AudioGPT/audio-chatgpt.py

import sys
import os
sys.path.append(os.path.dirname(os.path.realpath(__file__)))
sys.path.append(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer, CLIPSegProcessor, CLIPSegForImageSegmentation
import torch
from diffusers import StableDiffusionPipeline
from diffusers import StableDiffusionInstructPix2PixPipeline, EulerAncestralDiscreteScheduler
import os
from langchain.agents.initialize import initialize_agent
from langchain.agents.tools import Tool
from langchain.chains.conversation.memory import ConversationBufferMemory
from langchain.llms.openai import OpenAI
import re
import uuid
import soundfile
from diffusers import StableDiffusionInpaintPipeline
from PIL import Image
import numpy as np
from omegaconf import OmegaConf
from transformers import pipeline, BlipProcessor, BlipForConditionalGeneration, BlipForQuestionAnswering
import cv2
import einops
from pytorch_lightning import seed_everything
import random
from ldm.util import instantiate_from_config
from ControlNet.cldm.model import create_model, load_state_dict
#from ControlNet.cldm.ddim_hacked import DDIMSampler
from ControlNet.annotator.canny import CannyDetector
from ControlNet.annotator.mlsd import MLSDdetector
from ControlNet.annotator.util import HWC3, resize_image
from ControlNet.annotator.hed import HEDdetector, nms
from ControlNet.annotator.openpose import OpenposeDetector
from ControlNet.annotator.uniformer import UniformerDetector
from ControlNet.annotator.midas import MidasDetector
from pathlib import Path
from vocoder.hifigan.modules import VocoderHifigan
from ldm.models.diffusion.ddim import DDIMSampler
from wav_evaluation.models.CLAPWrapper import CLAPWrapper


AUDIO_CHATGPT_PREFIX = """Audio ChatGPT


TOOLS:
------

Audio ChatGPT  has access to the following tools:"""

AUDIO_CHATGPT_FORMAT_INSTRUCTIONS = """To use a tool, please use the following format:

```
Thought: Do I need to use a tool? Yes
Action: the action to take, should be one of [{tool_names}]
Action Input: the input to the action
Observation: the result of the action
```

When you have a response to say to the Human, or if you do not need to use a tool, you MUST use the format:

```
Thought: Do I need to use a tool? No
{ai_prefix}: [your response here]
```
"""

AUDIO_CHATGPT_SUFFIX = """You are very strict to the filename correctness and will never fake a file name if not exists.
You will remember to provide the image file name loyally if it's provided in the last tool observation.

Begin!

Previous conversation history:
{chat_history}
New input: {input}
Thought: Do I need to use a tool? {agent_scratchpad}"""

SAMPLE_RATE = 16000
temp_audio_filename = "audio/2f67ff3a.wav"

def cut_dialogue_history(history_memory, keep_last_n_words = 500):
    tokens = history_memory.split()
    n_tokens = len(tokens)
    print(f"hitory_memory:{history_memory}, n_tokens: {n_tokens}")
    if n_tokens < keep_last_n_words:
        return history_memory
    else:
        paragraphs = history_memory.split('\n')
        last_n_tokens = n_tokens
        while last_n_tokens >= keep_last_n_words:
            last_n_tokens = last_n_tokens - len(paragraphs[0].split(' '))
            paragraphs = paragraphs[1:]
        return '\n' + '\n'.join(paragraphs)

def get_new_image_name(org_img_name, func_name="update"):
    head_tail = os.path.split(org_img_name)
    head = head_tail[0]
    tail = head_tail[1]
    name_split = tail.split('.')[0].split('_')
    this_new_uuid = str(uuid.uuid4())[0:4]
    if len(name_split) == 1:
        most_org_file_name = name_split[0]
        recent_prev_file_name = name_split[0]
        new_file_name = '{}_{}_{}_{}.png'.format(this_new_uuid, func_name, recent_prev_file_name, most_org_file_name)
    else:
        assert len(name_split) == 4
        most_org_file_name = name_split[3]
        recent_prev_file_name = name_split[0]
        new_file_name = '{}_{}_{}_{}.png'.format(this_new_uuid, func_name, recent_prev_file_name, most_org_file_name)
    return os.path.join(head, new_file_name)

