Update app.py

app.py

@@ -1,154 +1,124 @@
 import gradio as gr
-import numpy as np
-import random
-
-# import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
 import torch
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
-
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
-
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
-
-# @spaces.GPU #[uncomment to use ZeroGPU]
-def infer(
-    prompt,
-    negative_prompt,
-    seed,
-    randomize_seed,
-    width,
-    height,
-    guidance_scale,
-    num_inference_steps,
-    progress=gr.Progress(track_tqdm=True),
-):
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-
-    generator = torch.Generator().manual_seed(seed)
-
-    image = pipe(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        width=width,
-        height=height,
-        generator=generator,
-    ).images[0]
-
-    return image, seed
-
-
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
-]
-
-css = """
-#col-container {
-    margin: 0 auto;
-    max-width: 640px;
-}
-"""
-
-with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
-
+from transformers import BertTokenizer
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+names = ['负向', '正向']
+
+# 分词器
+tokenizer = BertTokenizer.from_pretrained("bert-base-chinese")
+
+# 加载预训练模型
+bert_model = BertModel.from_pretrained("bert-base-chinese").to(device)
+model = Model(bert_model).to(device)
+model.load_state_dict(torch.load("/params/1bert.pt"))
+# 切换到eval模式
+model.eval()
+
+def collate_fn(data):
+    sentes = []
+    sentes.append(data)
+
+    #编码
+    data = tokenizer.batch_encode_plus(
+        batch_text_or_text_pairs=sentes,
+        truncation=True,
+        padding="max_length",
+        max_length=350,
+        return_tensors="pt",
+        return_length=True
+    )
+    input_ids = data["input_ids"]
+    attention_mask = data["attention_mask"]
+    token_type_ids = data["token_type_ids"]
+    return input_ids, attention_mask, token_type_ids
+
+
+def analyze_sentiment(text):
+    input_ids, attention_mask, token_type_ids = collate_fn(text)
+    input_ids = input_ids.to(device)
+    attention_mask = attention_mask.to(device)
+    token_type_ids = token_type_ids.to(device)
+    # 上游不参与训练
+    with torch.no_grad():
+        out = model(input_ids, attention_mask, token_type_ids)
+    # 找到每个样本在指定维度上的最大值的索引
+    out = out.argmax(dim=1)
+    return f"{names[out]}评价", names[out]
+
+def create_interface():
+    """
+    创建Gradio界面
+    """
+    with gr.Blocks(title="情感分析应用", theme=gr.themes.Soft()) as demo:
+        gr.Markdown("# 🎭 情感分析应用")
+        gr.Markdown("输入文本，AI将分析其情感倾向")
+        
         with gr.Row():
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
-
-            run_button = gr.Button("Run", scale=0, variant="primary")
-
-        result = gr.Image(label="Result", show_label=False)
-
-        with gr.Accordion("Advanced Settings", open=False):
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
-            )
-
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-            )
-
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-
-            with gr.Row():
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
+            with gr.Column(scale=2):
+                # 输入区域
+                text_input = gr.Textbox(
+                    label="输入要分析的文本",
+                    placeholder="请输入您想要分析情感的文本...",
+                    lines=4,
+                    max_lines=10
                 )
-
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
+                
+                # 按钮
+                with gr.Row():
+                    analyze_btn = gr.Button("🔍 分析情感", variant="primary")
+                    clear_btn = gr.Button("🗑️ 清空", variant="secondary")
+            
+            with gr.Column(scale=2):
+                # 输出区域
+                result_summary = gr.Textbox(
+                    label="分析结果",
+                    lines=3,
+                    interactive=False
                 )
-
-            with gr.Row():
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,  # Replace with defaults that work for your model
+                
+                # 情感标签显示
+                sentiment_label = gr.Label(
+                    label="情感分类",
                 )
-
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=2,  # Replace with defaults that work for your model
-                )
-
-        gr.Examples(examples=examples, inputs=[prompt])
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn=infer,
-        inputs=[
-            prompt,
-            negative_prompt,
-            seed,
-            randomize_seed,
-            width,
-            height,
-            guidance_scale,
-            num_inference_steps,
-        ],
-        outputs=[result, seed],
-    )
+        
+        # 示例文本
+        gr.Markdown("### 📝 示例文本")
+        examples = gr.Examples(
+            examples=[
+                ["这个产品真的很棒，我非常满意！"],
+                ["服务态度太差了，完全不推荐"],
+                ["还可以吧，没什么特别的感觉"],
+                ["质量很好，物流也很快，五星好评！"],
+                ["价格太贵了，性价比不高"]
+            ],
+            inputs=text_input,
+            outputs=[result_summary, sentiment_label],
+            fn=analyze_sentiment,
+            cache_examples=False
+        )
+        
+        # 绑定事件
+        analyze_btn.click(
+            fn=analyze_sentiment,
+            inputs=text_input,
+            outputs=[result_summary, sentiment_label]
+        )
+        
+        clear_btn.click(
+            fn=lambda: ("", "", ""),
+            outputs=[text_input, result_summary, sentiment_label]
+        )
+        
+        # 回车键触发分析
+        text_input.submit(
+            fn=analyze_sentiment,
+            inputs=text_input,
+            outputs=[result_summary, sentiment_label]
+        )
+    
+    return demo
 
 if __name__ == "__main__":
-    demo.launch()
+    demo = create_interface()
+    demo.launch(debug=True)