Update app.py

app.py

@@ -1,178 +1,188 @@
-from openai import OpenAI
-import gradio as gr
-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;
-}
-"""
-
-openAIToken = os.environ['openAIToken']
-assistantId = os.environ['assistantId']
-initial_message = os.environ['initialMessage']
-
-client = OpenAI(api_key=openAIToken)
-
-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_tabs():
-
-  pass
-
-def create_login_tab():
-  with gr.Blocks(fill_height=True) as login:
-    with gr.Row():
-      login_input = gr.Textbox(label="Login")
-    with gr.Row():
-      password_input = gr.Textbox(label="Password", type="password")
-  return login
-
-def create_chat_tab():
-  with gr.Blocks(css=CSS, fill_height=True) as demo:
-    storage = gr.State({"accumulative_string": ""})
-    btn_list = []
-    with gr.Row(elem_id="main_container"):
-      with gr.Column(scale=4):
-        chatbot = gr.Chatbot(label="Facility managment bot", line_breaks=False, height=300, show_label=False, show_share_button=False, elem_id="chatbot")
-        with gr.Row():
-          for i in range(6):
-              btn = gr.Button(visible=False, size="sm")
-              btn_list.append(btn)
-        msg = gr.Textbox(label="Answer", interactive=False)
-
-    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)
-    
-    demo.load(create_thread_openai, inputs=storage, outputs=storage
-        ).then(initiate_chatting, inputs=[chatbot, storage], outputs=[chatbot, storage]
-        ).then(*enable_msg_flow)
-  return demo
-
-demo = create_chat_tab()
-if __name__ == "__main__":
+from openai import OpenAI
+import gradio as gr
+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;
+}
+"""
+
+openAIToken = os.environ['openAIToken']
+assistantId = os.environ['assistantId']
+initial_message = os.environ['initialMessage']
+
+client = OpenAI(api_key=openAIToken)
+
+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_tabs():
+
+  pass
+
+def create_login_tab():
+  with gr.Blocks(fill_height=True) as login:
+    with gr.Row():
+      login_input = gr.Textbox(label="Login")
+    with gr.Row():
+      password_input = gr.Textbox(label="Password", type="password")
+  return login
+
+def create_chat_tab():
+  with gr.Blocks(css=CSS, fill_height=True) as demo:
+    storage = gr.State({"accumulative_string": ""})
+    btn_list = []
+    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")
+        with gr.Row():
+          for i in range(6):
+              btn = gr.Button(visible=False, size="sm")
+              btn_list.append(btn)
+        msg = gr.Textbox(label="Prompt", interactive=False)
+        with gr.Row():
+            examples = gr.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=[textbox],
+                label="Example inputs"
+            )
+
+    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)
+    
+    demo.load(create_thread_openai, inputs=storage, outputs=storage
+        ).then(initiate_chatting, inputs=[chatbot, storage], outputs=[chatbot, storage]
+        ).then(*enable_msg_flow)
+  return demo
+
+demo = create_chat_tab()
+if __name__ == "__main__":
   demo.launch(auth=(os.environ['login'], os.environ['password']), auth_message="Enter your Username and Password")