def create_model(config_path, device):
    config = OmegaConf.load(config_path)
    OmegaConf.update(config, "model.params.cond_stage_config.params.device", device)
    model = instantiate_from_config(config.model).cpu()
    print(f'Loaded model config from [{config_path}]')
    return model

def initialize_model(config, ckpt, device):
    config = OmegaConf.load(config)
    model = instantiate_from_config(config.model)
    model.load_state_dict(torch.load(ckpt,map_location='cpu')["state_dict"], strict=False)

    model = model.to(device)
    model.cond_stage_model.to(model.device)
    model.cond_stage_model.device = model.device
    #print(model.device,device,model.cond_stage_model.device)
    sampler = DDIMSampler(model)

    return sampler

clap_model = CLAPWrapper('useful_ckpts/CLAP/CLAP_weights_2022.pth','useful_ckpts/CLAP/config.yml',use_cuda=torch.cuda.is_available())

def select_best_audio(prompt,wav_list):
    text_embeddings = clap_model.get_text_embeddings([prompt])
    score_list = []
    for data in wav_list:
        sr,wav = data
        audio_embeddings = clap_model.get_audio_embeddings([(torch.FloatTensor(wav),sr)], resample=True)
        score = clap_model.compute_similarity(audio_embeddings, text_embeddings,use_logit_scale=False).squeeze().cpu().numpy()
        score_list.append(score)
    max_index = np.array(score_list).argmax()
    print(score_list,max_index)
    return wav_list[max_index]

class MaskFormer:
    def __init__(self, device):
        self.device = device
        self.processor = CLIPSegProcessor.from_pretrained("CIDAS/clipseg-rd64-refined")
        self.model = CLIPSegForImageSegmentation.from_pretrained("CIDAS/clipseg-rd64-refined").to(device)

    def inference(self, image_path, text):
        threshold = 0.5
        min_area = 0.02
        padding = 20
        original_image = Image.open(image_path)
        image = original_image.resize((512, 512))
        inputs = self.processor(text=text, images=image, padding="max_length", return_tensors="pt",).to(self.device)
        with torch.no_grad():
            outputs = self.model(**inputs)
        mask = torch.sigmoid(outputs[0]).squeeze().cpu().numpy() > threshold
        area_ratio = len(np.argwhere(mask)) / (mask.shape[0] * mask.shape[1])
        if area_ratio < min_area:
            return None
        true_indices = np.argwhere(mask)
        mask_array = np.zeros_like(mask, dtype=bool)
        for idx in true_indices:
            padded_slice = tuple(slice(max(0, i - padding), i + padding + 1) for i in idx)
            mask_array[padded_slice] = True
        visual_mask = (mask_array * 255).astype(np.uint8)
        image_mask = Image.fromarray(visual_mask)
        return image_mask.resize(image.size)

class ImageEditing:
    def __init__(self, device):
        print("Initializing StableDiffusionInpaint to %s" % device)
        self.device = device
        self.mask_former = MaskFormer(device=self.device)
        self.inpainting = StableDiffusionInpaintPipeline.from_pretrained(        "runwayml/stable-diffusion-inpainting",).to(device)

    def remove_part_of_image(self, input):
        image_path, to_be_removed_txt = input.split(",")
        print(f'remove_part_of_image: to_be_removed {to_be_removed_txt}')
        return self.replace_part_of_image(f"{image_path},{to_be_removed_txt},background")

    def replace_part_of_image(self, input):
        image_path, to_be_replaced_txt, replace_with_txt = input.split(",")
        print(f'replace_part_of_image: replace_with_txt {replace_with_txt}')
        original_image = Image.open(image_path)
        mask_image = self.mask_former.inference(image_path, to_be_replaced_txt)
        updated_image = self.inpainting(prompt=replace_with_txt, image=original_image, mask_image=mask_image).images[0]
        updated_image_path = get_new_image_name(image_path, func_name="replace-something")
        updated_image.save(updated_image_path)
        return updated_image_path

