import GPTVectorStoreIndex
 from adlfs import AzureBlobFileSystem
 import time 
+
+def ask_ai(doc,message):
+    return message
+
+header = """<center><b><p style=\"color: #E13C32; font-size: 36px;\">My Ardian Chatbot</p></b></center>
+<i><p style=\"font-size: 16px; color: grey;\">Please make sure to formulate clear and precise questions and to add contextual information when possible. This will help the tool produce the most relevant response. Adopt an iterative approach and ask for more details or explanations when necessary.</br><i/></p>"""
+
+footnote = "<p style=\"font-size: 16px; color: grey;\"> ⚠ The chatbot doesn't have a memory, it doesn't remember what it previously generated.</a></p>"
+
+theme = gr.themes.Base(
+  primary_hue="red",
+  secondary_hue="gray",
+  font=['FuturaTOT', '=', '36px']
+)
+
+
+with gr.Blocks(theme=theme) as demo:
+    gr.Markdown(header)
+
+    download_button = gr.inputs.File(label="Upload a PDF")
+    chatbot = gr.Chatbot()
+    question = gr.Textbox(label='Question', info="Please write your question here.")
+    clear = gr.Button("Clear")
+    
+    def respond(message, chat_history, doc):
+        bot_message = ask_ai(doc, message)
+        chat_history.append((message, bot_message))
+        time.sleep(2)
+        return "", chat_history
+
+    question.submit(respond, [question, chatbot, download_button], [question, chatbot])
+    
+    clear.click(lambda: None, None, chatbot, queue=False)
+    gr.Markdown(footnote)
+
+demo.launch(share=True, auth=("username", "password"), debug=True)