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| from transformers import AutoModel, AutoTokenizer | |
| import os | |
| import ipdb | |
| import gradio as gr | |
| import mdtex2html | |
| from model.openllama import OpenLLAMAPEFTModel | |
| import torch | |
| import json | |
| from header import TaskType, LoraConfig | |
| # init the model | |
| args = { | |
| 'model': 'openllama_peft', | |
| 'imagebind_ckpt_path': 'pretrained_ckpt/imagebind_ckpt', | |
| 'vicuna_ckpt_path': 'openllmplayground/vicuna_7b_v0', | |
| 'delta_ckpt_path': 'pretrained_ckpt/pandagpt_ckpt/7b/pytorch_model.pt', | |
| 'stage': 2, | |
| 'max_tgt_len': 128, | |
| 'lora_r': 32, | |
| 'lora_alpha': 32, | |
| 'lora_dropout': 0.1, | |
| } | |
| model = OpenLLAMAPEFTModel(**args) | |
| delta_ckpt = torch.load(args['delta_ckpt_path'], map_location=torch.device('cpu')) | |
| model.load_state_dict(delta_ckpt, strict=False) | |
| model = model.half().cuda().eval() if torch.cuda.is_available() else model.eval() | |
| print(f'[!] init the model over ...') | |
| """Override Chatbot.postprocess""" | |
| def postprocess(self, y): | |
| if y is None: | |
| return [] | |
| for i, (message, response) in enumerate(y): | |
| y[i] = ( | |
| None if message is None else mdtex2html.convert((message)), | |
| None if response is None else mdtex2html.convert(response), | |
| ) | |
| return y | |
| gr.Chatbot.postprocess = postprocess | |
| def parse_text(text): | |
| """copy from https://github.com/GaiZhenbiao/ChuanhuChatGPT/""" | |
| lines = text.split("\n") | |
| lines = [line for line in lines if line != ""] | |
| count = 0 | |
| for i, line in enumerate(lines): | |
| if "```" in line: | |
| count += 1 | |
| items = line.split('`') | |
| if count % 2 == 1: | |
| lines[i] = f'<pre><code class="language-{items[-1]}">' | |
| else: | |
| lines[i] = f'<br></code></pre>' | |
| else: | |
| if i > 0: | |
| if count % 2 == 1: | |
| line = line.replace("`", "\`") | |
| line = line.replace("<", "<") | |
| line = line.replace(">", ">") | |
| line = line.replace(" ", " ") | |
| line = line.replace("*", "*") | |
| line = line.replace("_", "_") | |
| line = line.replace("-", "-") | |
| line = line.replace(".", ".") | |
| line = line.replace("!", "!") | |
| line = line.replace("(", "(") | |
| line = line.replace(")", ")") | |
| line = line.replace("$", "$") | |
| lines[i] = "<br>"+line | |
| text = "".join(lines) | |
| return text | |
| def predict( | |
| input, | |
| image_path, | |
| audio_path, | |
| video_path, | |
| thermal_path, | |
| chatbot, | |
| max_length, | |
| top_p, | |
| temperature, | |
| history, | |
| modality_cache, | |
| ): | |
| if image_path is None and audio_path is None and video_path is None and thermal_path is None: | |
| return [(input, "There is no image/audio/video provided. Please upload the file to start a conversation.")] | |
| else: | |
| print(f'[!] image path: {image_path}\n[!] audio path: {audio_path}\n[!] video path: {video_path}\n[!] thermal pah: {thermal_path}') | |
| # prepare the prompt | |
| prompt_text = '' | |
| for idx, (q, a) in enumerate(history): | |
| if idx == 0: | |
| prompt_text += f'{q}\n### Assistant: {a}\n###' | |
| else: | |
| prompt_text += f' Human: {q}\n### Assistant: {a}\n###' | |
| if len(history) == 0: | |
| prompt_text += f'{input}' | |
| else: | |
| prompt_text += f' Human: {input}' | |
| response = model.generate({ | |
| 'prompt': prompt_text, | |
| 'image_paths': [image_path] if image_path else [], | |
| 'audio_paths': [audio_path] if audio_path else [], | |
| 'video_paths': [video_path] if video_path else [], | |
| 'thermal_paths': [thermal_path] if thermal_path else [], | |
| 'top_p': top_p, | |
| 'temperature': temperature, | |
| 'max_tgt_len': max_length, | |
| 'modality_embeds': modality_cache | |
| }) | |