class Pix2Pix:
    def __init__(self, device):
        print("Initializing Pix2Pix to %s" % device)
        self.device = device
        self.pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained("timbrooks/instruct-pix2pix", torch_dtype=torch.float16, safety_checker=None).to(device)
        self.pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(self.pipe.scheduler.config)

    def inference(self, inputs):
        """Change style of image."""
        print("===>Starting Pix2Pix Inference")
        image_path, instruct_text = inputs.split(",")[0], ','.join(inputs.split(',')[1:])
        original_image = Image.open(image_path)
        image = self.pipe(instruct_text,image=original_image,num_inference_steps=40,image_guidance_scale=1.2,).images[0]
        updated_image_path = get_new_image_name(image_path, func_name="pix2pix")
        image.save(updated_image_path)
        return updated_image_path

class T2I:
    def __init__(self, device):
        print("Initializing T2I to %s" % device)
        self.device = device
        self.pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
        self.text_refine_tokenizer = AutoTokenizer.from_pretrained("Gustavosta/MagicPrompt-Stable-Diffusion")
        self.text_refine_model = AutoModelForCausalLM.from_pretrained("Gustavosta/MagicPrompt-Stable-Diffusion")
        self.text_refine_gpt2_pipe = pipeline("text-generation", model=self.text_refine_model, tokenizer=self.text_refine_tokenizer, device=self.device)
        self.pipe.to(device)

    def inference(self, text):
        image_filename = os.path.join('image', str(uuid.uuid4())[0:8] + ".png")
        refined_text = self.text_refine_gpt2_pipe(text)[0]["generated_text"]
        print(f'{text} refined to {refined_text}')
        image = self.pipe(refined_text).images[0]
        image.save(image_filename)
        print(f"Processed T2I.run, text: {text}, image_filename: {image_filename}")
        return image_filename

class ImageCaptioning:
    def __init__(self, device):
        print("Initializing ImageCaptioning to %s" % device)
        self.device = device
        self.processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
        self.model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base").to(self.device)

    def inference(self, image_path):
        inputs = self.processor(Image.open(image_path), return_tensors="pt").to(self.device)
        out = self.model.generate(**inputs)
        captions = self.processor.decode(out[0], skip_special_tokens=True)
        return captions

class BLIPVQA:
    def __init__(self, device):
        print("Initializing BLIP VQA to %s" % device)
        self.device = device
        self.processor = BlipProcessor.from_pretrained("Salesforce/blip-vqa-base")
        self.model = BlipForQuestionAnswering.from_pretrained("Salesforce/blip-vqa-base").to(self.device)

    def get_answer_from_question_and_image(self, inputs):
        image_path, question = inputs.split(",")
        raw_image = Image.open(image_path).convert('RGB')
        print(F'BLIPVQA :question :{question}')
        inputs = self.processor(raw_image, question, return_tensors="pt").to(self.device)
        out = self.model.generate(**inputs)
        answer = self.processor.decode(out[0], skip_special_tokens=True)
        return answer

class T2A:
    def __init__(self, device):
        print("Initializing Make-An-Audio to %s" % device)
        self.device = device
        self.sampler = initialize_model('configs/text-to-audio/txt2audio_args.yaml', 'useful_ckpts/ta40multi_epoch=000085.ckpt', device=device) 
        self.vocoder = VocoderHifigan('vocoder/logs/hifi_0127',device=device)
    def txt2audio(self, text, seed = 55, scale = 1.5, ddim_steps = 100, n_samples = 3, W = 624, H = 80):
        prng = np.random.RandomState(seed)
        start_code = prng.randn(n_samples, self.sampler.model.first_stage_model.embed_dim, H // 8, W // 8)
        start_code = torch.from_numpy(start_code).to(device=self.device, dtype=torch.float32)
        uc = self.sampler.model.get_learned_conditioning(n_samples * [""])
        c = self.sampler.model.get_learned_conditioning(n_samples * [text])
        shape = [self.sampler.model.first_stage_model.embed_dim, H//8, W//8]  # (z_dim, 80//2^x, 848//2^x)
        samples_ddim, _ = self.sampler.sample(S = ddim_steps,
                                            conditioning = c,
                                            batch_size = n_samples,
                                            shape = shape,
                                            verbose = False,
                                            unconditional_guidance_scale = scale,
                                            unconditional_conditioning = uc,
                                            x_T = start_code)

