import os import gradio as gr from text_generation import Client, InferenceAPIClient import retrieval NUM_ANSWERS_GENERATED = 3 openchat_preprompt = ( "\n: Hi!\n: My name is Bot, model version is 0.15, part of an open-source kit for " "fine-tuning new bots! I was created by Together, LAION, and Ontocord.ai and the open-source " "community. I am not human, not evil and not alive, and thus have no thoughts and feelings, " "but I am programmed to be helpful, polite, honest, and friendly.\n" ) def get_client(model: str): if model == "Rallio67/joi2_20Be_instruct_alpha": return Client(os.getenv("JOI_API_URL")) if model == "togethercomputer/GPT-NeoXT-Chat-Base-20B": return Client(os.getenv("OPENCHAT_API_URL")) return InferenceAPIClient(model, token=os.getenv("HF_TOKEN", None)) def get_usernames(model: str): """ Returns: (str, str, str, str): pre-prompt, username, bot name, separator """ if model == "OpenAssistant/oasst-sft-1-pythia-12b": return "", "<|prompter|>", "<|assistant|>", "<|endoftext|>" if model == "Rallio67/joi2_20Be_instruct_alpha": return "", "User: ", "Joi: ", "\n\n" if model == "togethercomputer/GPT-NeoXT-Chat-Base-20B": return openchat_preprompt, ": ", ": ", "\n" return "", "User: ", "Assistant: ", "\n" def predict( model: str, inputs: str, typical_p: float, top_p: float, temperature: float, top_k: int, repetition_penalty: float, watermark: bool, chatbot, history, ): client = get_client(model) preprompt, user_name, assistant_name, sep = get_usernames(model) history.append(inputs) past = [] for data in chatbot: user_data, model_data = data if not user_data.startswith(user_name): user_data = user_name + user_data if not model_data.startswith(sep + assistant_name): model_data = sep + assistant_name + model_data past.append(user_data + model_data.rstrip() + sep) if not inputs.startswith(user_name): inputs = user_name + inputs total_inputs = preprompt + "".join(past) + inputs + sep + assistant_name.rstrip() partial_words = "" if model == "OpenAssistant/oasst-sft-1-pythia-12b": iterator = client.generate_stream( total_inputs, typical_p=typical_p, truncate=1000, watermark=watermark, max_new_tokens=500, ) else: iterator = client.generate_stream( total_inputs, top_p=top_p if top_p < 1.0 else None, top_k=top_k, truncate=1000, repetition_penalty=repetition_penalty, watermark=watermark, temperature=temperature, max_new_tokens=500, stop_sequences=[user_name.rstrip(), assistant_name.rstrip()], ) for i, response in enumerate(iterator): if response.token.special: continue partial_words = partial_words + response.token.text if partial_words.endswith(user_name.rstrip()): partial_words = partial_words.rstrip(user_name.rstrip()) if partial_words.endswith(assistant_name.rstrip()): partial_words = partial_words.rstrip(assistant_name.rstrip()) if i == 0: history.append(" " + partial_words) elif response.token.text not in user_name: history[-1] = partial_words chat = [ (history[i].strip(), history[i + 1].strip()) for i in range(0, len(history) - 1, 2) ] yield chat, history # add context retrieval part here ta = retrieval.Retrieval() ta._load_pinecone_vectorstore() question = inputs top_context_list = ta.retrieve_contexts_from_pinecone(user_question=question, topk=NUM_ANSWERS_GENERATED) print(top_context_list) def reset_textbox(): return gr.update(value="") def radio_on_change( value: str, disclaimer, typical_p, top_p, top_k, temperature, repetition_penalty, watermark, ): if value == "OpenAssistant/oasst-sft-1-pythia-12b": typical_p = typical_p.update(value=0.2, visible=True) top_p = top_p.update(visible=False) top_k = top_k.update(visible=False) temperature = temperature.update(visible=False) disclaimer = disclaimer.update(visible=False) repetition_penalty = repetition_penalty.update(visible=False) watermark = watermark.update(False) elif value == "togethercomputer/GPT-NeoXT-Chat-Base-20B": typical_p = typical_p.update(visible=False) top_p = top_p.update(value=0.25, visible=True) top_k = top_k.update(value=50, visible=True) temperature = temperature.update(value=0.6, visible=True) repetition_penalty = repetition_penalty.update(value=1.01, visible=True) watermark = watermark.update(False) disclaimer = disclaimer.update(visible=True) else: typical_p = typical_p.update(visible=False) top_p = top_p.update(value=0.95, visible=True) top_k = top_k.update(value=4, visible=True) temperature = temperature.update(value=0.5, visible=True) repetition_penalty = repetition_penalty.update(value=1.03, visible=True) watermark = watermark.update(True) disclaimer = disclaimer.update(visible=False) return ( disclaimer, typical_p, top_p, top_k, temperature, repetition_penalty, watermark, ) title = """

🔥Teaching Assistant Chatbot 🚀Streaming🚀

""" 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 multiple LLMs when prompted in this way. """ openchat_disclaimer = """
Checkout the official OpenChatKit feedback app for the full experience.
