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

from langchain_community.document_loaders import PyMuPDFLoader
import docx
from pptx import Presentation


MODEL_LIST = ["THUDM/glm-4v-9b"]
HF_TOKEN = os.environ.get("HF_TOKEN", None)
MODEL_ID = os.environ.get("MODEL_ID")
MODEL_NAME = MODEL_ID.split("/")[-1]

TITLE = "<h1>VL-Chatbox</h1>"

DESCRIPTION = f"""
<center>
<p>A SPACE FOR MY FAV VLM.
<br>
MODEL NOW: <a href="https://hf.co/{MODEL_ID}">{MODEL_NAME}</a>
<br>
TIPS: NOW SUPPORT DM & ONE IMAGE/FILE UPLOAD PER TIME.
</p></center>"""

CSS = """
.duplicate-button {
    margin: auto !important;
    color: white !important;
    background: black !important;
    border-radius: 100vh !important;
}
h1 {
    text-align: center;
    display: block;
}
"""

model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID,
    torch_dtype=torch.bfloat16,
    low_cpu_mem_usage=True,
    trust_remote_code=True
).to(0)
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)
model.eval()


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

def extract_pdf(path):
    loader = PyMuPDFLoader(path)
    data = loader.load()
    data = [x.page_content for x in data]
    content = '\n\n'.join(data)
    return content

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

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]
    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)
        return choice, content
    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):
    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}])
            choice, contents = mode_load(prompt_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})
    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(height=450)
chat_input = gr.MultimodalTextbox(
    interactive=True,
    placeholder="Enter message or upload a file one time...",
    show_label=False,

)
EXAMPLES = [
        [{"text": "Describe it in detailed", "files": ["./laptop.jpg"]}],
        [{"text": "Where it is?", "files": ["./hotel.jpg"]}],
        [{"text": "Is it real?", "files": ["./spacecat.png"]}]
]

with gr.Blocks(css=CSS, theme="soft") as demo:
    gr.HTML(TITLE)
    gr.HTML(DESCRIPTION)
    gr.DuplicateButton(value="Duplicate Space for private use", elem_classes="duplicate-button")
    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=128,
                maximum=8192,
                step=1,
                value=1024,
                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)