Delete app_ISF.py

app_ISF.py

@@ -1,417 +0,0 @@
-import json
-import os
-import logging
-import sys
-import torch
-import gradio as gr
-from huggingface_hub import Repository
-from text_generation import Client
-from app_modules.utils import convert_to_markdown
-# from dialogues import DialogueTemplate
-from share_btn import (community_icon_html, loading_icon_html, share_btn_css,
-                       share_js)
-
-HF_TOKEN = os.environ.get("HF_TOKEN", None)
-API_TOKEN = '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'
-API_URL = os.environ.get("API_URL", None)
-API_URL = "https://api-inference.huggingface.co/models/timdettmers/guanaco-33b-merged"
-
-client = Client(
-    API_URL,
-    headers={"Authorization": f"Bearer {API_TOKEN}"},
-)
-
-repo = None
-
-logging.basicConfig(
-    format="%(asctime)s [%(levelname)s] [%(name)s] %(message)s",
-    datefmt="%Y-%m-%dT%H:%M:%SZ",
-)
-logger = logging.getLogger(__name__)
-logger.setLevel(logging.DEBUG)
-
-examples = [
-    "Describe the advantages and disadvantages of Incremental Sheet Forming.",
-    "Describe the applications of Incremental Sheet Forming.",
-    "Describe the process parameters included in Incremental Sheet Forming in dot points."
-]
-
-
-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
-
-
-header = "A chat between a curious human and an artificial intelligence assistant about Incremental Sheet Forming (ISF). " \
-         "The assistant gives helpful, detailed, and polite answers to the user's questions."
-prompt_template = "### Human: {query}\n### Assistant:{response}"
-
-
-def generate(
-        user_message,
-        chatbot,
-        history,
-        temperature,
-        top_p,
-        top_k,
-        max_new_tokens,
-        repetition_penalty,
-):
-    # Don't return meaningless message when the input is empty
-    if not user_message:
-        print("Empty input")
-
-    history.append(user_message)
-
-    past_messages = []
-    for data in chatbot:
-        user_data, model_data = data
-
-        past_messages.extend(
-            [{"role": "user", "content": user_data}, {"role": "assistant", "content": model_data.rstrip()}]
-        )
-
-    if len(past_messages) < 1:
-        prompt = header + prompt_template.format(query=user_message, response="")
-    else:
-        prompt = header
-        for i in range(0, len(past_messages), 2):
-            intermediate_prompt = prompt_template.format(query=past_messages[i]["content"],
-                                                         response=past_messages[i + 1]["content"])
-            print("intermediate: ", intermediate_prompt)
-            prompt = prompt + '\n' + intermediate_prompt
-
-        prompt = prompt + prompt_template.format(query=user_message, response="")
-
-    temperature = float(temperature)
-    if temperature < 1e-2:
-        temperature = 1e-2
-    top_p = float(top_p)
-
-    generate_kwargs = dict(
-        temperature=temperature,
-        max_new_tokens=max_new_tokens,
-        top_p=top_p,
-        top_k=top_k,
-        repetition_penalty=repetition_penalty,
-        do_sample=True,
-        truncate=999,
-        seed=42,
-    )
-
-    stream = client.generate_stream(
-        prompt,
-        **generate_kwargs,
-    )
-
-    output = ""
-    for idx, response in enumerate(stream):
-        if response.token.text == '':
-            break
-
-        if response.token.special:
-            continue
-        output += response.token.text
-        if idx == 0:
-            history.append(" " + output)
-        else:
-            history[-1] = output
-
-        chat = [(convert_to_markdown(history[i].strip()), convert_to_markdown(history[i + 1].strip())) for i in range(0, len(history) - 1, 2)]
-
-        yield chat, history, user_message, ""
-
-    return chat, history, user_message, ""
-
-
-def clear_chat():
-    return [], []
-
-
-def save(
-        history,
-        temperature=0.7,
-        top_p=0.9,
-        top_k=50,
-        max_new_tokens=512,
-        repetition_penalty=1.2,
-        max_memory=1024,
-):
-    history = [] if history is None else history
-    data_point = {'history': history, 'generation_parameter': {
-        "temperature": temperature,
-        "top_p": top_p,
-        "top_k": top_k,
-        "max_new_tokens": max_new_tokens,
-        "repetition_penalty": repetition_penalty,
-        "max_memory": max_memory,
-    }}
-    print(data_point)
