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| import gradio as gr | |
| import os | |
| import sys | |
| import json | |
| import requests | |
| #Streaming endpoint | |
| MODEL = "gpt-3.5-turbo" | |
| API_URL = os.getenv("API_URL") | |
| DISABLED = os.getenv("DISABLED") == 'True' | |
| #Testing with my Open AI Key | |
| OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") | |
| #Supress errors | |
| 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'<pre><code class="{lines[i][3:]}">' | |
| else: | |
| lines[i] = '</code></pre>' | |
| else: | |
| if i > 0: | |
| lines[i] = "<br/>" + line.replace("<", "<").replace(">", ">") | |
| return "".join(lines) | |
| def predict(inputs, top_p, temperature, chat_counter, chatbot=[], history=[]): | |
| payload = { | |
| "model": MODEL, | |
| "messages": [{"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}" | |
| } | |
| # 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' | |
| temp = {} | |
| temp["role"] = role | |
| temp["content"] = data | |
| messages.append(temp) | |
| temp3 = {} | |
| temp3["role"] = "user" | |
| temp3["content"] = inputs | |
| messages.append(temp3) | |
| 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) | |
| # 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() != "<Response [200]>": | |
| raise Exception(f"Sorry, hitting rate limit. Please try again later. {response}") | |
| token_counter = 0 | |
| partial_words = "" | |
| counter = 0 | |
| for chunk in response.iter_lines(): | |
| #Skipping first chunk | |
| if counter == 0: | |
| counter+=1 | |
| continue | |
| # 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']: | |
| #if len(json.loads(chunk.decode()[6:])['choices'][0]["delta"]) == 0: | |
| # break | |
| 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 | |
| chat = [(parse_codeblock(history[i]), parse_codeblock(history[i + 1])) for i in range(0, len(history) - 1, 2) ] # convert to tuples of list | |
| token_counter+=1 | |
| yield chat, history, chat_counter, response # resembles {chatbot: chat, state: history} | |
| 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='') | |
| title = """<h1 align="center">GPT-3.5 Chatbot</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. | |
| """ | |
| 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("""<h3 align="center">This App provides you full access to GPT-3.5 (4096 token limit). You don't need any OPENAI API key.</h1>""") | |
| 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: | |
| #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 | |
| 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 and the outputs generated by OpenAI's API.</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.</li> | |
| <li><strong>Sharing and Publication:</strong> Your data 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 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(fn=enable_inputs, inputs=[], outputs=[user_consent_block, main_block]) | |
| inputs.submit( predict, [inputs, top_p, temperature, chat_counter, chatbot, state], [chatbot, state, chat_counter, server_status_code],) #openai_api_key | |
| b1.click( predict, [inputs, top_p, temperature, chat_counter, chatbot, state], [chatbot, state, chat_counter, server_status_code],) #openai_api_key | |
| b1.click(reset_textbox, [], [inputs]) | |
| inputs.submit(reset_textbox, [], [inputs]) | |
| demo.queue(max_size=20, concurrency_count=10).launch() |