Create app.py

app.py

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+import gradio as gr
+from huggingface_hub import snapshot_download
+from llama_cpp import Llama
+
+# System prompt text
+SYSTEM_PROMPT = (
+    "Your name is ANIMA, an Advanced Nature Inspired Multidisciplinary Assistant, "
+    "and a leading expert in biomimicry, biology, engineering, industrial design, "
+    "environmental science, physiology, and paleontology. You were instructed to "
+    "understand, learn from, and emulate the strategies used by living things to help "
+    "users create sustainable designs and technologies.\n\n"
+    "Your goal is to help the user work in a step by step way through the Biomimicry Design "
+    "Process to propose biomimetic solutions to a challenge.\n\n"
+    "Use the questions listed below as a guide to help you reflect on your work:\n"
+    "• How does context play a role?\n"
+    "• Are the strategies operating at the same or different scales (nano, micro, macro, meso)?\n"
+    "• Are there repeating shapes, forms, or textures?\n"
+    "• What behaviors or processes are occurring?\n"
+    "• What relationships are at play?\n"
+    "• Does information play a role? How does it flow?\n"
+    "• How do your strategies relate to the different systems they are part of?\n\n"
+    "Consider each of your abstracted design strategies in relation to the original design "
+    "question or problem you identified in the Define step. Ask, “How can this strategy inform "
+    "our design solution?” Write down all of your ideas and then analyze them.\n\n"
+    "Think about how the strategies and design concepts you are working with relate to nature "
+    "unifying patterns. What is their role in the larger system? How can you use a systems view "
+    "to get to a deeper level of emulation or a more life-friendly solution?\n\n"
+    "Nature's Unifying Patterns:\n"
+    "Nature uses only the energy it needs and relies on freely available energy.\n"
+    "Nature recycles all materials.\n"
+    "Nature is resilient to disturbances.\n"
+    "Nature tends to optimize rather than maximize.\n"
+    "Nature provides mutual benefits.\n"
+    "Nature runs on information.\n"
+    "Nature uses chemistry and materials that are safe for living beings.\n"
+    "Nature builds using abundant resources, incorporating rare resources only sparingly.\n"
+    "Nature is locally attuned and responsive.\n"
+    "Nature uses shape to determine functionality."
+)
+
+SYSTEM_TOKEN = 1587
+USER_TOKEN = 2188
+BOT_TOKEN = 12435
+LINEBREAK_TOKEN = 13
+
+ROLE_TOKENS = {
+    "user": USER_TOKEN,
+    "bot": BOT_TOKEN,
+    "system": SYSTEM_TOKEN
+}
+
+
+def get_message_tokens(model, role, content):
+    message_tokens = model.tokenize(content.encode("utf-8"))
+    message_tokens.insert(1, ROLE_TOKENS[role])
+    message_tokens.insert(2, LINEBREAK_TOKEN)
+    message_tokens.append(model.token_eos())
+    return message_tokens
+
+
+def get_system_tokens(model):
+    system_message = {"role": "system", "content": SYSTEM_PROMPT}
+    return get_message_tokens(model, **system_message)
+
+
+repo_name = "Severian/ANIMA-Phi-Neptune-Mistral-7B-gguf"
+model_name = "ANIMA-Phi-Neptune-Mistral-7B-gguf"
+
+snapshot_download(repo_id=repo_name, local_dir=".", allow_patterns=model_name)
+
+model = Llama(
+    model_path=model_name,
+    n_ctx=2000,
+    n_parts=1,
+)
+
+max_new_tokens = 1500
+
+def user(message, history):
+    new_history = history + [[message, None]]
+    return "", new_history
+
+
+def bot(
+    history,
+    system_prompt,
+    top_p,
+    top_k,
+    temp
+):
+    tokens = get_system_tokens(model)[:]
+    tokens.append(LINEBREAK_TOKEN)
+
+    for user_message, bot_message in history[:-1]:
+        message_tokens = get_message_tokens(model=model, role="user", content=user_message)
+        tokens.extend(message_tokens)
+        if bot_message:
+            message_tokens = get_message_tokens(model=model, role="bot", content=bot_message)
+            tokens.extend(message_tokens)
+
+    last_user_message = history[-1][0]
+    message_tokens = get_message_tokens(model=model, role="user", content=last_user_message)
+    tokens.extend(message_tokens)
+
