import torch
from PIL import Image
import gradio as gr
import spaces
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
import os
from threading import Thread

import pymupdf
import docx
from pptx import Presentation


MODEL_LIST = ["nikravan/glm-4vq"]

HF_TOKEN = os.environ.get("HF_TOKEN", None)
MODEL_ID = MODEL_LIST[0]
MODEL_NAME = "GLM-4vq"

TITLE = "<h1>AI CHAT DOCS</h1>"

DESCRIPTION = f"""
<center>
<p> 
<br>
USANDO MODELO: <a href="https://hf.co/nikravan/glm-4vq">{MODEL_NAME}</a>
</center>"""

CSS = """
h1 {
    text-align: center;
    display: block;
}
"""


tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)



def extract_text(path):
    return open(path, 'r').read()


def extract_pdf(path):
    doc = pymupdf.open(path)
    text = ""
    for page in doc:
        text += page.get_text()
    return text


def extract_docx(path):
    doc = docx.Document(path)
    data = []
    for paragraph in doc.paragraphs:
        data.append(paragraph.text)
    content = '\n\n'.join(data)
    return content


def extract_pptx(path):
    prs = Presentation(path)
    text = ""
    for slide in prs.slides:
        for shape in slide.shapes:
            if hasattr(shape, "text"):
                text += shape.text + "\n"
    return text


def mode_load(path):
    choice = ""
    file_type = path.split(".")[-1]
    print(file_type)
    if file_type in ["pdf", "txt", "py", "docx", "pptx", "json", "cpp", "md"]:
        if file_type.endswith("pdf"):
            content = extract_pdf(path)
        elif file_type.endswith("docx"):
            content = extract_docx(path)
        elif file_type.endswith("pptx"):
            content = extract_pptx(path)
        else:
            content = extract_text(path)
        choice = "doc"
        print(content[:100])
        return choice, content[:5000]


    elif file_type in ["png", "jpg", "jpeg", "bmp", "tiff", "webp"]:
        content = Image.open(path).convert('RGB')
        choice = "image"
        return choice, content

    else:
        raise gr.Error("Oops, unsupported files.")


@spaces.GPU()
def stream_chat(message, history: list, temperature: float, max_length: int, top_p: float, top_k: int, penalty: float):
    
    model = AutoModelForCausalLM.from_pretrained(
        MODEL_ID,
        torch_dtype=torch.bfloat16,
        low_cpu_mem_usage=True,
        trust_remote_code=True
    )
        
    print(f'message is - {message}')
    print(f'history is - {history}')
    conversation = []
    prompt_files = []
    if message["files"]:
        choice, contents = mode_load(message["files"][-1])
        if choice == "image":
            conversation.append({"role": "user", "image": contents, "content": message['text']})
        elif choice == "doc":
            format_msg = contents + "\n\n\n" + "{} files uploaded.\n" + message['text']
            conversation.append({"role": "user", "content": format_msg})
    else:
        if len(history) == 0:
            # raise gr.Error("Please upload an image first.")
            contents = None
            conversation.append({"role": "user", "content": message['text']})
        else:
            # image = Image.open(history[0][0][0])
            for prompt, answer in history:
                if answer is None:
                    prompt_files.append(prompt[0])
                    conversation.extend([{"role": "user", "content": ""}, {"role": "assistant", "content": ""}])
                else:
                    conversation.extend([{"role": "user", "content": prompt}, {"role": "assistant", "content": answer}])
            if len(prompt_files) > 0:
                choice, contents = mode_load(prompt_files[-1])
            else:
                choice = ""
                conversation.append({"role": "user", "image": "", "content": message['text']})


            if choice == "image":
                conversation.append({"role": "user", "image": contents, "content": message['text']})
            elif choice == "doc":
                format_msg = contents + "\n\n\n" + "{} files uploaded.\n" + message['text']
                conversation.append({"role": "user", "content": format_msg})
    print(f"Conversation is -\n{conversation}")

    input_ids = tokenizer.apply_chat_template(conversation, tokenize=True, add_generation_prompt=True,
                                              return_tensors="pt", return_dict=True).to(model.device)
    streamer = TextIteratorStreamer(tokenizer, timeout=60.0, skip_prompt=True, skip_special_tokens=True)

    generate_kwargs = dict(
        max_length=max_length,
        streamer=streamer,
        do_sample=True,
        top_p=top_p,
        top_k=top_k,
        temperature=temperature,
        repetition_penalty=penalty,
        eos_token_id=[151329, 151336, 151338],
    )
    gen_kwargs = {**input_ids, **generate_kwargs}

    with torch.no_grad():
        thread = Thread(target=model.generate, kwargs=gen_kwargs)
        thread.start()
        buffer = ""
        for new_text in streamer:
            buffer += new_text
            yield buffer


chatbot = gr.Chatbot(
    #rtl=True,
)
chat_input = gr.MultimodalTextbox(
    interactive=True,
    placeholder="Enter message or upload a file ...",
    show_label=False,
    #rtl=True,



)
EXAMPLES = [
    [{"text": "Resumir Documento", }],
    [{"text": "Explicar la Imagen", }],
    [{"text": "¿De que es la foto?", "files": ["perro.jpg"]}]
]

with gr.Blocks(css=CSS, theme="soft", fill_height=True) as demo:
    gr.HTML(TITLE)
    gr.HTML(DESCRIPTION)
    gr.ChatInterface(
        fn=stream_chat,
        multimodal=True,


        textbox=chat_input,
        chatbot=chatbot,
        fill_height=True,
        additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False, render=False),
        additional_inputs=[
            gr.Slider(
                minimum=0,
                maximum=1,
                step=0.1,
                value=0.8,
                label="Temperature",
                render=False,
            ),
            gr.Slider(
                minimum=1024,
                maximum=8192,
                step=1,
                value=4096,
                label="Max Length",
                render=False,
            ),
            gr.Slider(
                minimum=0.0,
                maximum=1.0,
                step=0.1,
                value=1.0,
                label="top_p",
                render=False,
            ),
            gr.Slider(
                minimum=1,
                maximum=20,
                step=1,
                value=10,
                label="top_k",
                render=False,
            ),
            gr.Slider(
                minimum=0.0,
                maximum=2.0,
                step=0.1,
                value=1.0,
                label="Repetition penalty",
                render=False,
            ),
        ],
    ),
    gr.Examples(EXAMPLES, [chat_input])

if __name__ == "__main__":
    
    demo.queue(api_open=False).launch(show_api=False, share=False, )#server_name="0.0.0.0", )