app.py

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 con Documentos </h1>"

DESCRIPTION = f"""
<center>
<p>Chat con Documentos. 
<br>
🚀 MODEL NOW: <a href="https://hf.co/nikravan/glm-4vq">{MODEL_NAME}</a>
</center>"""


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:
            contents = None
            conversation.append({"role": "user", "content": message['text']})
        else:
            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": "Quien es el Demandado?", }],
    [{"text": "Resumir el Documento", }],
    [{"text": "Explicar el Documento", }]
]

with gr.Blocks(css=CSS, theme="soft", fill_height=True) as demo:
    gr.HTML(TITLE)
    gr.HTML(DESCRIPTION)
    with gr.Column(id="chatbot-container"):
        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)