Update app.py

app.py

@@ -1,3 +1,295 @@
-import gradio
+"""Run codes"""
+# import gradio
+
+# gradio.load("models/WizardLM/WizardCoder-15B-V1.0").launch()
+import os
+import time
+from types import SimpleNamespace
+
+import gradio as gr
+from about_time import about_time
+from ctransformers import AutoConfig, AutoModelForCausalLM
+from huggingface_hub import hf_hub_download
+from loguru import logger
+
+os.environ["TZ"] = "Asia/Shanghai"
+try:
+    time.tzset()  # type: ignore # pylint: disable=no-member
+except Exception:
+    # Windows
+    logger.warning("Windows, cant run time.tzset()")
+
+ns = SimpleNamespace(
+    response="",
+    generator=[],
+)
+
+
+def predict(prompt, bot):
+    # logger.debug(f"{prompt=}, {bot=}, {timeout=}")
+    logger.debug(f"{prompt=}, {bot=}")
+
+    ns.response = ""
+    with about_time() as atime:  # type: ignore
+        try:
+            # user_prompt = prompt
+            generator = generate(llm, generation_config, system_prompt, prompt.strip())
+            print(assistant_prefix, end=" ", flush=True)
+
+            response = ""
+            buff.update(value="diggin...")
+
+            for word in generator:
+                # print(word, end="", flush=True)
+                print(word, flush=True)  # vertical stream
+                response += word
+                ns.response = response
+                buff.update(value=response)
+            print("")
+            logger.debug(f"{response=}")
+        except Exception as exc:
+            logger.error(exc)
+            response = f"{exc=}"
+
+    # bot = {"inputs": [response]}
+    _ = (
+        f"(time elapsed: {atime.duration_human}, "  # type: ignore
+        f"{atime.duration/(len(prompt) + len(response)):.1f}s/char)"  # type: ignore
+    )
+
+    bot.append([prompt, f"{response} {_}"])
+
+    return prompt, bot
+
+
+def predict_api(prompt):
+    logger.debug(f"{prompt=}")
+    ns.response = ""
+    try:
+        # user_prompt = prompt
+        generator = generate(llm, generation_config, system_prompt, prompt.strip())
+        print(assistant_prefix, end=" ", flush=True)
+
+        response = ""
+        buff.update(value="diggin...")
+        for word in generator:
+            print(word, end="", flush=True)
+            response += word
+            ns.response = response
+            buff.update(value=response)
+        print("")
+        logger.debug(f"{response=}")
+    except Exception as exc:
+        logger.error(exc)
+        response = f"{exc=}"
+    # bot = {"inputs": [response]}
+    # bot = [(prompt, response)]
+
+    return response
+
+
+def download_quant(destination_folder: str, repo_id: str, model_filename: str):
+    local_path = os.path.abspath(destination_folder)
+    return hf_hub_download(
+        repo_id=repo_id,
+        filename=model_filename,
+        local_dir=local_path,
+        local_dir_use_symlinks=True,
+    )
+
+
+logger.info("start dl")
+_ = """full url: https://huggingface.co/TheBloke/mpt-30B-chat-GGML/blob/main/mpt-30b-chat.ggmlv0.q4_1.bin"""
+
+# https://huggingface.co/TheBloke/mpt-30B-chat-GGML
+_ = """
+mpt-30b-chat.ggmlv0.q4_0.bin 	q4_0 	4 	16.85 GB 	19.35 GB 	4-bit.
+mpt-30b-chat.ggmlv0.q4_1.bin 	q4_1 	4 	18.73 GB 	21.23 GB 	4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models.
