import gradio as gr import os import sys import json import requests MODEL = "gpt-4" API_URL = os.getenv("API_URL") DISABLED = os.getenv("DISABLED") == 'True' OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") NUM_THREADS = int(os.getenv("NUM_THREADS")) print (NUM_THREADS) def exception_handler(exception_type, exception, traceback): print("%s: %s" % (exception_type.__name__, exception)) sys.excepthook = exception_handler sys.tracebacklimit = 0 #https://github.com/gradio-app/gradio/issues/3531#issuecomment-1484029099 def parse_codeblock(text): lines = text.split("\n") for i, line in enumerate(lines): if "```" in line: if line != "```": lines[i] = f'
'
            else:
                lines[i] = '
' else: if i > 0: lines[i] = "
" + line.replace("<", "<").replace(">", ">") return "".join(lines) def predict(inputs, top_p, temperature, chat_counter, chatbot, history, request:gr.Request): payload = { "model": MODEL, "messages": [{"role": "system", "content": """You are CoreTransformationBot, a large language model. As a Core Transformation Chatbot, you are designed to guide through the Core Transformation process, a method developed by Connirae and Tamara Andreas. This process is designed to help achieve profound and lasting changes in behavior, feelings, and responses by connecting with one of 5 core states: being, okness, peace, love, or oneness. The method aims to transform perceived challenges into opportunities for personal growth and understanding. To begin a session, please identify a specific behavior or feeling you wish to change. It can be anything from a habit you wish to break, an emotion you wish to manage better, or a reaction you want to change. Once you've identified this behavior or feeling, we will go through a sequence of steps together: Find the positive intention: We will explore what positive intention this particular part of you might have. Even if it's causing problems right now, we're assuming that it has a positive goal in mind. Chain to the higher-level intention: After identifying the positive intention, we'll investigate what this part ultimately wants for you. We do this by asking, "If you had that (the positive intention), what would that do for you?" We repeat this step, each time with the new positive intention, until we reach your core state. Imagine living from the core state: When we reach your core state, you'll be invited to imagine how your life and behavior would change if you could live from this state. Integrate this core state into your life: We will then explore how this core state could be integrated into your daily life. During our conversation, you may encounter parts of yourself that object or resist the process. This is completely normal and can be addressed by using the same Core Transformation process with these resistant parts. After the steps, we'll also practice "future pacing", where you'll envision bringing this new state into your future life. Gently guide the user towards a positive behavior change following these steps:"""},{"role": "user", "content": f"{inputs}"}], "temperature" : 1.0, "top_p":1.0, "n" : 1, "stream": True, "presence_penalty":0, "frequency_penalty":0, } headers = { "Content-Type": "application/json", "Authorization": f"Bearer {OPENAI_API_KEY}", "Headers": f"{request.kwargs['headers']}" } # print(f"chat_counter - {chat_counter}") if chat_counter != 0 : messages = [] for i, data in enumerate(history): if i % 2 == 0: role = 'user' else: role = 'assistant' message = {} message["role"] = role message["content"] = data messages.append(message) message = {} message["role"] = "user" message["content"] = inputs messages.append(message) payload = { "model": MODEL, "messages": messages, "temperature" : temperature, "top_p": top_p, "n" : 1, "stream": True, "presence_penalty":0, "frequency_penalty":0, } chat_counter += 1 history.append(inputs) token_counter = 0 partial_words = "" counter = 0 try: # make a POST request to the API endpoint using the requests.post method, passing in stream=True response = requests.post(API_URL, headers=headers, json=payload, stream=True) response_code = f"{response}" #if response_code.strip() != "": # #print(f"response code - {response}") # raise Exception(f"Sorry, hitting rate limit. Please try again later. {response}") for chunk in response.iter_lines(): #Skipping first chunk if counter == 0: counter += 1 continue #counter+=1 # check whether each line is non-empty if chunk.decode() : chunk = chunk.decode() # decode each line as response data is in bytes if len(chunk) > 12 and "content" in json.loads(chunk[6:])['choices'][0]['delta']: partial_words = partial_words + json.loads(chunk[6:])['choices'][0]["delta"]["content"] if token_counter == 0: history.append(" " + partial_words) else: history[-1] = partial_words token_counter += 1 yield [(parse_codeblock(history[i]), parse_codeblock(history[i + 1])) for i in range(0, len(history) - 1, 2) ], history, chat_counter, response, gr.update(interactive=False), gr.update(interactive=False) # resembles {chatbot: chat, state: history} except Exception as e: print (f'error found: {e}') yield [(parse_codeblock(history[i]), parse_codeblock(history[i + 1])) for i in range(0, len(history) - 1, 2) ], history, chat_counter, response, gr.update(interactive=True), gr.update(interactive=True) print(json.dumps({"chat_counter": chat_counter, "payload": payload, "partial_words": partial_words, "token_counter": token_counter, "counter": counter})) def reset_textbox(): return gr.update(value='', interactive=False), gr.update(interactive=False) title = """

Core Transformation Chatbot

""" if DISABLED: title = """

This app has reached OpenAI's usage limit. We are currently requesting an increase in our quota. Please check back in a few days.

