app.py

import gradio as gr
import os
import sys
import json 
import requests
import glob
from PIL import Image
from io import BytesIO
import random
import numpy as np
MODEL = "gpt-3.5-turbo"
API_URL = os.getenv("API_URL")
DISABLED = os.getenv("DISABLED") == 'True'
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
NUM_THREADS = int(16)
TOTAL_K = 5
SAMPLE_IDX = []
RANDOM_POSITION = [[145 + 200 * i + 400 * (i//2), j * 110 + 900] for i in range(4) for j in range(4) ]
CURRENT_POSITION = []

print (NUM_THREADS)

def exception_handler(exception_type, exception, traceback):
    print("%s: %s" % (exception_type.__name__, exception))
sys.excepthook = exception_handler
sys.tracebacklimit = 0

def show_image_from_link(image_link):
    response = requests.get(image_link)
    img = Image.open(BytesIO(response.content))
    return img

#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'<pre><code class="{lines[i][3:]}">'
            else:
                lines[i] = '</code></pre>'
        else:
            if i > 0:
                lines[i] = "<br/>" + line.replace("<", "&lt;").replace(">", "&gt;")
    return "".join(lines)

def random_sample():
    global SAMPLE_IDX
    # SAMPLE_IDX = get_random_sample(range(25), TOTAL_K)
    SAMPLE_IDX = list(np.random.choice(range(25), TOTAL_K, replace = False))
    # SAMPLE_IDX = random.choices(range(25), k=TOTAL_K)
    image = np.array(Image.open(os.path.join(os.path.dirname(__file__), "data/Court.jpeg")))
    random_characters = [Image.open(os.path.join(os.path.dirname(__file__), f'data/characters_{idx:02}.png')).resize((64, 64)) for idx in SAMPLE_IDX]
    global CURRENT_POSITION
    # CURRENT_POSITION = get_random_sample(RANDOM_POSITION, TOTAL_K)
    CURRENT_POSITION = [ RANDOM_POSITION[k] for k in list(np.random.choice(range(len(RANDOM_POSITION)), TOTAL_K, replace = False))]
    # CURRENT_POSITION = random.choices(range(len(RANDOM_POSITION)), k = TOTAL_K)
    new_image = Image.fromarray(image).convert('RGBA')
    for k in range(TOTAL_K):
        new_image.paste(random_characters[k], CURRENT_POSITION[k], random_characters[k])
    print(CURRENT_POSITION)
    return np.array(new_image)

def reset_sample(*agents):
    image = np.array(Image.open(os.path.join(os.path.dirname(__file__), "data/Court.jpeg")))
    new_agents = []
    for k in range(TOTAL_K):
        new_agents.append('')
    return image, *[gr.Textbox.update(value=answer) for answer in new_agents]


title = """<h1 align="center">Courtroom with AI juries</h1>"""
if DISABLED:
    title = """<h1 align="center" style="color:red">This app has reached OpenAI's usage limit. We are currently requesting an increase in our quota. Please check back in a few days.</h1>"""
description = """Language models can be conditioned to act like dialogue agents through a conversational prompt that typically takes the form:
```
User: <utterance>
Assistant: <utterance>
User: <utterance>
Assistant: <utterance>
...
```
In this app, you can explore the outputs of a gpt-3.5 LLM.
"""

def submit(image, *agents):
    global CURRENT_POSITION
    balloon = Image.open(os.path.join(os.path.dirname(__file__), 'data/balloon.png')).resize((64, 64))
    new_image = Image.fromarray(image).convert('RGBA')
    for k in range(TOTAL_K):
        new_image.paste(balloon, [CURRENT_POSITION[k][0], CURRENT_POSITION[k][1] - 64], balloon)

    Answer = 'X'
    new_agents = []
    for k in range(TOTAL_K):
        new_agents.append(Answer)
        
    return np.array(new_image), *[gr.Textbox.update(value=answer) for answer in new_agents]

theme = gr.themes.Default(primary_hue="green")
agents = []
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('''<center><a href="https://huggingface.co/spaces/yuntian-deng/ChatGPT?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>Duplicate the Space and run securely with your OpenAI API Key</center>''')
    with gr.Column(elem_id = "col_container", visible=False) as main_block:

        with gr.Row():
            with gr.Column(scale=7):
                image = gr.Image(os.path.join(os.path.dirname(__file__), "data/Court.jpeg"), height = 800, width = 1600)
            with gr.Column(scale=3):
                for idx in range(TOTAL_K):
                    text = gr.Textbox(label=f"Random Sample - {idx+1}")
                    agents.append(text)

        # chatbot = gr.Chatbot(elem_id='chatbot') #c
        with gr.Row():
            with gr.Column(scale=7):
                inputs = gr.Textbox(placeholder= "Hi there!", label= "Type an input and press Enter") #t
            with gr.Column(scale=3):
                b1 = gr.Button('Submit', visible=not DISABLED)

        with gr.Row():
            with gr.Column(scale=7):
                b0 = gr.Button('Random Sample', visible=not DISABLED)
            with gr.Column(scale=3):
                b2 = gr.Button('Reset', visible=not DISABLED)

                
        #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("""
            <div>
                <p>By using our app, which is powered by OpenAI's API, you acknowledge and agree to the following terms regarding the data you provide:</p>
                <ol>
                    <li><strong>Collection:</strong> 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.</li>
                    <li><strong>Use:</strong> 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.</li>
                    <li><strong>Sharing and Publication:</strong> 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.</li>
                    <li><strong>Data Retention:</strong> We may retain your data, including the technical details collected from your device's request headers, for as long as necessary.</li>
                </ol>
                <p>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.</p>
            </div>
            """)
            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)

    b0.click(random_sample, inputs = [], outputs = [image])
    b2.click(reset_sample, inputs = [], outputs = [image, *agents])

    b1.click(submit, inputs = [image, *agents], outputs = [image, *agents])
                  
    # 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)