import spaces
import os
import json
import subprocess
from functools import lru_cache

from cachetools import LRUCache
import threading

MODEL_CACHE = LRUCache(maxsize=3)   # max 10 models
CACHE_LOCK = threading.Lock()
from docling.document_converter import DocumentConverter


### monkey patch 
# from llama_cpp.llama_chat_format import Qwen35ChatHandler
# import llama_cpp._internals as internals


# 2️⃣ Monkey patch BEFORE creating Llama()

# _original_close = internals.LlamaModel.close

def safe_close(self):
    try:
        if hasattr(self, "sampler") and self.sampler is not None:
            return _original_close(self)
    except Exception:
        pass

# internals.LlamaModel.close = safe_close


def safe_del(self):
    try:
        self.close()
    except Exception:
        pass

# internals.LlamaModel.__del__ = safe_del




##### final verdict

# qwen 3 next 80b infrence 
#qwen 3 VL
#mini max 2.5
#gpt oss 120B     #seems unstable 










# import llama_cpp._internals as internals
# from llama_cpp.llama_chat_format import Qwen3VLChatHandler


# from llama_cpp import Llama
# from llama_cpp_agent import LlamaCppAgent, MessagesFormatterType
# from llama_cpp_agent.providers import LlamaCppPythonProvider
# from llama_cpp_agent.chat_history import BasicChatHistory
# from llama_cpp_agent.chat_history.messages import Roles
# import gradio as gr
# from huggingface_hub import hf_hub_download

# import llama_cpp
# print(llama_cpp.__file__)
# print(llama_cpp.__version__)

huggingface_token = os.getenv("HUGGINGFACE_TOKEN")

# hf_hub_download(
#     repo_id="bartowski/gemma-2-9b-it-GGUF",
#     filename="gemma-2-9b-it-Q5_K_M.gguf",
#     local_dir="./models"
# )

# hf_hub_download(
#     repo_id="bartowski/gemma-2-27b-it-GGUF",
#     filename="gemma-2-27b-it-Q5_K_M.gguf",
#     local_dir="./models"
# )

# hf_hub_download(
#     repo_id="google/gemma-2-2b-it-GGUF",
#     filename="2b_it_v2.gguf",
#     local_dir="./models",
#     token=huggingface_token
# )

# hf_hub_donload(
#     repo_id="unsloth/GLM-4.7-Flash-GGUF",
#     filename="GLM-4.7-Flash-UD-Q8_K_XL.gguf",
#     local_dir="./models",
#     token=huggingface_token
# )

# hf_hub_download(
#     repo_id="unsloth/gpt-oss-20b-GGUF",
#     filename="gpt-oss-20b-Q4_K_M.gguf",
#     local_dir="./models",
#     token=huggingface_token
# )

# hf_hub_download(
#     repo_id="unsloth/gpt-oss-20b-GGUF",
#     filename="gpt-oss-20b-Q4_K_M.gguf",
#     local_dir="./models",
#     token=huggingface_token
# )




# hf_hub_download(
#     repo_id="unsloth/Qwen3-Next-80B-A3B-Instruct-GGUF",
#     filename="Qwen3-Next-80B-A3B-Instruct-Q4_K_M.gguf",
#     local_dir="./models",
#     token=huggingface_token
# )


# hf_hub_download(
#     repo_id="unsloth/Qwen3-VL-32B-Thinking-GGUF",
#     filename="Qwen3-VL-32B-Thinking-Q8_0.gguf",
#     local_dir="./models"
# )


# hf_hub_download(
#     repo_id="unsloth/Qwen3-Coder-Next-GGUF",
#     filename="Qwen3-Coder-Next-Q4_K_M.gguf",
#     local_dir="./models"
# )

import os
import base64



import os
import tempfile
import requests
from urllib.parse import urlparse






import os




llm = None
llm_model = None

import json

def str_to_json(str_obj):
    json_obj = json.loads(str_obj)
    return json_obj



import os

@spaces.GPU(duration=70)
def a():
    print()

# 
def respond(
    message,
    history: list[tuple[str, str]],
    model,
    system_message,
    max_tokens,
    temperature,
    top_p,
    top_k,
    repeat_penalty,
):
    try:
        j_r= str_to_json(message)
        try:
            mode = j_r['mode']
        except:
            yield "bekar request "
    except Exception as e:
        print(e)
        print("not valid json")
        

    if mode=="1":
        source= j_r["source"]
        # source = "https://arxiv.org/pdf/2408.09869"  # a document via a local path or URL
        converter = DocumentConverter()
        result = converter.convert(source)
        print(result.document.export_to_markdown())
        yield str(x)

    

description = """<p align="center">Defaults to 2B (you can switch to 9B or 27B from additional inputs)</p>
<p><center>
<a href="https://huggingface.co/google/gemma-2-27b-it" target="_blank">[27B it Model]</a>
<a href="https://huggingface.co/google/gemma-2-9b-it" target="_blank">[9B it Model]</a>
<a href="https://huggingface.co/google/gemma-2-2b-it" target="_blank">[2B it Model]</a>
<a href="https://huggingface.co/bartowski/gemma-2-27b-it-GGUF" target="_blank">[27B it Model GGUF]</a>
<a href="https://huggingface.co/bartowski/gemma-2-9b-it-GGUF" target="_blank">[9B it Model GGUF]</a>
<a href="https://huggingface.co/google/gemma-2-2b-it-GGUF" target="_blank">[2B it Model GGUF]</a>
</center></p>
"""
import gradio as gr

demo = gr.ChatInterface(
    fn=respond,
    additional_inputs=[
        gr.Dropdown(
            [
                # "gemma-2-9b-it-Q5_K_M.gguf",
                # "gemma-2-27b-it-Q5_K_M.gguf",
                # # "2b_it_v2.gguf",
                # "GLM-4.7-Flash-UD-Q8_K_XL.gguf",
                # "Qwen3-Coder-Next-Q4_K_M.gguf",
                # "gpt-oss-20b-Q4_K_M.gguf",
                # "Qwen3-Next-80B-A3B-Instruct-Q4_K_M.gguf",
                "UD-Q3_K_XL/Qwen3.5-122B-A10B-UD-Q3_K_XL-00001-of-00003.gguf",
                # "Qwen3-VL-32B-Thinking-Q8_0.gguf",
                # "Q8_0/gpt-oss-120b-Q8_0-00001-of-00002.gguf"
            ],
            value= "UD-Q3_K_XL/Qwen3.5-122B-A10B-UD-Q3_K_XL-00001-of-00003.gguf",
            label="Model",
        ),
        gr.Textbox(
            value="You are a helpful assistant.",
            label="System message",
        ),
        gr.Slider(1, 4096, value=2048, step=1, label="Max tokens"),
        gr.Slider(0.1, 4.0, value=0.7, step=0.1, label="Temperature"),
        gr.Slider(0.1, 1.0, value=0.95, step=0.05, label="Top-p"),
        gr.Slider(0, 100, value=40, step=1, label="Top-k"),
        gr.Slider(0.0, 2.0, value=1.1, step=0.1, label="Repetition penalty"),
    ],
    title="Chat with Gemma 2 using llama.cpp",
    description=description,
)


# demo.launch()

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