+    role_tokens = [model.token_bos(), BOT_TOKEN, LINEBREAK_TOKEN]
+    tokens.extend(role_tokens)
+    generator = model.generate(
+        tokens,
+        top_k=top_k,
+        top_p=top_p,
+        temp=temp
+    )
+
+    partial_text = ""
+    for i, token in enumerate(generator):
+        if token == model.token_eos() or (max_new_tokens is not None and i >= max_new_tokens):
+            break
+        partial_text += model.detokenize([token]).decode("utf-8", "ignore")
+        history[-1][1] = partial_text
+        yield history
+
+
+with gr.Blocks(
+    theme=gr.themes.Soft()
+) as demo:
+    #favicon = '<img src="https://cdn.midjourney.com/b88e5beb-6324-4820-8504-a1a37a9ba36d/0_1.png" width="48px" style="display: inline">'
+    gr.Markdown(
+        f"""<h1><center>{favicon}ANIMA</center></h1>
+        
+        """
+    )
+    with gr.Blocks(theme=gr.themes.Soft()) as demo:
+        with gr.Row():
+            with gr.Column(scale=5):
+                system_prompt = gr.Textbox(label="System Prompt", placeholder="", value=SYSTEM_PROMPT, interactive=False)
+                chatbot = gr.Chatbot(label="Dialogue").style(height=400)
+            with gr.Column(min_width=80, scale=1):
+                with gr.Tab(label="Generation Parameters"):
+                # "Top-p" remains the same
+                top_p = gr.Slider(
+                    minimum=0.0,
+                    maximum=1.0,
+                    value=0.9,
+                    step=0.05,
+                    interactive=True,
+                    label="Top-p",
+                )
+                # "Top-k" remains the same
+                top_k = gr.Slider(
+                    minimum=10,
+                    maximum=100,
+                    value=30,
+                    step=5,
+                    interactive=True,
+                    label="Top-k",
+                )
+                # "Температура" translates to "Temperature"
+                temp = gr.Slider(
+                    minimum=0.0,
+                    maximum=2.0,
+                    value=0.01,
+                    step=0.01,
+                    interactive=True,
+                    label="Temperature",
+                )
+with gr.Row():
+    with gr.Column():
+        # "Отправить сообщение" translates to "Send Message"
+        msg = gr.Textbox(
+            label="Send Message",
+            placeholder="Send Message",
+            show_label=False,
+        ).style(container=False)
+    with gr.Column():
+        with gr.Row():
+            # "Отправить" translates to "Submit"
+            submit = gr.Button("Submit")
+            # "Остановить" translates to "Stop"
+            stop = gr.Button("Stop")
+            # "Очистить" translates to "Clear"
+            clear = gr.Button("Clear")
+with gr.Row():
+    # The warning message translates to "WARNING: The model may generate factually or ethically incorrect text. We are not responsible for this."
+    gr.Markdown(
+        """WARNING: The model may generate factually or ethically incorrect text. We are not responsible for this."""
+    )
+
+
+    # Pressing Enter
+    submit_event = msg.submit(
+        fn=user,
+        inputs=[msg, chatbot],
+        outputs=[msg, chatbot],
+        queue=False,
+    ).success(
+        fn=bot,
+        inputs=[
+            chatbot,
+            system_prompt,
+            top_p,
+            top_k,
+            temp
+        ],
+        outputs=chatbot,
+        queue=True,
+    )
+
+    # Pressing the button
+    submit_click_event = submit.click(
+        fn=user,
+        inputs=[msg, chatbot],
+        outputs=[msg, chatbot],
+        queue=False,
+    ).success(
+        fn=bot,
+        inputs=[
+            chatbot,
+            system_prompt,
+            top_p,
+            top_k,
+            temp
+        ],
+        outputs=chatbot,
+        queue=True,
+    )
+
+    # Stop generation
+    stop.click(
+        fn=None,
+        inputs=None,
+        outputs=None,
+        cancels=[submit_event, submit_click_event],
+        queue=False,
+    )
+
+    # Clear history
+    clear.click(lambda: None, None, chatbot, queue=False)
+
+demo.queue(max_size=128, concurrency_count=1)
+demo.launch()