+mpt-30b-chat.ggmlv0.q5_0.bin 	q5_0 	5 	20.60 GB 	23.10 GB
+mpt-30b-chat.ggmlv0.q5_1.bin 	q5_1 	5 	22.47 GB 	24.97 GB
+mpt-30b-chat.ggmlv0.q8_0.bin 	q8_0 	8 	31.83 GB 	34.33 GB
+"""
+model_filename = "mpt-30b-chat.ggmlv0.q4_1.bin"
+model_filename = "WizardCoder-15B-1.0.ggmlv3.q4_0.bin"  # 10.7G
+model_filename = "WizardCoder-15B-1.0.ggmlv3.q4_1.bin"  # 11.9G
+destination_folder = "models"
+
+repo_id = "TheBloke/mpt-30B-chat-GGML"
+if "WizardCoder" in model_filename:
+    repo_id = "TheBloke/WizardCoder-15B-1.0-GGML"
+    
+download_quant(destination_folder, repo_id, model_filename)
+
+logger.info("done dl")
+
+if "mpt" in model_filename:
+    config = AutoConfig.from_pretrained("mosaicml/mpt-30b-chat", context_length=8192)
+    llm = AutoModelForCausalLM.from_pretrained(
+        os.path.abspath(f"models/{model_filename}"),
+        model_type="mpt",
+        config=config,
+    )
+
+# https://huggingface.co/spaces/matthoffner/wizardcoder-ggml/blob/main/main.py
+if "WizardCoder" in model_filename:
+    llm = AutoModelForCausalLM.from_pretrained("TheBloke/WizardCoder-15B-1.0-GGML",
+                                            model_file="",
+                                            model_type="starcoder",
+                                            threads=8)
+
+system_prompt = "A conversation between a user and an LLM-based AI assistant named Local Assistant. Local Assistant gives helpful and honest answers."
+
+user_prefix = "[user]: "
+assistant_prefix = "[assistant]: "
+
+css = """
+    .importantButton {
+        background: linear-gradient(45deg, #7e0570,#5d1c99, #6e00ff) !important;
+        border: none !important;
+    }
+    .importantButton:hover {
+        background: linear-gradient(45deg, #ff00e0,#8500ff, #6e00ff) !important;
+        border: none !important;
+    }
+    .disclaimer {font-variant-caps: all-small-caps; font-size: xx-small;}
+    .xsmall {font-size: x-small;}
+"""
+
+with gr.Blocks(
+    # title="mpt-30b-chat-ggml",
+    title=f"{model_filename}",
+    theme=gr.themes.Soft(text_size="sm", spacing_size="sm"),
+    css=css,
+) as block:
+    with gr.Accordion("🎈 Info", open=False):
+        # gr.HTML(
+        #     """<center><a href="https://huggingface.co/spaces/mikeee/mpt-30b-chat?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate"></a> and spin a CPU UPGRADE to avoid the queue</center>"""
+        # )
+        gr.Markdown(
+            f"""<h4><center>{model_filename}</center></h4>
+
+            The examples are meant for another model. You should try with
+            some coder-related prompts.
+
+            Try to refresh the browser and try again when occasionally errors occur.
+
+            It takes about >100 seconds to get a response. Restarting the space takes about 5 minutes if the space is asleep due to inactivity. If the space crashes for some reason, it will also take about 5 minutes to restart. You need to refresh the browser to reload the new space.
+            """,
+            elem_classes="xsmall",
+        )
+    conversation = Chat()
+    chatbot = gr.Chatbot(scroll_to_output=True).style(height=700)  # 500
+    buff = gr.Textbox(show_label=False)
+    with gr.Row():
+        with gr.Column(scale=1):
+            msg = gr.Textbox(
+                label="Chat Message Box",
+                placeholder="Ask me anything (press Enter or click Submit to send)",
+                show_label=False,
+            ).style(container=False)
+        with gr.Column(scale=0.1):
+            with gr.Row():
+                submit = gr.Button("Submit", elem_classes="xsmall")
+                stop = gr.Button("Stop", visible=False)
+                clear = gr.Button("Clear History", visible=True)
+    with gr.Row(visible=False):
+        with gr.Accordion("Advanced Options:", open=False):
+            with gr.Row():
+                with gr.Column(scale=2):
+                    system = gr.Textbox(
+                        label="System Prompt",
+                        value=Chat.default_system_prompt,
+                        show_label=False,
+                    ).style(container=False)
+                with gr.Column():
+                    with gr.Row():
+                        change = gr.Button("Change System Prompt")
+                        reset = gr.Button("Reset System Prompt")
+
+    with gr.Accordion("Example inputs", open=True):
+        etext = """In America, where cars are an important part of the national psyche, a decade ago people had suddenly started to drive less, which had not happened since the oil shocks of the 1970s. """
+        examples = gr.Examples(
+            examples=[
+                ["Explain the plot of Cinderella in a sentence."],
+                [
+                    "How long does it take to become proficient in French, and what are the best methods for retaining information?"