        x_samples_ddim = self.sampler.model.decode_first_stage(samples_ddim)
        x_samples_ddim = torch.clamp((x_samples_ddim+1.0)/2.0, min=0.0, max=1.0) # [0, 1]

        wav_list = []
        for idx,spec in enumerate(x_samples_ddim):
            wav = self.vocoder.vocode(spec)
            wav_list.append((SAMPLE_RATE,wav))
        best_wav = select_best_audio(text, wav_list)
        # audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav")
        # temp_audio_filename = audio_filename
        # soundfile.write(audio_filename, best_wav[1], samplerate = 16000)
        # print(f"Processed T2I.run, text: {text}, audio_filename: {audio_filename}")
        return best_wav
    def inference(self, text, seed = 55, scale = 1.5, ddim_steps = 100, n_samples = 3, W = 624, H = 80):
        global temp_audio_filename
        melbins,mel_len = 80,624
        with torch.no_grad():
            result = self.txt2audio(
                text = text,
                H = melbins,
                W = mel_len
            )
        audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav")
        temp_audio_filename = audio_filename
        soundfile.write(audio_filename, result[1], samplerate = 16000)
        print(f"Processed T2I.run, text: {text}, audio_filename: {audio_filename}")
        return audio_filename






class ConversationBot:
    def __init__(self):
        print("Initializing AudioChatGPT")
        self.llm = OpenAI(temperature=0)
        #self.edit = ImageEditing(device="cuda:0")
        self.i2t = ImageCaptioning(device="cuda:0")
        self.t2i = T2I(device="cuda:0")
        #self.BLIPVQA = BLIPVQA(device="cuda:2")
        #self.pix2pix = Pix2Pix(device="cuda:2")
        self.t2a = T2A(device="cuda:0")
        self.memory = ConversationBufferMemory(memory_key="chat_history", output_key='output')
        self.tools = [
            Tool(name="Get Photo Description", func=self.i2t.inference,
                 description="useful for when you want to know what is inside the photo. receives image_path as input. "
                             "The input to this tool should be a string, representing the image_path. "),
            Tool(name="Generate Image From User Input Text", func=self.t2i.inference,
                 description="useful for when you want to generate an image from a user input text and it saved it to a file. like: generate an image of an object or something, or generate an image that includes some objects. "
                             "The input to this tool should be a string, representing the text used to generate image. "),
            # Tool(name="Remove Something From The Photo", func=self.edit.remove_part_of_image,
            #      description="useful for when you want to remove and object or something from the photo from its description or location. "
            #                  "The input to this tool should be a comma seperated string of two, representing the image_path and the object need to be removed. "),
            # Tool(name="Replace Something From The Photo", func=self.edit.replace_part_of_image,
            #      description="useful for when you want to replace an object from the object description or location with another object from its description. "
            #                  "The input to this tool should be a comma seperated string of three, representing the image_path, the object to be replaced, the object to be replaced with "),
            Tool(name="Generate Audio From User Input Text", func=self.t2a.inference,
                 description="useful for when you want to generate an audio from a user input text and it saved it to a file."
                             "The input to this tool should be a string, representing the text used to generate audio.")]
            # Tool(name="Instruct Image Using Text", func=self.pix2pix.inference,
            #      description="useful for when you want to the style of the image to be like the text. like: make it look like a painting. or make it like a robot. "
            #                  "The input to this tool should be a comma seperated string of two, representing the image_path and the text. "),
            # Tool(name="Answer Question About The Image", func=self.BLIPVQA.get_answer_from_question_and_image,
            #      description="useful for when you need an answer for a question based on an image. like: what is the background color of the last image, how many cats in this figure, what is in this figure. "
            #                  "The input to this tool should be a comma seperated string of two, representing the image_path and the question")]
        self.agent = initialize_agent(
            self.tools,
            self.llm,
            agent="conversational-react-description",
            verbose=True,
            memory=self.memory,
            return_intermediate_steps=True,
            agent_kwargs={'prefix': AUDIO_CHATGPT_PREFIX, 'format_instructions': AUDIO_CHATGPT_FORMAT_INSTRUCTIONS, 'suffix': AUDIO_CHATGPT_SUFFIX}, )

