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()