import os
import gradio as gr
from llama_cpp import Llama
from huggingface_hub import hf_hub_download

model = Llama(
    model_path=hf_hub_download(
        repo_id=os.environ.get("REPO_ID", "SimpleBerry/LLaMA-O1-Supervised-1129-Q2_K-GGUF"),
        filename=os.environ.get("MODEL_FILE", "LLaMA-O1-Supervised-1129-q2_k.gguf"),
    )
)

DESCRIPTION = '''
# SimpleBerry/LLaMA-O1-Supervised-1129 | Duplicate the space and set it to private for faster & personal inference for free.
SimpleBerry/LLaMA-O1-Supervised-1129: an experimental research model developed by the SimpleBerry.  
Focused on advancing AI reasoning capabilities.  

## This Space was designed by Lyte/LLaMA-O1-Supervised-1129-GGUF, Many Thanks!

**To start a new chat**, click "clear" and start a new dialog.
'''

LICENSE = """
--- MIT License ---
"""

template = "<start_of_father_id>-1<end_of_father_id><start_of_local_id>0<end_of_local_id><start_of_thought><problem>{content}<end_of_thought><start_of_rating><positive_rating><end_of_rating>\n<start_of_father_id>0<end_of_father_id><start_of_local_id>1<end_of_local_id><start_of_thought><expansion>"

def llama_o1_template(data):
    #query = data['query']
    text = template.format(content=data)
    return text

def generate_text(message, history, max_tokens=512, temperature=0.9, top_p=0.95):
    temp = ""
    input_texts = [llama_o1_template(message)]
    input_texts = [input_text.replace('<|end_of_text|>','') for input_text in input_texts]
    #print(f"input_texts[0]: {input_texts[0]}")
    inputs = model.tokenize(input_texts[0].encode('utf-8'))
    for token in model.generate(inputs, top_p=top_p, temp=temperature):
        #print(f"token: {token}")
        text = model.detokenize([token])
        #print(f"text detok: {text}")
        temp += text.decode('utf-8')
        yield temp

with gr.Blocks() as demo:
    gr.Markdown(DESCRIPTION)

    chatbot = gr.ChatInterface(
        generate_text,
        title="SimpleBerry/LLaMA-O1-Supervised-1129 | GGUF Demo",
        description="Edit Settings below if needed.",
        examples=[
            ["How many r's are in the word strawberry?"],
            ['If Diana needs to bike 10 miles to reach home and she can bike at a speed of 3 mph for two hours before getting tired, and then at a speed of 1 mph until she reaches home, how long will it take her to get home?'],
            ['Find the least odd prime factor of $2019^8+1$.'],
        ],
        cache_examples=False,
        fill_height=True
    )

    with gr.Accordion("Adjust Parameters", open=False):
        gr.Slider(minimum=128, maximum=8192, value=512, step=1, label="Max Tokens")
        gr.Slider(minimum=0.1, maximum=1.5, value=0.7, step=0.1, label="Temperature")
        gr.Slider(minimum=0.05, maximum=1.0, value=0.95, step=0.01, label="Top-p (nucleus sampling)")

    gr.Markdown(LICENSE)

if __name__ == "__main__":
    demo.launch()
# # import spaces

# import os
# import gradio as gr
# from transformers import AutoTokenizer, AutoModelForCausalLM
# from huggingface_hub import hf_hub_download, snapshot_download
# import accelerate

# accelerator = accelerate.Accelerator()

# # Load the model and tokenizer from Hugging Face
# model_path = snapshot_download(
#     repo_id=os.environ.get("REPO_ID", "SimpleBerry/LLaMA-O1-Supervised-1129")
# )

# tokenizer = AutoTokenizer.from_pretrained(model_path)
# model = AutoModelForCausalLM.from_pretrained(model_path,device_map='auto')

# DESCRIPTION = '''
# # SimpleBerry/LLaMA-O1-Supervised-1129 | Duplicate the space and set it to private for faster & personal inference for free.
# SimpleBerry/LLaMA-O1-Supervised-1129: an experimental research model developed by the SimpleBerry.  
# Focused on advancing AI reasoning capabilities.  

# ## This Space was designed by Lyte/LLaMA-O1-Supervised-1129-GGUF, Many Thanks!

# **To start a new chat**, click "clear" and start a new dialogue.
# '''

# LICENSE = """
# --- MIT License ---
# """

# template = "<start_of_father_id>-1<end_of_father_id><start_of_local_id>0<end_of_local_id><start_of_thought><problem>{content}<end_of_thought><start_of_rating><positive_rating><end_of_rating>\n<start_of_father_id>0<end_of_father_id><start_of_local_id>1<end_of_local_id><start_of_thought><expansion>"

# def llama_o1_template(data):
#     #query = data['query']
#     text = template.format(content=data)
#     return text

# def format_response(response):
#     response = response.replace('<start_of_father_id>','')
#     response = response.replace('<end_of_father_id><start_of_local_id>','👉')
#     response = response.replace('<end_of_local_id><start_of_thought>',', ')
#     response = response.replace('<end_of_thought><start_of_rating>','')
#     response = response.replace('<end_of_rating>','')
#     response = response.replace('<positive_rating>','👍')
#     response = response.replace('<negative_rating>','👎')
    
# # @spaces.GPU
# def generate_text(message, history, max_tokens=512, temperature=0.9, top_p=0.95):
#     input_text = llama_o1_template(message)
#     inputs = tokenizer(input_text, return_tensors="pt").to(accelerator.device)

#     # Generate the text with the model
#     output = model.generate(
#         **inputs,
#         max_length=max_tokens,
#         temperature=temperature,
#         top_p=top_p,
#         do_sample=True,
#     )

#     response = tokenizer.decode(output[0], skip_special_tokens=False)
#     yield response

# with gr.Blocks() as demo:
#     gr.Markdown(DESCRIPTION)

#     chatbot = gr.ChatInterface(
#         generate_text,
#         title="SimpleBerry/LLaMA-O1-Supervised-1129 | GGUF Demo",
#         description="Edit Settings below if needed.",
#         examples=[
#             ["How many r's are in the word strawberry?"],
#             ['If Diana needs to bike 10 miles to reach home and she can bike at a speed of 3 mph for two hours before getting tired, and then at a speed of 1 mph until she reaches home, how long will it take her to get home?'],
#             ['Find the least odd prime factor of $2019^8+1$.'],
#         ],
#         cache_examples=True,
#         fill_height=True,
#     )

#     with gr.Accordion("Adjust Parameters", open=False):
#         gr.Slider(minimum=1024, maximum=8192, value=2048, step=1, label="Max Tokens")
#         gr.Slider(minimum=0.1, maximum=1.5, value=0.7, step=0.1, label="Temperature")
#         gr.Slider(minimum=0.05, maximum=1.0, value=0.95, step=0.01, label="Top-p (nucleus sampling)")

#     gr.Markdown(LICENSE)

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