| chatbot.append((parse_text(input), parse_text(response))) | |
| history.append((input, response)) | |
| return chatbot, history, modality_cache | |
| def reset_user_input(): | |
| return gr.update(value='') | |
| def reset_state(): | |
| return None, None, None, None, [], [], [] | |
| with gr.Blocks() as demo: | |
| gr.HTML("""<h1 align="center">PandaGPT</h1>""") | |
| gr.Markdown('''We note that the current online demo uses the 7B version of PandaGPT due to the limitation of computation resource. | |
| Better results should be expected when switching to the 13B version of PandaGPT. | |
| For more details on how to run 13B PandaGPT, please refer to our [main project repository](https://github.com/yxuansu/PandaGPT). | |
| Many thanks to Huggingface for providing us with the GPU grant to support our demo 🤗！ | |
| We apologize for the internal error of pytorchvideo library that occurs when parsing videos in concurrent requests. We are actively working on resolving this issue 😤''') | |
| with gr.Row(scale=4): | |
| with gr.Column(scale=2): | |
| image_path = gr.Image(type="filepath", label="Image", value=None) | |
| gr.Examples( | |
| [ | |
| os.path.join(os.path.dirname(__file__), "assets/images/bird_image.jpg"), | |
| os.path.join(os.path.dirname(__file__), "assets/images/dog_image.jpg"), | |
| os.path.join(os.path.dirname(__file__), "assets/images/car_image.jpg"), | |
| ], | |
| image_path | |
| ) | |
| with gr.Column(scale=2): | |
| audio_path = gr.Audio(type="filepath", label="Audio", value=None) | |
| gr.Examples( | |
| [ | |
| os.path.join(os.path.dirname(__file__), "assets/audios/bird_audio.wav"), | |
| os.path.join(os.path.dirname(__file__), "assets/audios/dog_audio.wav"), | |
| os.path.join(os.path.dirname(__file__), "assets/audios/car_audio.wav"), | |
| ], | |
| audio_path | |
| ) | |
| with gr.Row(scale=4): | |
| with gr.Column(scale=2): | |
| video_path = gr.Video(type='file', label="Video") | |
| gr.Examples( | |
| [ | |
| os.path.join(os.path.dirname(__file__), "assets/videos/world.mp4"), | |
| os.path.join(os.path.dirname(__file__), "assets/videos/a.mp4"), | |
| ], | |
| video_path | |
| ) | |
| with gr.Column(scale=2): | |
| thermal_path = gr.Image(type="filepath", label="Thermal Image", value=None) | |
| gr.Examples( | |
| [ | |
| os.path.join(os.path.dirname(__file__), "assets/thermals/190662.jpg"), | |
| os.path.join(os.path.dirname(__file__), "assets/thermals/210009.jpg"), | |
| ], | |
| thermal_path | |
| ) | |
| chatbot = gr.Chatbot() | |
| with gr.Row(): | |
| with gr.Column(scale=4): | |
| with gr.Column(scale=12): | |
| user_input = gr.Textbox(show_label=False, placeholder="Input...", lines=10).style(container=False) | |
| with gr.Column(min_width=32, scale=1): | |
| submitBtn = gr.Button("Submit", variant="primary") | |
| with gr.Column(scale=1): | |
| emptyBtn = gr.Button("Clear History") | |
| max_length = gr.Slider(0, 512, value=128, step=1.0, label="Maximum length", interactive=True) | |
| top_p = gr.Slider(0, 1, value=0.01, step=0.01, label="Top P", interactive=True) | |
| temperature = gr.Slider(0, 1, value=0.8, step=0.01, label="Temperature", interactive=True) | |
| history = gr.State([]) | |
| modality_cache = gr.State([]) | |
| submitBtn.click( | |
| predict, [ | |
| user_input, | |
| image_path, | |
| audio_path, | |
| video_path, | |
| thermal_path, | |
| chatbot, | |
| max_length, | |
| top_p, | |
| temperature, | |
| history, | |
| modality_cache, | |
| ], [ | |
| chatbot, | |
| history, | |
| modality_cache | |
| ], | |
| show_progress=True | |
| ) | |
| submitBtn.click(reset_user_input, [], [user_input]) | |
| emptyBtn.click(reset_state, outputs=[ | |
| image_path, | |
| audio_path, | |
| video_path, | |
| thermal_path, | |
| chatbot, | |
| history, | |
| modality_cache | |
| ], show_progress=True) | |
| demo.launch(enable_queue=True) | |