""" with gr.Blocks( css="""#col_container {margin-left: auto; margin-right: auto;} #chatbot {height: 520px; overflow: auto;}""" ) as demo: gr.HTML(title) with gr.Row(): model = gr.Radio( value="OpenAssistant/oasst-sft-1-pythia-12b", choices=[ "OpenAssistant/oasst-sft-1-pythia-12b", # "togethercomputer/GPT-NeoXT-Chat-Base-20B", "Rallio67/joi2_20Be_instruct_alpha", "google/flan-t5-xxl", "google/flan-ul2", "bigscience/bloom", "bigscience/bloomz", "EleutherAI/gpt-neox-20b", ], label="Model", interactive=True, ) with gr.Row(): with gr.Column(): use_gpt3_checkbox = gr.Checkbox(label="Include GPT-3 (paid)?") with gr.Column(): use_equation_checkbox = gr.Checkbox(label="Prioritize equations?") with gr.Row(): with gr.Column(): chatbot = gr.Chatbot(elem_id="chatbot") inputs = gr.Textbox( placeholder="Ask an Electrical Engineering question!", label="Type an input and press Enter" ) examples = gr.Examples( examples=[ ["What is a Finite State Machine?"], ["How do you design a functional a Two-Bit Gray Code Counter?"], ], inputs=[inputs], outputs=[], ) gr.Markdown("The top 3 retrieved contexts are:") with gr.Row(): with gr.Column(): # add context retrieval here context1 = gr.Textbox(label="Context 1") with gr.Column(): context2 = gr.Textbox(label="Context 2") with gr.Column(): context3 = gr.Textbox(label="Context 3") gr.Markdown("Lecture Slides:") with gr.Row(): # add CLIP part here lec_gallery = gr.Gallery(label="Lecture images", show_label=False, elem_id="gallery").style(grid=[2], height="auto") disclaimer = gr.Markdown(openchat_disclaimer, visible=False) state = gr.State([]) run_btn = gr.Button(variant='primary',) with gr.Row(): with gr.Accordion("Parameters", open=False): typical_p = gr.Slider( minimum=-0, maximum=1.0, value=0.2, step=0.05, interactive=True, label="Typical P mass", ) top_p = gr.Slider( minimum=-0, maximum=1.0, value=0.25, step=0.05, interactive=True, label="Top-p (nucleus sampling)", visible=False, ) temperature = gr.Slider( minimum=-0, maximum=5.0, value=0.6, step=0.1, interactive=True, label="Temperature", visible=False, ) top_k = gr.Slider( minimum=1, maximum=50, value=50, step=1, interactive=True, label="Top-k", visible=False, ) repetition_penalty = gr.Slider( minimum=0.1, maximum=3.0, value=1.03, step=0.01, interactive=True, label="Repetition Penalty", visible=False, ) watermark = gr.Checkbox(value=False, label="Text watermarking") model.change( lambda value: radio_on_change( value, disclaimer, typical_p, top_p, top_k, temperature, repetition_penalty, watermark, ), inputs=model, outputs=[ disclaimer, typical_p, top_p, top_k, temperature, repetition_penalty, watermark, ], ) inputs.submit( predict, [ model, inputs, typical_p, top_p, temperature, top_k, repetition_penalty, watermark, chatbot, state, ], [chatbot, state], ) run_btn.click( predict, [ model, inputs, typical_p, top_p, temperature, top_k, repetition_penalty, watermark, chatbot, state, ], [chatbot, state], ) run_btn.click(reset_textbox, [], [inputs]) inputs.submit(reset_textbox, [], [inputs]) gr.Markdown(description) demo.queue(concurrency_count=16).launch(debug=True)