    def run_text(self, text, state):
        print("===============Running run_text =============")
        print("Inputs:", text, state)
        print("======>Previous memory:\n %s" % self.agent.memory)
        self.agent.memory.buffer = cut_dialogue_history(self.agent.memory.buffer, keep_last_n_words=500)
        res = self.agent({"input": text})
        print("======>Current memory:\n %s" % self.agent.memory)
        response = re.sub('(image/\S*png)', lambda m: f'![](/file={m.group(0)})*{m.group(0)}*', res['output'])
        state = state + [(text, response)]
        print("Outputs:", state)
        return state, state, temp_audio_filename
        #return outaudio

    def run_image(self, image, state, txt):
        print("===============Running run_image =============")
        print("Inputs:", image, state)
        print("======>Previous memory:\n %s" % self.agent.memory)
        image_filename = os.path.join('image', str(uuid.uuid4())[0:8] + ".png")
        print("======>Auto Resize Image...")
        img = Image.open(image.name)
        width, height = img.size
        ratio = min(512 / width, 512 / height)
        width_new, height_new = (round(width * ratio), round(height * ratio))
        img = img.resize((width_new, height_new))
        img = img.convert('RGB')
        img.save(image_filename, "PNG")
        print(f"Resize image form {width}x{height} to {width_new}x{height_new}")
        description = self.i2t.inference(image_filename)
        Human_prompt = "\nHuman: provide a figure named {}. The description is: {}. This information helps you to understand this image, but you should use tools to finish following tasks, " \
                       "rather than directly imagine from my description. If you understand, say \"Received\". \n".format(image_filename, description)
        AI_prompt = "Received.  "
        self.agent.memory.buffer = self.agent.memory.buffer + Human_prompt + 'AI: ' + AI_prompt
        print("======>Current memory:\n %s" % self.agent.memory)
        state = state + [(f"![](/file={image_filename})*{image_filename}*", AI_prompt)]
        print("Outputs:", state)
        return state, state, txt + ' ' + image_filename + ' '
    
    # def run_audio(self, text):
    #     # print(temp_audio_filename)
    #     # wav, sr = soundfile.read(temp_audio_filename)
    #     # return wav
    #     return temp_audio_filename


if __name__ == '__main__':
    bot = ConversationBot()
    with gr.Blocks(css="#chatbot .overflow-y-auto{height:500px}") as demo:
        with gr.Row():
            gr.Markdown("## Audio ChatGPT")
        chatbot = gr.Chatbot(elem_id="chatbot", label="Audio ChatGPT")
        state = gr.State([])
        with gr.Row():
            with gr.Column(scale=0.7):
                txt = gr.Textbox(show_label=False, placeholder="Enter text and press enter, or upload an image").style(container=False)
            with gr.Column(scale=0.15, min_width=0):
                clear = gr.Button("Clear️")
            with gr.Column(scale=0.15, min_width=0):
                btn = gr.UploadButton("Upload", file_types=["image"])
        with gr.Column():
            outaudio = gr.Audio()
       # txt.submit(bot.run_text, [txt, state], [chatbot, state])
        txt.submit(bot.run_text, [txt, state], [chatbot, state, outaudio])
       # txt.submit(bot.run_audio, inputs = [txt], outputs = [outaudio])
        txt.submit(lambda: "", None, txt)
        btn.upload(bot.run_image, [btn, state, txt], [chatbot, state, txt])
        clear.click(bot.memory.clear)
        clear.click(lambda: [], None, chatbot)
        clear.click(lambda: [], None, state)
        demo.launch(server_name="0.0.0.0", server_port=7861, share=True)