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="Qwen/Qwen3-VL-32B-Thinking-GGUF",
    filename="mmproj-Qwen3VL-32B-Thinking-F16.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/gpt-oss-120b-GGUF",
#     repo_type="model",
#     local_dir="./models/",
#     allow_patterns=["Q8_0/*"],   # 👈 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



_IMAGE_MIME_TYPES = {
    # Most common formats
    '.png':  'image/png',
    '.jpg':  'image/jpeg',
    '.jpeg': 'image/jpeg',
    '.gif':  'image/gif',
    '.webp': 'image/webp',

    # Next-generation formats
    '.avif': 'image/avif',
    '.jp2':  'image/jp2',
    '.j2k':  'image/jp2',
    '.jpx':  'image/jp2',

    # Legacy / Windows formats
    '.bmp':  'image/bmp',
    '.ico':  'image/x-icon',
    '.pcx':  'image/x-pcx',
    '.tga':  'image/x-tga',
    '.icns': 'image/icns',

    # Professional / Scientific imaging
    '.tif':  'image/tiff',
    '.tiff': 'image/tiff',
    '.eps':  'application/postscript',
    '.dds':  'image/vnd-ms.dds',
    '.dib':  'image/dib',
    '.sgi':  'image/sgi',

    # Portable Map formats (PPM/PGM/PBM)
    '.pbm':  'image/x-portable-bitmap',
    '.pgm':  'image/x-portable-graymap',
    '.ppm':  'image/x-portable-pixmap',

    # Miscellaneous / Older formats
    '.xbm':  'image/x-xbitmap',
    '.mpo':  'image/mpo',
    '.msp':  'image/msp',
    '.im':   'image/x-pillow-im',
    '.qoi':  'image/qoi',
}

def image_to_base64_data_uri(
    file_path: str,
    *,
    fallback_mime: str = "application/octet-stream"
) -> str:
    """
    Convert a local image file to a base64-encoded data URI with the correct MIME type.

    Supports 20+ image formats (PNG, JPEG, WebP, AVIF, BMP, ICO, TIFF, etc.).

    Args:
        file_path: Path to the image file on disk.
        fallback_mime: MIME type used when the file extension is unknown.

    Returns:
        A valid data URI string (e.g., data:image/webp;base64,...).

    Raises:
        FileNotFoundError: If the file does not exist.
        OSError: If reading the file fails.
    """
    if not os.path.isfile(file_path):
        raise FileNotFoundError(f"Image file not found: {file_path}")

    extension = os.path.splitext(file_path)[1].lower()
    mime_type = _IMAGE_MIME_TYPES.get(extension, fallback_mime)

    if mime_type == fallback_mime:
        print(f"Warning: Unknown extension '{extension}' for '{file_path}'. "
              f"Using fallback MIME type: {fallback_mime}")

    try:
        with open(file_path, "rb") as img_file:
            encoded_data = base64.b64encode(img_file.read()).decode("utf-8")
    except OSError as e:
        raise OSError(f"Failed to read image file '{file_path}': {e}") from e

    return f"data:{mime_type};base64,{encoded_data}"



######################   sample code ################################################
# --- Main Logic for Image Processing ---

# # 1. Create a list containing all image paths
# image_paths = [
#     r'./scene.jpeg',
#     r'./cat.png',
#     r'./network.webp',
#     # Add more image paths here if needed
# ]

# # 2. Create an empty list to store the message objects (images and text)
# images_messages = []



# # 3. Loop through the image path list, convert each image to a Data URI,
# #    and add it to the message list as an image_url object.
# for path in image_paths:
#     data_uri = image_to_base64_data_uri(path)
#     images_messages.append({"type": "image_url", "image_url": {"url": data_uri}})

# # 4. Add the final text prompt at the end of the list
# images_messages.append({"type": "text", "text": "Describes the images."})

# # 5. Use this list to build the chat_completion request
# res = llm.create_chat_completion(
#     messages=[
#         {"role": "system", "content": "You are a highly accurate vision-language assistant. Provide detailed, precise, and well-structured image descriptions."},
#         # The user's content is the list containing both images and text
#         {"role": "user", "content": images_messages}
#     ]
# )

# # Print the assistant's response
# print(res["choices"][0]["message"]["content"])




@spaces.GPU(duration=120)
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

    
    if model == "Qwen3-VL-32B-Thinking-Q8_0.gguf" :
        if llm_model_qwen == None:
            llm_model_qwen = Llama(
               model_path=f"models/Qwen3-VL-32B-Thinking-Q8_0.gguf",
                flash_attn=True,
                n_gpu_layers=-1,
                n_batch=2048,        # increase
                n_ctx= 8196,          # reduce if you don’t need 8k
                n_threads=16,        # set to your CPU cores
                use_mlock=True,
                verbose=True,
                chat_handler=Qwen3VLChatHandler(
      clip_model_path=f"models/mmproj-Qwen3VL-32B-Thinking-F16.gguf",
      force_reasoning=True,
      image_min_tokens=1024, # Note: Qwen-VL models require at minimum 1024 image tokens to function correctly on bbox grounding tasks
    ),
            )
            
        x=llm_model_qwen.create_chat_completion(
        messages = [
                  {"role": "system", "content": "hi"},
                  {
                      "role": "user",
                      "content": str(message)
                  }
              ]
                )
        print(x)
        # delete_llama_model(llm_model_qwen)
        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",
                # "GLM-4.7-Flash-Q8_0.gguf",
                # "Qwen3-Coder-Next-Q4_K_M.gguf",
                # "gpt-oss-20b-Q4_K_M.gguf",
                # "Qwen3-Next-80B-A3B-Instruct-Q4_K_M.gguf",
                "Qwen3-VL-32B-Thinking-Q8_0.gguf",
                # "Qwen3-VL-32B-Thinking-Q8_0.gguf",
                # "Q8_0/gpt-oss-120b-Q8_0-00001-of-00002.gguf"
            ],
            value="Qwen3-VL-32B-Thinking-Q8_0.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()