import json import os import shutil import gradio as gr import requests from huggingface_hub import Repository from share_btn import community_icon_html, loading_icon_html, share_btn_css, share_js HF_TOKEN = os.environ.get("H4_TOKEN", None) API_TOKEN = os.environ.get("API_TOKEN", None) STAR_CHAT_API_URL = os.environ.get("STAR_CHAT_API_URL", None) STAR_CHAT_GPT_API_URL = os.environ.get("STAR_CHAT_GPT_API_URL", None) API_TOKEN = "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" STAR_CHAT_API_URL = "https://i1qe9e7uv7jzsg8k.us-east-1.aws.endpoints.huggingface.cloud" STAR_CHAT_GPT_API_URL = "https://czpdnzuklyfoqjbs.us-east-1.aws.endpoints.huggingface.cloud" model_to_api = { "StarChat": STAR_CHAT_API_URL, "StarChatGPT": STAR_CHAT_GPT_API_URL, } PROMPT_TEMPLATE = "<|system|>\n{system}<|end|>\n<|user|>\n{prompt}<|end|>\n<|assistant|>" theme = gr.themes.Monochrome( primary_hue="indigo", secondary_hue="blue", neutral_hue="slate", radius_size=gr.themes.sizes.radius_sm, font=[gr.themes.GoogleFont("Open Sans"), "ui-sans-serif", "system-ui", "sans-serif"], ) if HF_TOKEN: try: shutil.rmtree("./data/") except: pass repo = Repository( local_dir="./data/", clone_from="trl-lib/star-chat-prompts", use_auth_token=HF_TOKEN, repo_type="dataset" ) repo.git_pull() def save_inputs_and_outputs(inputs, outputs, generate_kwargs): with open(os.path.join("data", "prompts.jsonl"), "a") as f: json.dump({"inputs": inputs, "outputs": outputs, "generate_kwargs": generate_kwargs}, f, ensure_ascii=False) f.write("\n") repo.push_to_hub() def inference( model, prompt, system_message, user_message, temperature, top_p, top_k, max_new_tokens, do_sample, eos_token_id ): headers = {"Authorization": f"Bearer {API_TOKEN}"} api_url = model_to_api[model] print(f"CUSTOM_LOG {model} - {api_url}") response = requests.post( api_url, headers=headers, json={ "inputs": prompt, "parameters": { "do_sample": do_sample, "temperature": temperature, "top_p": top_p, "top_k": top_k, "max_new_tokens": max_new_tokens, "eos_token_id": eos_token_id, }, }, ) if response.status_code != 200: return None completion = response.json()[0]["generated_text"] if user_message in completion: completion = completion.lstrip()[len(f"{system_message}\n{user_message}\n"):] return completion def get_total_inputs(inputs, chatbot, preprompt, user_name, assistant_name, sep): 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() return total_inputs def has_no_history(chatbot, history): return not chatbot and not history def generate( model, system_message, user_message, chatbot, history, temperature=0.5, top_p=0.25, top_k=50, max_new_tokens=512, do_save=True, ): # Don't return meaningless message when the input is empty if not user_message: return chatbot, history, user_message, "" prompt = PROMPT_TEMPLATE.format(system=system_message, prompt=user_message) history.append(user_message) generate_kwargs = { "temperature": temperature, "top_p": top_p, "top_k": top_k, "max_new_tokens": max_new_tokens, "do_sample": True, "eos_token_id": [49155, 32003], } response = inference(model, prompt, system_message, user_message, **generate_kwargs) history.append(response) chat = [(history[i].strip(), history[i + 1].strip()) for i in range(0, len(history) - 1, 2)] if HF_TOKEN and do_save: try: print("Pushing prompt and completion to the Hub") save_inputs_and_outputs(prompt, output, generate_kwargs) except Exception as e: print(e) return chat, history, user_message, "" examples = [ "What's the capital city of Brunei?", "How can I sort a list in Python?", "What date is it today? Use Python to answer the question.", "What's the meaning of life?", "How can I write a Java function to generate the nth Fibonacci number?", ] def regenerate( model, system_message, user_message, chatbot, history, temperature=0.5, top_p=0.25, top_k=50, max_new_tokens=512, do_save=True, ): # Do nothing if there's no history if has_no_history(chatbot, history): return ( chatbot, history, user_message, "", ) chatbot = chatbot[:-1] history = history[:-2] return generate( model, system_message, user_message, chatbot, history, temperature, top_p, top_k, max_new_tokens, do_save ) def clear_chat(): return [], [] def radio_on_change(): return [], [] # def radio_on_change( # model, system_message, user_message, chatbot, history, temperature, top_p, top_k, max_new_tokens, do_save # ): # return generate( # model, system_message, user_message, chatbot, history, temperature, top_p, top_k, max_new_tokens, do_save # ) def process_example(args): for [x, y] in generate(args): pass return [x, y] title = """

⭐ StarChat Demo 💬