-    file_name = "history.jsonl"
-    with open(file_name, 'a') as f:
-        for line in [data_point]:
-            f.write(json.dumps(line, ensure_ascii=False) + '\n')
-
-
-def process_example(args):
-    for [x, y] in generate(args):
-        pass
-    return [x, y]
-
-
-title = """<h1 align="center">ISF Alpaca 💬</h1>"""
-custom_css = """
-#banner-image {
-    display: block;
-    margin-left: auto;
-    margin-right: auto;
-}
-#chat-message {
-    font-size: 14px;
-    min-height: 300px;
-}
-"""
-
-with gr.Blocks(analytics_enabled=False,
-               theme=gr.themes.Soft(),
-               css=".disclaimer {font-variant-caps: all-small-caps;}") as demo:
-    gr.HTML(title)
-    # status_display = gr.Markdown("Success", elem_id="status_display")
-    with gr.Row():
-        with gr.Column():
-            gr.Markdown(
-                """
-            🏭 The fine-tuned model primarily emphasizes **Knowledge Augmentation** in the Manufacturing domain, 
-            with **Incremental Sheet Forming (ISF)** serving as a use case.
-    """
-            )
-    history = gr.components.State()
-
-    with gr.Row(scale=1).style(equal_height=True):
-        with gr.Column(scale=5):
-            with gr.Row(scale=1):
-                chatbot = gr.Chatbot(elem_id="chuanhu_chatbot").style(height=476)
-            with gr.Row(scale=1):
-                with gr.Column(scale=12):
-                    user_message = gr.Textbox(
-                        show_label=False, placeholder="Enter text"
-                    ).style(container=False)
-                with gr.Column(min_width=70, scale=1):
-                    submit_btn = gr.Button("Send")
-                with gr.Column(min_width=70, scale=1):
-                    stop_btn = gr.Button("Stop")
-            with gr.Row():
-                gr.Examples(
-                    examples=examples,
-                    inputs=[user_message],
-                    cache_examples=False,
-                    outputs=[chatbot, history],
-                )
-            with gr.Row(scale=1):
-                clear_history = gr.Button(
-                    "🧹 New Conversation",
-                )
-                reset_btn = gr.Button("🔄 Reset Parameter")
-                save_btn = gr.Button("📥 Save Chat")
-        with gr.Column():
-            input_component_column = gr.Column(min_width=50, scale=1)
-            with input_component_column:
-                with gr.Tab(label="Parameter Setting"):
-                    gr.Markdown("# Parameters")
-                    temperature = gr.components.Slider(minimum=0, maximum=1, value=0.7, label="Temperature")
-                    top_p = gr.components.Slider(minimum=0, maximum=1, value=0.9, label="Top p")
-                    top_k = gr.components.Slider(minimum=0, maximum=100, step=1, value=20, label="Top k")
-                    max_new_tokens = gr.components.Slider(minimum=1, maximum=2048, step=1, value=512,
-                                                          label="Max New Tokens")
-                    repetition_penalty = gr.components.Slider(minimum=0.1, maximum=10.0, step=0.1, value=1.2,
-                                                              label="Repetition Penalty")
-                    max_memory = gr.components.Slider(minimum=0, maximum=2048, step=1, value=2048, label="Max Memory")
-
-        history = gr.State([])
-        last_user_message = gr.State("")
-
-        user_message.submit(
-            generate,
-            inputs=[
-                user_message,
-                chatbot,
-                history,
-                temperature,
-                top_p,
-                top_k,
-                max_new_tokens,
-                repetition_penalty,
-            ],
-            outputs=[chatbot, history, last_user_message, user_message],
-        )
-
-        submit_event = submit_btn.click(
-            generate,
-            inputs=[
-                user_message,
-                chatbot,
-                history,
-                temperature,
-                top_p,
-                top_k,
-                max_new_tokens,
-                repetition_penalty,
-            ],
-            outputs=[chatbot, history, last_user_message, user_message],
-        )
-        # submit_btn.click(
-        #     lambda: (
-        #         submit_btn.update(visible=False),
-        #         stop_btn.update(visible=True),
