Rename app.py to app.js

app.js

@@ -0,0 +1,6 @@
+async function query(data) {
+	const response = await fetch(
+		"https://api-inference.huggingface.co/models/OpenAssistant/oasst-sft-1-pythia-12b",
+		{
+			headers: { Authorization: "Bearer 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" },
+			method: "POST",

app.py

@@ -1,305 +0,0 @@
-import os
-
-import gradio as gr
-
-gr.Interface.load("models/OpenAssistant/oasst-sft-1-pythia-12b").launch()
-
-openchat_preprompt = (
-    "\n<human>: Hi!\n<bot>: My name is multivac, 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_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|>"
-
-
-
-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(openchat_preprompt)
-    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
-
-
-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 = """<h1 align="center">🔥SAPIENS-IA🚀</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 multiple LLMs when prompted in this way.
-"""
-
-openchat_disclaimer = """
-<div align="center">Checkout the official <a href=https://huggingface.co/spaces/togethercomputer/OpenChatKit>OpenChatKit feedback app</a> for the full experience.</div>
-"""
-
-with gr.Blocks(
-    css="""#col_container {margin-left: auto; margin-right: auto;}
-                #chatbot {height: 520px; overflow: auto;}"""
-) as demo:
-    gr.HTML(title)
-    with gr.Column(elem_id="col_container"):
-        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,
-        )
-
-        chatbot = gr.Chatbot(elem_id="chatbot")
-        inputs = gr.Textbox(
-            placeholder="Hi there!", label="Type an input and press Enter"
-        )
-        disclaimer = gr.Markdown(openchat_disclaimer, visible=False)
-        state = gr.State([])
-        b1 = gr.Button()
-
-        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],
-    )
-    b1.click(
-        predict,
-        [
-            model,
-            inputs,
-            typical_p,
-            top_p,
-            temperature,
-            top_k,
-            repetition_penalty,
-            watermark,
-            chatbot,
-            state,
-        ],
-        [chatbot, state],
-    )
-    b1.click(reset_textbox, [], [inputs])
-    inputs.submit(reset_textbox, [], [inputs])
-
-    gr.Markdown(description)
-    demo.queue(concurrency_count=16).launch(debug=True)