如何利用ChatGPT和Python实现多模态对话功能

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "microsoft/DialoGPT-medium"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

from PIL import Image

def process_image(image_path):
    image = Image.open(image_path)
    # 将图像转换为模型所需的输入格式
    # 对于ChatGPT，一般是将图像编码为Base64格式的字符串
    image_base64 = image_to_base64(image)
    return image_base64

import librosa

def process_audio(audio_path):
    # 使用librosa库读取音频文件
    audio, sr = librosa.load(audio_path, sr=None)
    # 将音频文件转换为模型所需的输入格式
    return audio.tolist()

def chat(model, tokenizer, text_input, image_input, audio_input):
    # 将输入数据编码为模型所需的输入格式
    text_input_ids = tokenizer.encode(text_input, return_tensors="pt")
    image_input_base64 = process_image(image_input)
    audio_input = process_audio(audio_input)

    # 将输入数据与模型所需的输入格式拼接起来
    input_data = {
        "input_ids": text_input_ids,
        "image_input": image_input_base64,
        "audio_input": audio_input
    }

    # 使用模型进行多模态对话
    output = model.generate(**input_data, max_length=50)

    # 对模型生成的输出进行解码
    response = tokenizer.decode(output[0], skip_special_tokens=True)

    return response