app.py

from transformers import AutoModel, AutoTokenizer
from copy import deepcopy
import os
import ipdb
import gradio as gr
import mdtex2html
from model.openllama import OpenLLAMAPEFTModel
import torch
import json

# 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/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()
print(f'[!] init the 13b 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("<", "&lt;")
                    line = line.replace(">", "&gt;")
                    line = line.replace(" ", "&nbsp;")
                    line = line.replace("*", "&ast;")
                    line = line.replace("_", "&lowbar;")
                    line = line.replace("-", "&#45;")
                    line = line.replace(".", "&#46;")
                    line = line.replace("!", "&#33;")
                    line = line.replace("(", "&#40;")
                    line = line.replace(")", "&#41;")
                    line = line.replace("$", "&#36;")
                lines[i] = "<br>"+line
    text = "".join(lines)
    return text


def re_predict(
    input, 
    image_path, 
    audio_path, 
    video_path, 
    thermal_path, 
    chatbot, 
    max_length, 
    top_p, 
    temperature, 
    history, 
    modality_cache, 
):
    # drop the latest query and answers and generate again
    q, a = history.pop()
    chatbot.pop()
    return predict(q, image_path, audio_path, video_path, thermal_path, chatbot, max_length, top_p, temperature, history, modality_cache)


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, "图片和音频以及视频为空！请重新上传才能开启对话。")]
    else:
        print(f'[!] image path: {image_path}\n[!] audio path: {audio_path}\n[!] video path: {video_path}\n[!] thermal path: {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}'

    with torch.no_grad():
        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_dialog():
    return [], []

def reset_state():
    return None, None, None, None, [], [], []


with gr.Blocks(scale=4) as demo:
    gr.HTML("""<h1 align="center">PandaGPT</h1>

    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).
            """)

    with gr.Row(scale=4):
        with gr.Column(scale=1):
            image_path = gr.Image(type="filepath", label="Image", value=None)
        with gr.Column(scale=1):
            audio_path = gr.Audio(type="filepath", label="Audio", value=None)
        with gr.Column(scale=1):
            video_path = gr.Video(type='file', label="Video")
        with gr.Column(scale=1):
            thermal_path = gr.Image(type="filepath", label="Thermal Image", value=None)

    chatbot = gr.Chatbot().style(height=300)
    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):
                with gr.Row(scale=1):
                    submitBtn = gr.Button("Submit", variant="primary")
                with gr.Row(scale=1):
                    resubmitBtn = gr.Button("Resubmit", variant="primary")
        with gr.Column(scale=1):
            emptyBtn = gr.Button("Clear History")
            max_length = gr.Slider(0, 400, value=256, 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=1.0, 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
    )

    resubmitBtn.click(
        re_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)