Spaces:
Runtime error
Runtime error
File size: 6,059 Bytes
8c31cdd |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 |
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__":
demo.launch(auth=(os.environ['login'], os.environ['password']), auth_message="Enter your Username and Password") |