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 = ["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 = "

Multimodal Model for Complex Doc Extraction

" DESCRIPTION = f"""

😊 A Demo For Complex Doc Extraction via GLM4.
🚀 MODEL NOW: {MODEL_NAME}
✨ Important: Do not upload any sensitive documents.
🙇‍♂️ May be rebuilding from time to time.

""" CSS = """ 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): 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): 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() 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": ["./IMG_2700.png"]}], [{"text": "Where it is?", "files": ["./hotel.jpg"]}], [{"text": "Is it real?", "files": ["./spacecat.png"]}] ] 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)