+                ],
+                ["What are some common mistakes to avoid when writing code?"],
+                ["Build a prompt to generate a beautiful portrait of a horse"],
+                ["Suggest four metaphors to describe the benefits of AI"],
+                ["Write a pop song about leaving home for the sandy beaches."],
+                ["Write a summary demonstrating my ability to tame lions"],
+                ["鲁迅和周树人什么关系 说中文"],
+                ["鲁迅和周树人什么关系"],
+                ["鲁迅和周树人什么关系 用英文回答"],
+                ["从前有一头牛，这头牛后面有什么？"],
+                ["正无穷大加一大于正无穷大吗？"],
+                ["正无穷大加正无穷大大于正无穷大吗？"],
+                ["-2的平方根等于什么"],
+                ["树上有5只鸟，猎人开枪打死了一只。树上还有几只鸟？"],
+                ["树上有11只鸟，猎人开枪打死了一只。树上还有几只鸟？提示：需考虑鸟可能受惊吓飞走。"],
+                ["以红楼梦的行文风格写一张委婉的请假条。不少于320字。"],
+                [f"{etext} 翻成中文，列出3个版本"],
+                [f"{etext} \n 翻成中文，保留原意，但使用文学性的语言。不要写解释。列出3个版本"],
+                ["js 判断一个数是不是质数"],
+                ["js 实现python 的 range(10)"],
+                ["js 实现python 的 [*(range(10)]"],
+                ["假定 1 + 2 = 4, 试求 7 + 8"],
+                ["Erkläre die Handlung von Cinderella in einem Satz."],
+                ["Erkläre die Handlung von Cinderella in einem Satz. Auf Deutsch"],
+            ],
+            inputs=[msg],
+            examples_per_page=40,
+        )
+
+    # with gr.Row():
+    with gr.Accordion("Disclaimer", open=False):
+        gr.Markdown(
+            f"Disclaimer: {"-".join(model_filename.split("-")[:2])} can produce factually incorrect output, and should not be relied on to produce "
+            "factually accurate information. MPT-30B was trained on various public datasets; while great efforts "
+            "have been taken to clean the pretraining data, it is possible that this model could generate lewd, "
+            "biased, or otherwise offensive outputs.",
+            elem_classes=["disclaimer"],
+        )
+
+    msg.submit(
+        # fn=conversation.user_turn,
+        fn=predict0,
+        inputs=[msg, chatbot],
+        outputs=[msg, chatbot],
+        queue=True,
+        show_progress="full",
+        api_name="predict",
+    )
+    submit.click(
+        # fn=predict0,
+        fn=lambda x, y: ("",) + predict0(x, y)[1:],  # clear msg
+        inputs=[msg, chatbot],
+        outputs=[msg, chatbot],
+        queue=True,
+        show_progress="full",
+    )
+    clear.click(lambda: None, None, chatbot, queue=False)
+
+    # update buff Textbox, every: units in seconds)
+    # https://huggingface.co/spaces/julien-c/nvidia-smi/discussions
+    # does not work
+    # AttributeError: 'Blocks' object has no attribute 'run_forever'
+    # block.run_forever(lambda: ns.response, None, [buff], every=1)
+
+    with gr.Accordion("For Chat/Translation API", open=False, visible=False):
+        input_text = gr.Text()
+        api_btn = gr.Button("Go", variant="primary")
+        out_text = gr.Text()
+    api_btn.click(
+        predict_api,
+        input_text,
+        out_text,
+        # show_progress="full",
+        api_name="api",
+    )
+
+# concurrency_count=5, max_size=20
+# max_size=36, concurrency_count=14
+block.queue(concurrency_count=5, max_size=20).launch(debug=True)
 
-gradio.load("models/WizardLM/WizardCoder-15B-V1.0").launch()