app.py

import spaces
import os
os.environ["LLAMA_CUBLAS"] = "1"
os.environ["GGML_CUDA_FORCE_DMMV"] = "1"
import json
import subprocess

### monkey patch 

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


# 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

# GLM 4.7 flash fast infrence
#qwen 3 VL
#mini max 2.5
# qwen 3 coder next
#gpt oss 120B










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_download(
#     repo_id="unsloth/GLM-4.7-Flash-GGUF",
#     filename="GLM-4.7-Flash-Q8_0.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"
# )
from huggingface_hub import snapshot_download

# snapshot_download(
#     repo_id="unsloth/Qwen3-Coder-Next-GGUF",
#     repo_type="model",
#     local_dir="./models/",
#     allow_patterns=["Q5_K_M/*"],   # 👈 folder inside repo
#     token=huggingface_token      # only if gated/private
# )




#### Deploy Minimax 2.5 insplace of  gpt oss 120b     its larger . and better and more recet leeases
snapshot_download(
    repo_id="unsloth/Qwen3-Coder-Next-GGUF",
    repo_type="model",
    local_dir="./models/",
    allow_patterns=["Q6_K/*"],   # 👈 folder inside repo
    token=huggingface_token      # only if gated/private
)




# llm = Llama.from_pretrained(
#     repo_id="stepfun-ai/Step-3.5-Flash-GGUF-Q4_K_S",

#     # ALWAYS first shard only here
#     filename="step3p5_flash_Q4_K_S-00001-of-00012.gguf",

#     # Download all shards
#     additional_files=[
#         f"step3p5_flash_Q4_K_S-{i:05d}-of-00012.gguf"
#         for i in range(2, 13)
#     ],

#     local_dir="./models",

#     # Performance settings
#     flash_attn=True,
#     n_gpu_layers=-1,      # use full GPU (if you have enough VRAM)
#     n_batch=2048,
#     n_ctx=4096,           # 8000 is heavy unless needed
# )


# llm = Llama.from_pretrained(
#     repo_id="stepfun-ai/Step-3.5-Flash-GGUF-Q4_K_S",
#      filename="step3p5_flash_Q4_K_S-00001-of-00012.gguf",
#     allow_patterns=["UD-TQ1_0/*.gguf"],
#     verbose=False
# )

import os

def print_tree(start_path="models"):
    for root, dirs, files in os.walk(start_path):
        level = root.replace(start_path, "").count(os.sep)
        indent = "│   " * level
        print(f"{indent}├── {os.path.basename(root)}/")
        
        subindent = "│   " * (level + 1)
        for f in files:
            print(f"{subindent}├── {f}")

print_tree("models")




import gc
import torch

def delete_llama_model(llm):
    # global llm

    if llm is not None:
        try:
            llm.close()   # 🔥 VERY IMPORTANT
        except Exception as e:
            print("Close error:", e)

        llm = None

    # Force Python garbage collection
    gc.collect()

    # Clear GPU cache (if using CUDA)
    try:
        torch.cuda.empty_cache()
        torch.cuda.ipc_collect()
        torch.cuda.synchronize()
    except:
        pass

    print("Model fully unloaded.")



llm = None
llm_model_glm = None
llm_model_qwen= None


import json

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


@spaces.GPU(duration=100)
def respond(
    message,
    history: list[tuple[str, str]],
    model,
    system_message,
    max_tokens,
    temperature,
    top_p,
    top_k,
    repeat_penalty,
):
    # chat_template = MessagesFormatterType.GEMMA_2
    chat_template = MessagesFormatterType.CHATML
    # chat_template = 

    # global llm
    # global llm_model
    # global llm_model_glm
    # global llm_model_qwen

    try:
        j_r= str_to_json(message)
        messages= j_r['messages']
        max_token= j_r['max_token']
    except Exception as e:
        print(e)
        print("not valid json")
        max_token=8196
        messages=[
            {
                "role":"user",
                "content":str(message)
            }
        ]
 
    # if llm_model_glm == None:
    llm_model_glm = Llama(
               model_path=f"models/{model}",
               flash_attn=False,
            n_gpu_layers=-1,
            n_batch=2048,        # increase
            n_ctx= max_token,          # reduce if you don’t need 8k
            n_threads=  os.cpu_count(),        # set to your CPU cores
            # use_mlock=True,
            verbose=True,
                chat_format="chatml"
            )
    x=llm_model_glm.create_chat_completion(
    messages =messages
                )
        # delete_llama_model(llm_model_glm)
    print(x)
    x= x['choices'][0]['message']['content']
    yield str(x)
        
    
    

    

    # provider = LlamaCppPythonProvider(llm)

    # agent = LlamaCppAgent(
    #     provider,
    #     system_prompt=f"{system_message}",
    #     predefined_messages_formatter_type=chat_template,
    #     debug_output=False
    # )
    
    # 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.repeat_penalty = repeat_penalty
    # # settings.stream = True
    # # settings.reasoning_effort ="low"

    # 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 = ""
    # for output in stream:
    #     outputs += output
    #     yield outputs

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",
                
                # "Qwen3-Coder-Next-Q4_K_M.gguf",
                # "gpt-oss-20b-Q4_K_M.gguf",
                # "Qwen3-Next-80B-A3B-Instruct-Q4_K_M.gguf",
                "Q6_K/Qwen3-Coder-Next-Q6_K-00001-of-00003.gguf",
                # "Qwen3-VL-32B-Thinking-Q8_0.gguf",
                # "Q8_0/gpt-oss-120b-Q8_0-00001-of-00002.gguf"
            ],
            value="Q6_K/Qwen3-Coder-Next-Q6_K-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()