| 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 |
|
|
| |
| 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}') |
| |
| 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 π€οΌ |
| |
| 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 π€''') |
|
|
| 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) |
|
|