-        #     ),
-        #     inputs=None,
-        #     outputs=[submit_btn, stop_btn],
-        #     queue=False,
-        # )
-
-        stop_btn.click(
-            lambda: (
-                submit_btn.update(visible=True),
-                stop_btn.update(visible=True),
-            ),
-            inputs=None,
-            outputs=[submit_btn, stop_btn],
-            cancels=[submit_event],
-            queue=False,
-        )
-
-        clear_history.click(clear_chat, outputs=[chatbot, history])
-        save_btn.click(
-            save,
-            inputs=[user_message, chatbot, history, temperature, top_p, top_k, max_new_tokens, repetition_penalty],
-            outputs=None,
-        )
-
-        input_components_except_states = [user_message, chatbot, history, temperature, top_p, top_k, max_new_tokens,
-                                          repetition_penalty]
-
-        reset_btn.click(
-            None,
-            [],
-            (input_components_except_states + [input_component_column]),  # type: ignore
-            _js=f"""() => {json.dumps([getattr(component, "cleared_value", None) for component in input_components_except_states]
-                                      + ([gr.Column.update(visible=True)])
-                                      + ([])
-                                      )}
-                    """,
-        )
-
-    demo.queue(concurrency_count=16).launch(debug=True, share=True)
-
-    # with gr.Row():
-    #     with gr.Box():
-    #         output = gr.Markdown()
-    #         chatbot = gr.Chatbot(elem_id="chat-message", label="Chat")
-    #
-    # with gr.Row():
-    #     with gr.Column(scale=3):
-    #         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)
-    #
-    #             clear_chat_button = gr.Button("Clear chat", elem_id="clear-btn", visible=True)
-    #
-    #         with gr.Accordion(label="Parameters", open=False, elem_id="parameters-accordion"):
-    #             temperature = gr.Slider(
-    #                 label="Temperature",
-    #                 value=0.7,
-    #                 minimum=0.0,
-    #                 maximum=1.0,
-    #                 step=0.1,
-    #                 interactive=True,
-    #                 info="Higher values produce more diverse outputs",
-    #             )
-    #             top_p = gr.Slider(
-    #                 label="Top-p (nucleus sampling)",
-    #                 value=0.9,
-    #                 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=1024,
-    #                 minimum=0,
-    #                 maximum=2048,
-    #                 step=4,
-    #                 interactive=True,
-    #                 info="The maximum numbers of new tokens",
-    #             )
-    #             repetition_penalty = gr.Slider(
-    #                 label="Repetition Penalty",
-    #                 value=1.2,
-    #                 minimum=0.0,
-    #                 maximum=10,
-    #                 step=0.1,
-    #                 interactive=True,
-    #                 info="The parameter for repetition penalty. 1.0 means no penalty.",
-    #             )
-    #         with gr.Row():
-    #             gr.Examples(
-    #                 examples=examples,
-    #                 inputs=[user_message],
-    #                 cache_examples=False,
-    #                 fn=process_example,
-    #                 outputs=[output],
-    #             )
-    #
-    # history = gr.State([])
-    # last_user_message = gr.State("")
-    #
-    # user_message.submit(
-    #     generate,
-    #     inputs=[
-    #         user_message,
-    #         chatbot,
-    #         history,
-    #         temperature,
-    #         top_p,
-    #         max_new_tokens,
-    #         repetition_penalty,
-    #     ],
-    #     outputs=[chatbot, history, last_user_message, user_message],
-    # )
-    #
-    # send_button.click(
-    #     generate,
-    #     inputs=[
-    #         user_message,
-    #         chatbot,
-    #         history,
-    #         temperature,
-    #         top_p,
-    #         max_new_tokens,
-    #         repetition_penalty,
-    #     ],
-    #     outputs=[chatbot, history, last_user_message, user_message],
-    # )
-    #
-    # clear_chat_button.click(clear_chat, outputs=[chatbot, history])
-
-demo.queue(concurrency_count=16).launch(debug=True, share=True)