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import os
import torch
import torch.nn as nn
import numpy as np
import random
import gradio as gr
from transformers import (
BartForConditionalGeneration,
AutoModelForCausalLM,
BertModel,
Wav2Vec2Model,
CLIPModel,
AutoTokenizer
)
class MultiModalModel(nn.Module):
def __init__(self):
super(MultiModalModel, self).__init__()
# 初始化子模型
self.text_generator = BartForConditionalGeneration.from_pretrained('facebook/bart-base')
self.code_generator = AutoModelForCausalLM.from_pretrained('gpt2')
self.nlp_encoder = BertModel.from_pretrained('bert-base-uncased')
self.speech_encoder = Wav2Vec2Model.from_pretrained('facebook/wav2vec2-base-960h')
self.vision_encoder = CLIPModel.from_pretrained('openai/clip-vit-base-patch32')
# 初始化分词器和处理器
self.text_tokenizer = AutoTokenizer.from_pretrained('facebook/bart-base')
self.code_tokenizer = AutoTokenizer.from_pretrained('gpt2')
self.nlp_tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
self.speech_processor = AutoTokenizer.from_pretrained('facebook/wav2vec2-base-960h')
self.vision_processor = AutoTokenizer.from_pretrained('openai/clip-vit-base-patch32')
def forward(self, task, inputs):
if task == 'text_generation':
attention_mask = inputs.get('attention_mask')
outputs = self.text_generator.generate(
inputs['input_ids'],
max_new_tokens=100,
pad_token_id=self.text_tokenizer.eos_token_id,
attention_mask=attention_mask,
top_p=0.9,
top_k=50,
temperature=0.8,
do_sample=True
)
return self.text_tokenizer.decode(outputs[0], skip_special_tokens=True)
elif task == 'code_generation':
attention_mask = inputs.get('attention_mask')
outputs = self.code_generator.generate(
inputs['input_ids'],
max_new_tokens=50,
pad_token_id=self.code_tokenizer.eos_token_id,
attention_mask=attention_mask,
top_p=0.95,
top_k=50,
temperature=1.2,
do_sample=True
)
return self.code_tokenizer.decode(outputs[0], skip_special_tokens=True)
# 添加其他任务的逻辑...
# 定义 Gradio 接口的推理函数
def gradio_inference(task, input_text):
if task == "text_generation":
tokenizer = model.text_tokenizer
elif task == "code_generation":
tokenizer = model.code_tokenizer
# 根据任务选择合适的分词器
inputs = tokenizer(input_text, return_tensors='pt')
inputs['attention_mask'] = torch.ones_like(inputs['input_ids'])
with torch.no_grad():
result = model(task, inputs)
return result
# 初始化模型
model = MultiModalModel()
# 创建 Gradio 接口
interface = gr.Interface(
fn=gradio_inference,
inputs=[gr.Dropdown(choices=["text_generation", "code_generation"], label="任务类型"), gr.Textbox(lines=2, placeholder="输入文本...")],
outputs="text",
title="多模态模型推理",
description="选择任务类型并输入文本以进行推理"
)
# 启动 Gradio 应用
interface.launch()
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