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| from openai import OpenAI | |
| import gradio as gr | |
| import os | |
| import json | |
| import html | |
| import random | |
| import datetime | |
| api_key = os.environ.get('FEATHERLESS_API_KEY') | |
| if not api_key: | |
| raise RuntimeError("Cannot start without required API key. Please register for one at https://featherless.ai") | |
| client = OpenAI( | |
| base_url="https://api.featherless.ai/v1", | |
| api_key=api_key | |
| ) | |
| with open('./model-cache.json', 'r') as f_model_cache: | |
| model_cache = json.load(f_model_cache) | |
| model_class_from_model_id = { model_id: model_class for model_class, model_ids in model_cache.items() for model_id in model_ids } | |
| model_class_filter = { | |
| "mistral-v02-7b-std-lc": True, | |
| "llama3-8b-8k": True, | |
| "llama31-8b-16k": True, | |
| "llama2-solar-10b7-4k": True, | |
| "mistral-nemo-12b-lc": True, | |
| "llama2-13b-4k": True, | |
| "llama3-15b-8k": True, | |
| "qwen2-32b-lc":False, | |
| "llama3-70b-8k":False, | |
| "llama31-70b-16k": False, | |
| "qwen2-72b-lc":False, | |
| "mixtral-8x22b-lc":False, | |
| "llama3-405b-lc":False, | |
| } | |
| # we run a few other models here as well | |
| REFLECTION="mattshumer/Reflection-Llama-3.1-70B" | |
| QWEN25_72B="Qwen/Qwen2.5-72B" | |
| NEMOTRON="nvidia/Llama-3.1-Nemotron-70B-Instruct-HF" | |
| bigger_whitelisted_models = [ | |
| QWEN25_72B, | |
| NEMOTRON | |
| ] | |
| # REFLECTION is in backup hosting | |
| model_class_from_model_id[REFLECTION] = 'llama31-70b-16k' | |
| model_class_from_model_id[NEMOTRON] = 'llama31-70b-16k' | |
| def build_model_choices(): | |
| all_choices = [] | |
| for model_class in model_cache: | |
| if model_class not in model_class_filter: | |
| print(f"Warning: new model class {model_class}. Treating as blacklisted") | |
| continue | |
| if not model_class_filter[model_class]: | |
| continue | |
| all_choices += [ (f"{model_id} ({model_class})", model_id) for model_id in model_cache[model_class] ] | |
| all_choices += [ (f"{model_id}, {model_class_from_model_id[model_id]}", model_id) for model_id in bigger_whitelisted_models ] | |
| return all_choices | |
| model_choices = build_model_choices() | |
| def model_in_list(model): | |
| for label, id in model_choices: | |
| if id == model: | |
| return True | |
| return False | |
| # let's use a random but different model each day. | |
| key=os.environ.get('RANDOM_SEED', 'kcOtfNHA+e') | |
| o = random.Random(f"{key}-{datetime.date.today().strftime('%Y-%m-%d')}") | |
| initial_model = o.choice(model_choices)[1] | |
| initial_model = NEMOTRON | |
| # this doesn't work in HF spaces because we're iframed :( | |
| # def initial_model(referer=None): | |
| # return REFLECTION | |
| # if referer == 'http://127.0.0.1:7860/': | |
| # return 'Sao10K/Venomia-1.1-m7' | |
| # if referer and referer.startswith("https://huggingface.co/"): | |
| # possible_model = referer[23:] | |
| # full_model_list = functools.reduce(lambda x,y: x+y, model_cache.values(), []) | |
| # model_is_supported = possible_model in full_model_list | |
| # if model_is_supported: | |
| # return possible_model | |
| # # let's use a random but different model each day. | |
| # key=os.environ.get('RANDOM_SEED', 'kcOtfNHA+e') | |
| # o = random.Random(f"{key}-{datetime.date.today().strftime('%Y-%m-%d')}") | |
| # return o.choice(model_choices)[1] | |
| REFLECTION_SYSTEM_PROMPT = """You are a world-class AI system, capable of complex reasoning and reflection. Reason through the query inside <thinking> tags, and then provide your final response inside <output> tags. If you detect that you made a mistake in your reasoning at any point, correct yourself inside <reflection> tags.""" | |
| def respond(message, history, model): | |
| # insist on that model is in model_choices | |
