import gradio as gr
from gpt4all import GPT4All
from huggingface_hub import hf_hub_download

title = "S O L A R"
description = """
Is it really that good? Let's see... (Note: This is a Q4 gguf so thst I can run it on the free cpu. Clone and upgrade for a getter version)
"""

model_path = "TheBloke/openchat-3.5-0106-GGUF"
model_name = "openchat-3.5-0106.Q4_K_M.gguf"
hf_hub_download(repo_id="TheBloke/openchat-3.5-0106-GGUF", filename=model_name, local_dir=model_path, local_dir_use_symlinks=True)

print("Start the model init process")
model = model = GPT4All(model_name, model_path, allow_download = True, device="cpu")
print("Finish the model init process")

model.config["promptTemplate"] = '''GPT4 Correct User: {0}<|end_of_turn|>GPT4 Correct Assistant:

'''
model.config["systemPrompt"] = "You are a helpful assistant named 兮辞."
model._is_chat_session_activated = True

max_new_tokens = 2048

def generater(message, history, temperature, top_p, top_k):
    prompt = ""
    for user_message, assistant_message in history:
        prompt += model.config["promptTemplate"].format(user_message)
        prompt += assistant_message + "<|end_of_turn|>"
    prompt += model.config["promptTemplate"].format(message)
    outputs = []    
    for token in model.generate(prompt=prompt, temp=temperature, top_k = top_k, top_p = top_p, max_tokens = max_new_tokens, streaming=True):
        outputs.append(token)
        yield "".join(outputs)

def vote(data: gr.LikeData):
    if data.liked:
        return
    else:
        return

chatbot = gr.Chatbot(avatar_images=('resourse/user-icon.png', 'resourse/chatbot-icon.png'),bubble_full_width = False)

additional_inputs=[
    gr.Slider(
        label="temperature",
        value=0.5,
        minimum=0.0,
        maximum=2.0,
        step=0.05,
        interactive=True,
        info="Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic.",
    ),
    gr.Slider(
        label="top_p",
        value=1.0,
        minimum=0.0,
        maximum=1.0,
        step=0.01,
        interactive=True,
        info="0.1 means only the tokens comprising the top 10% probability mass are considered. Suggest set to 1 and use temperature. 1 means 100% and will disable it",
    ),
    gr.Slider(
        label="top_k",
        value=40,
        minimum=0,
        maximum=1000,
        step=1,
        interactive=True,
        info="limits candidate tokens to a fixed number after sorting by probability. Setting it higher than the vocabulary size deactivates this limit.",
    )
]


iface = gr.ChatInterface(
    fn = generater,
    title=title,
    description = description,
    additional_inputs=additional_inputs,
    examples=[
        ["Can you tell me how the Namib Desert Beetle inspires water collection methods?"],
        ["I'm working on a project related to sustainable architecture. How can biomimicry guide my design process?"],
        ["Can you explain the concept of biomimicry and its importance in today’s world?"],
        ["I need some ideas for a biomimicry project in my biology class. Can you suggest some organisms to study?"],
        ["How does the structure of a lotus leaf help in creating self-cleaning surfaces?"]
    ]
)


with gr.Blocks(css="resourse/style/custom.css") as demo:
    chatbot.like(vote, None, None)
    iface.render()

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