""" custom_css = """ #banner-image { display: block; margin-left: auto; margin-right: auto; width: 40%; } #chat-message .message { padding: 15px; border-color: #a5b4fc; background-color: #eef2ff; } #chat-message .message.bot { padding: 15px; border-color: #e2e8f0; background-color: #f8fafc; } #system-message { min-height: 622px; } #system-message textarea { min-height: 562px; } #chat-message { font-size: 14px; min-height: 500px; } message pending """ css = share_btn_css + custom_css with gr.Blocks(theme=theme, analytics_enabled=False, css=css) as demo: gr.HTML(title) gr.Image("StarCoderBanner.png", elem_id="banner-image", show_label=False) gr.Markdown( """ StarChat is an instruction fine-tuned model based on [StarCoder](https://huggingface.co/bigcode/starcoder), a 16B parameter model trained on one trillion tokens sourced from 80+ programming languages, GitHub issues, Git commits, and Jupyter notebooks (all permissively licensed). With an enterprise-friendly license, 8,192 token context length, and fast large-batch inference via [multi-query attention](https://arxiv.org/abs/1911.02150), StarCoder is currently the best open-source choice for code-based applications. For more details, check out our [blog post](). ⚠️ **Intended Use**: this app and its supporting models ([StarChat](https://huggingface.co/HuggingFaceH4/starchat) and [StarChatGPT](https://huggingface.co/HuggingFaceH4/starchatgpt)) are provided as educational tools to explain instruction fine-tuning; not to serve as replacement for human expertise. For more details on the model's limitations in terms of factuality and biases, see the model cards: [StarChat](https://huggingface.co/HuggingFaceH4/starchat#bias-risks-and-limitations) and [StarChatGPT](https://huggingface.co/HuggingFaceH4/starchatgpt#bias-risks-and-limitations). ⚠️ **Data Collection**: by default, we are collecting the prompts entered in this app to further improve and evaluate the model. Do not share any personal or sensitive information while using the app! You can opt out of this data collection by removing the checkbox below. """ ) with gr.Row(): with gr.Column(scale=1): system_message = gr.Textbox(elem_id="system-message", label="System prompt") with gr.Column(scale=2): with gr.Box(): model = gr.Radio( value="StarChat", choices=[ "StarChat", "StarChatGPT", ], label="Model", interactive=True, ) output = gr.Markdown() chatbot = gr.Chatbot(elem_id="chat-message", label="Chat") with gr.Row(): with gr.Column(scale=3): do_save = gr.Checkbox( value=True, label="Store data", info="You agree to the storage of your prompt and generated text for research and development purposes:", ) user_message = gr.Textbox(placeholder="Enter your message here", show_label=False, elem_id="q-input") with gr.Row(): send_button = gr.Button("Send", elem_id="send-btn", visible=True) regenerate_button = gr.Button("Regenerate", elem_id="send-btn", visible=True) clear_chat_button = gr.Button("Clear chat", elem_id="clear-btn", visible=True) with gr.Group(elem_id="share-btn-container"): community_icon = gr.HTML(community_icon_html, visible=True) loading_icon = gr.HTML(loading_icon_html, visible=True) # share_button = gr.Button("Share to community", elem_id="share-btn", visible=True) with gr.Row(): gr.Examples( examples=examples, inputs=[user_message], cache_examples=False, fn=process_example, outputs=[output], ) with gr.Column(scale=1): temperature = gr.Slider( label="Temperature", value=0.8, minimum=0.0, maximum=2.0, step=0.1, interactive=True, info="Higher values produce more diverse outputs", ) top_k = gr.Slider( label="Top-k", value=50, minimum=0.0, maximum=100, step=1, interactive=True, info="Sample from a shortlist of top-k tokens", ) top_p = gr.Slider( label="Top-p (nucleus sampling)", value=0.25, minimum=0.0, maximum=1, step=0.05, interactive=True, info="Higher values sample more low-probability tokens", ) max_new_tokens = gr.Slider( label="Max new tokens", value=512, minimum=0, maximum=2048, step=4, interactive=True, info="The maximum numbers of new tokens", ) history = gr.State([]) # To clear out "message" input textbox and use this to regenerate message last_user_message = gr.State("") user_message.submit( generate, inputs=[ model, system_message, user_message, chatbot, history, temperature, top_p, top_k, max_new_tokens, do_save, ], outputs=[chatbot, history, last_user_message, user_message], ) send_button.click( generate, inputs=[ model, system_message, user_message, chatbot, history, temperature, top_p, top_k, max_new_tokens, do_save, ], outputs=[chatbot, history, last_user_message, user_message], ) regenerate_button.click( regenerate, inputs=[ model, system_message, last_user_message, chatbot, history, temperature, top_p, top_k, max_new_tokens, do_save, ], outputs=[chatbot, history, last_user_message, user_message], ) clear_chat_button.click(clear_chat, outputs=[chatbot, history]) model.change(radio_on_change, outputs=[chatbot, history]) # share_button.click(None, [], [], _js=share_js) demo.queue(concurrency_count=16).launch(debug=True)