| if not model_in_list(model): | |
| raise RuntimeError(f"{model} is not supported in this hf space. Visit https://featherless.ai to see and use the complete model catalogue") | |
| history_openai_format = [] | |
| for human, assistant in history: | |
| history_openai_format.append({"role": "user", "content": human }) | |
| history_openai_format.append({"role": "assistant", "content":assistant}) | |
| history_openai_format.append({"role": "user", "content": message}) | |
| if model == REFLECTION: | |
| history_openai_format = [ | |
| {"role": "system", "content": REFLECTION_SYSTEM_PROMPT}, | |
| *history_openai_format | |
| ] | |
| response = client.chat.completions.create( | |
| model=model, | |
| messages= history_openai_format, | |
| temperature=1.0, | |
| stream=True, | |
| max_tokens=2000, | |
| extra_headers={ | |
| 'HTTP-Referer': 'https://huggingface.co/spaces/featherless-ai/try-this-model', | |
| 'X-Title': "HF's missing inference widget" | |
| } | |
| ) | |
| partial_message = "" | |
| for chunk in response: | |
| if chunk.choices[0].delta.content is not None: | |
| content = chunk.choices[0].delta.content | |
| escaped_content = html.escape(content) | |
| partial_message += escaped_content | |
| yield partial_message | |
| logo = open('./logo.svg').read() | |
| logo_small = open('./logo-small.svg').read() | |
| title_text="HuggingFace's missing inference widget" | |
| css = """ | |
| .logo-mark { fill: #ffe184; } | |
| /* from https://github.com/gradio-app/gradio/issues/4001 | |
| * necessary as putting ChatInterface in gr.Blocks changes behaviour | |
| */ | |
| .row { | |
| display: flex; | |
| justify-content: center; | |
| } | |
| .footer p { | |
| width: 450px; | |
| } | |
| .contain { display: flex; flex-direction: column; } | |
| .gradio-container { height: 100vh !important; } | |
| #component-0 { height: 100%; } | |
| #chatbot { flex-grow: 1; overflow: auto;} | |
| """ | |
| with gr.Blocks(title_text, css=css) as demo: | |
| gr.HTML(f""" | |
| <div class="header"> | |
| <h1 class="row">HuggingFace's missing inference widget</h1> | |
| <h3 class="row">powered by</h3> | |
| <div class="row"> | |
| <a href="https://featherless.ai"> | |
| {logo} | |
| </a> | |
| </div> | |
| </div> | |
| """) | |
| # hidden_state = gr.State(value=initial_model) | |
| with gr.Row(): | |
| model_selector = gr.Dropdown( | |
| label="Select your Model", | |
| choices=build_model_choices(), | |
| value=initial_model, | |
| # value=hidden_state, | |
| scale=4 | |
| ) | |
| gr.Button( | |
| value="Visit Model Card ↗️", | |
| scale=1 | |
| ).click( | |
| inputs=[model_selector], | |
| js="(model_selection) => { window.open(`https://huggingface.co/${model_selection}`, '_blank') }", | |
| fn=None, | |
| ) | |
| gr.ChatInterface( | |
| respond, | |
| additional_inputs=[model_selector], | |
| head=""", | |
| <script>console.log("Hello from gradio!")</script> | |
| """, | |
| concurrency_limit=5 | |
| ) | |
| # logo_small_no_text = open('./logo-small-no-text.svg').read() | |
| # x_logo = open('./x-logo.svg').read() | |
| # discord_logo = open('./discord-logo.svg').read() | |
| gr.HTML(f""" | |
| <div class="footer"> | |
| <div class="row"> | |
| If you enjoyed this space, | |
| check out <a href="https://featherless.ai">featherless.ai</a>, | |
| and follow us <a href="https://x.com/featherless.ai">on twitter</a>! | |
| </div> | |
| <!-- <div class="row">If you enjoyed this space,</div> | |
| <div class="row">check out <a href="https://featherless.ai">featherless.ai</a>,</div> | |
| <div class="row">and follow us <a href="https://x.com/FeatherlessAI">on twitter</a>!</div> --> | |
| </div> | |
| """) | |
| # def update_initial_model_choice(request: gr.Request): | |
| # return initial_model(request.headers.get('referer')) | |
| # demo.load(update_initial_model_choice, outputs=model_selector) | |
| demo.launch() | |