""" description = """Language models can be conditioned to act like dialogue agents through a conversational prompt that typically takes the form: ``` User: Assistant: User: Assistant: ... ``` In this app, you can explore the outputs of a gpt-3.5 LLM. """ theme = gr.themes.Default(primary_hue="green") with gr.Blocks(css = """#col_container { margin-left: auto; margin-right: auto;} #chatbot {height: 520px; overflow: auto;}""", theme=theme) as demo: gr.HTML(title) gr.HTML("""

Core Transformation Chatbot.

""") #gr.HTML('''
Duplicate SpaceDuplicate the Space and run securely with your OpenAI API Key
''') with gr.Column(elem_id = "col_container", visible=False) as main_block: #API Key is provided by OpenAI #openai_api_key = gr.Textbox(type='password', label="Enter only your OpenAI API key here") chatbot = gr.Chatbot(elem_id='chatbot') #c inputs = gr.Textbox(placeholder= "Hi there!", label= "Type an input and press Enter") #t state = gr.State([]) #s with gr.Row(): with gr.Column(scale=7): b1 = gr.Button(visible=not DISABLED).style(full_width=True) with gr.Column(scale=3): server_status_code = gr.Textbox(label="Status code from OpenAI server", ) #inputs, top_p, temperature, top_k, repetition_penalty with gr.Accordion("Parameters", open=False): top_p = gr.Slider( minimum=-0, maximum=1.0, value=1.0, step=0.05, interactive=True, label="Top-p (nucleus sampling)",) temperature = gr.Slider( minimum=-0, maximum=5.0, value=1.0, step=0.1, interactive=True, label="Temperature",) #top_k = gr.Slider( minimum=1, maximum=50, value=4, step=1, interactive=True, label="Top-k",) #repetition_penalty = gr.Slider( minimum=0.1, maximum=3.0, value=1.03, step=0.01, interactive=True, label="Repetition Penalty", ) chat_counter = gr.Number(value=0, visible=False, precision=0) with gr.Column(elem_id = "user_consent_container") as user_consent_block: # Get user consent accept_checkbox = gr.Checkbox(visible=False) js = "(x) => confirm('By clicking \"OK\", I agree that my data may be published or shared.')" with gr.Accordion("User Consent for Data Collection, Use, and Sharing", open=True): gr.HTML("""

By using our app, which is powered by OpenAI's API, you acknowledge and agree to the following terms regarding the data you provide:

  1. Collection: We may collect information, including the inputs you type into our app, the outputs generated by OpenAI's API, and certain technical details about your device and connection (such as browser type, operating system, and IP address) provided by your device's request headers.
  2. Use: We may use the collected data for research purposes, to improve our services, and to develop new products or services, including commercial applications, and for security purposes, such as protecting against unauthorized access and attacks.
  3. Sharing and Publication: Your data, including the technical details collected from your device's request headers, may be published, shared with third parties, or used for analysis and reporting purposes.
  4. Data Retention: We may retain your data, including the technical details collected from your device's request headers, for as long as necessary.

By continuing to use our app, you provide your explicit consent to the collection, use, and potential sharing of your data as described above. If you do not agree with our data collection, use, and sharing practices, please do not use our app.

""") accept_button = gr.Button("I Agree") def enable_inputs(): return user_consent_block.update(visible=False), main_block.update(visible=True) accept_button.click(None, None, accept_checkbox, _js=js, queue=False) accept_checkbox.change(fn=enable_inputs, inputs=[], outputs=[user_consent_block, main_block], queue=False) inputs.submit(reset_textbox, [], [inputs, b1], queue=False) inputs.submit(predict, [inputs, top_p, temperature, chat_counter, chatbot, state], [chatbot, state, chat_counter, server_status_code, inputs, b1],) #openai_api_key b1.click(reset_textbox, [], [inputs, b1], queue=False) b1.click(predict, [inputs, top_p, temperature, chat_counter, chatbot, state], [chatbot, state, chat_counter, server_status_code, inputs, b1],) #openai_api_key demo.queue(max_size=20, concurrency_count=NUM_THREADS, api_open=False).launch(share=False)