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How to use ChatGPT and Python to implement multi-modal dialogue function
Overview:
With the development of artificial intelligence technology, multi-modal dialogue has gradually become a research topic and application hot spots. Multimodal conversations include not only text conversations, but also communication through various media forms such as images, audio, and video. This article will introduce how to use ChatGPT and Python to implement multi-modal dialogue functions, and provide corresponding code examples.
from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "microsoft/DialoGPT-medium" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name)
First, let’s take a look at how to process images. Suppose we want to pass in a picture as the input of the conversation, we can use the following code to convert the image into the input format required by the pre-trained model:
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
For audio processing, we can use the librosa library to convert the audio file Convert to the input format required by the model. The following is a sample code:
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
In the above code, we first encode the text input into the model along with the image input and audio input The required input format, and then calls the model's generate
method to generate the model's output. Finally, we decode the output and return the dialogue system's answer.
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