Update app.py

app.py

@@ -4,163 +4,14 @@ import json
 from bot_actions import functions_dictionary
 import os
  
-CSS ="""
-.contain { display: flex; flex-direction: column; }
-.svelte-vt1mxs div:first-child { flex-grow: 1; overflow: auto;}
-#chatbot { flex-grow: 1; overflow: auto;}
-footer {display: none !important;}
-.app.svelte-182fdeq.svelte-182fdeq {
-  max-width: 100vw !important;
-}
-#main_container {
-  height: 95vh;
-}
-#markup_container {
-  height: 100%;
-  overflow:auto;
-}
-"""
+def chatbot(input_text):
+    return input_text
 
-openAIToken = os.environ['openAIToken']
-assistantId = os.environ['assistantId']
-initial_message = os.environ['initialMessage']
 
-client = OpenAI(api_key=openAIToken)
+iface = gr.Interface(fn=chatbot,
+                     inputs=gr.components.Textbox(lines=5, label="Enter your text", show_copy_button=True),
+                     outputs=gr.components.Textbox(lines=5, label="Answer", show_copy_button=True),
+                     examples=["What are the different types of 'work product' lifespan? Provide detailed answer", "Хто може бути наставником?", "Question3", "Question4", "Question5"],
+                     title="PMO Documents AI Chatbot")
 
-def handle_requires_action(data):
-  actions_results = []
-  for tool in data.required_action.submit_tool_outputs.tool_calls:
-    function_name = tool.function.name
-    function_args = json.loads(tool.function.arguments)
-    print(function_name)
-    print(function_args)
-    try:
-      result = functions_dictionary[tool.function.name](**function_args)
-      print("Function result:", result)
-      actions_results.append({"tool_output" : {"tool_call_id": tool.id, "output": result["message"]}})
-    except Exception as e:
-      print(e)
-
-    
-  # Submit all tool_outputs at the same time
-  return actions_results 
-
-
-def create_thread_openai(sessionStorage):
-  streaming_thread = client.beta.threads.create()
-  sessionStorage["threadId"] = streaming_thread.id
-  return sessionStorage
-
-def add_message_to_openai(text, threadId):
-  print("User message: ", text)
-  return client.beta.threads.messages.create(
-    thread_id=threadId,
-    role="user",
-    content=text
-  )
-
-
-def process_text_chunk(text, storage):
-  print(text, end="", flush=True)
-  local_message = None
-  accumulative_string = storage["accumulative_string"] + text
-  local_message = accumulative_string
-  return local_message, storage
-
-def handle_events(threadId, chat_history, storage):
-  storage.update({
-    "accumulative_string" : "",
-    "markup_string": "",
-  })
-  try:
-    with client.beta.threads.runs.stream(
-      thread_id=threadId,
-      assistant_id=assistantId
-    ) as stream:
-      for event in stream:
-        if event.event == "thread.message.delta" and event.data.delta.content:
-          text = event.data.delta.content[0].text.value
-          local_message, storage = process_text_chunk(text, storage)
-          if local_message is not None:
-            chat_history[-1][1] += local_message 
-          yield [chat_history,  storage]
-        if event.event == 'thread.run.requires_action':
-          result = handle_requires_action(event.data)
-          tool_outputs = [x["tool_output"] for x in result]
-          with client.beta.threads.runs.submit_tool_outputs_stream(
-            thread_id=stream.current_run.thread_id,
-            run_id=event.data.id,
-            tool_outputs=tool_outputs,
-          ) as action_stream:
-            for text in action_stream.text_deltas:
-              local_message, storage = process_text_chunk(text, storage)
-              if local_message is not None:
-                chat_history[-1][1] += local_message
-              yield [chat_history, storage]
-            action_stream.close()
-      stream.until_done()
-      print("")
-      return [chat_history, storage]
-  except Exception as e:
-    print(e)
-    chat_history[-1][1] = "Error occured during processing your message. Please try again"
-    yield [chat_history, storage]
-
-def initiate_chatting(chat_history, storage):
-  threadId = storage["threadId"]
-  chat_history = [[None, ""]]
-  add_message_to_openai(initial_message, threadId)
-  for response in handle_events(threadId, chat_history, storage):
-    yield response
-
-def respond_on_user_msg(chat_history, storage):
-  message = chat_history[-1][0]
-  threadId = storage["threadId"]
-  print("Responding for threadId: ", threadId)
-  chat_history[-1][1] = ""
-  add_message_to_openai(message, threadId)
-  for response in handle_events(threadId, chat_history, storage):
-    yield response
-
-def create_chat_tab():
-  msg = gr.Textbox(label="Answer")
-  with gr.Blocks(css=CSS, fill_height=True) as chat_view:
-    storage = gr.State({"accumulative_string": ""})
-    with gr.Row(elem_id="main_container"):
-      with gr.Column(scale=4):
-        chatbot = gr.Chatbot(label="Board of Advisors Assistant", line_breaks=False, height=300, show_label=False, show_share_button=False, elem_id="chatbot")
-        examples = gr.Examples(examples=["I need someone that can help me with real estate in Texas",
-"I'm looking for help with a payment system for my business",
-"I need help to develop my leadership skills"], inputs=msg)
-        msg.render()
-
-    def user(user_message, history):
-        return "", history + [[user_message, None]]
-
-    def disable_msg():
-      message_box = gr.Textbox(value=None, interactive=False)
-      return message_box
-    
-    def enable_msg():
-      message_box = gr.Textbox(value=None, interactive=True)
-      return message_box
-
-    add_user_message_flow = [user, [msg,chatbot],  [msg,chatbot]]
-    chat_response_flow = [respond_on_user_msg, [chatbot, storage], [chatbot, storage]]
-    disable_msg_flow = [disable_msg, None, msg] 
-    enable_msg_flow = [enable_msg, None, msg]
-
-    msg.submit(*add_user_message_flow
-        ).then(*disable_msg_flow
-        ).then(*chat_response_flow
-        ).then(*enable_msg_flow)
-    
-    chat_view.load(*disable_msg_flow
-        ).then(create_thread_openai, inputs=storage, outputs=storage
-        ).then(initiate_chatting, inputs=[chatbot, storage], outputs=[chatbot, storage]
-        ).then(*enable_msg_flow)
-  return chat_view
-
-if __name__ == "__main__":
-  chat_view = create_chat_tab()
-  chat_view.launch(auth=(os.environ['login'], os.environ['password']), auth_message="Enter your Username and Password")
+iface.launch()