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retrosynthesis_16X
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import gradio as gr
import spaces
import selfies as sf
from llama_cpp import Llama
from llama_cpp_agent import LlamaCppAgent
from llama_cpp_agent.providers import LlamaCppPythonProvider
from llama_cpp_agent.chat_history import BasicChatHistory
from llama_cpp_agent.chat_history.messages import Roles

css = """

.message-row {

    justify-content: space-evenly !important;

}

.message-bubble-border {

    border-radius: 6px !important;

}

.dark.message-bubble-border {

    border-color: #343140 !important;

}

.dark.user {

    background: #1e1c26 !important;

}

.dark.assistant.dark, .dark.pending.dark {

    background: #16141c !important;

}

"""

def get_messages_formatter_type(model_name):
    from llama_cpp_agent import MessagesFormatterType
    return MessagesFormatterType.CHATML

@spaces.GPU(duration=120)
def respond(

    message,

    history: list[tuple[str, str]],

    max_tokens,

    temperature,

    top_p,

    top_k,

    model,

):
    chat_template = get_messages_formatter_type(model)

    llm = Llama(model_path="model.guff")
    provider = LlamaCppPythonProvider(llm)

    agent = LlamaCppAgent(
        provider,
        predefined_messages_formatter_type=chat_template,
        debug_output=True
    )

    settings = provider.get_provider_default_settings()
    settings.temperature = temperature
    settings.top_k = top_k
    settings.top_p = top_p
    settings.max_tokens = max_tokens
    settings.stream = True
    settings.num_beams = 10  # Enable beam search with 10 beams

    messages = BasicChatHistory()

    for msn in history:
        user = {
            'role': Roles.user,
            'content': msn[0]
        }
        assistant = {
            'role': Roles.assistant,
            'content': msn[1]
        }
        messages.add_message(user)
        messages.add_message(assistant)

    stream = agent.get_chat_response(
        message,
        llm_sampling_settings=settings,
        chat_history=messages,
        returns_streaming_generator=True,
        print_output=False
    )

    outputs = set()  # Use a set to store unique outputs
    unique_responses = []
    prompt_length = len(message)  # Assuming `message` is the prompt

    for index, output in enumerate(stream, start=1):
        if output not in outputs:
            outputs.add(output)

            # Post-process the output
            output1 = output[prompt_length:]
            first_inst_index = output1.find("[/INST]")
            second_inst_index = output1.find("[/IN", first_inst_index + len("[/INST]") + 1)
            predicted_selfies = output1[first_inst_index + len("[/INST]") : second_inst_index].strip()
            predicted_smiles = sf.decoder(predicted_selfies)
            unique_responses.append(f"Predict {index}: {predicted_smiles}")
            yield "\n".join(unique_responses)


PLACEHOLDER = """

<div class="message-bubble-border" style="display:flex; max-width: 600px; border-radius: 8px; box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1); backdrop-filter: blur(10px);">

    <div style="padding: .5rem 1.5rem;">

        <h2 style="text-align: left; font-size: 1.5rem; font-weight: 700; margin-bottom: 0.5rem;">Chat with CausalLM 35B long (Q6K GGUF)</h2>

        <p style="text-align: left; font-size: 16px; line-height: 1.5; margin-bottom: 15px;">You can try different models from CausalLM here.<br>Running on NVIDIA A100-SXM4-80GB MIG 3g.40gb with Zero-GPU from Hugging Face.</p>

    </div>

</div>

"""

demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max tokens"),
        gr.Slider(minimum=0.1, maximum=4.0, value=1.0, step=0.1, label="Temperature"),
        gr.Slider(
            minimum=0.1,
            maximum=1.0,
            value=1.0,
            step=0.05,
            label="Top-p",
        ),
        gr.Slider(
            minimum=0,
            maximum=100,
            value=50,
            step=1,
            label="Top-k",
        )
    ],
    theme=gr.themes.Soft(primary_hue="violet", secondary_hue="violet", neutral_hue="gray", font=[gr.themes.GoogleFont("Exo"), "ui-sans-serif", "system-ui", "sans-serif"]).set(
        body_background_fill_dark="#16141c",
        block_background_fill_dark="#16141c",
        block_border_width="1px",
        block_title_background_fill_dark="#1e1c26",
        input_background_fill_dark="#292733",
        button_secondary_background_fill_dark="#24212b",
        border_color_primary_dark="#343140",
        background_fill_secondary_dark="#16141c",
        color_accent_soft_dark="transparent"
    ),
    css=css,
    retry_btn="Retry",
    undo_btn="Undo",
    clear_btn="Clear",
    submit_btn="Send",
    description="Retrosynthesis chatbot",
    chatbot=gr.Chatbot(scale=1, placeholder=PLACEHOLDER)
)

if __name__ == "__main__":
    demo.launch()