Update app.py

app.py

@@ -1,3 +1,92 @@
+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
+)
 
-gr.load("models/zeroMN/SHMT").launch()
+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')
+            print("输入数据:", inputs)
+            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
+            )
+            print("生成的输出:", outputs)
+            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.inputs.Dropdown(choices=["text_generation", "code_generation"], label="任务类型"), gr.inputs.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()