initial commit

README.md

@@ -11,4 +11,78 @@ license: apache-2.0
 short_description: NER for Diner Restaurant Reservation with Gemma 3 270m it
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+# 🍽️ 餐廳訂位資訊提取器
+
+一個基於AI的訂位資訊提取工具，專門用於從中文訊息中自動提取餐廳訂位資訊。
+
+## 功能特色
+
+- 🔍 自動從中文訊息提取訂位資訊
+- 📋 輸出結構化的JSON格式
+- 🎯 高準確率的資訊提取
+- 💻 完全基於CPU運行，無需GPU
+- 🎨 現代化的 Gradio v5 界面
+
+## 提取資訊
+
+- **👥 人數** (num_people)
+- **📅 預訂日期/時間** (reservation_date)  
+- **📞 電話號碼** (phone_num)
+
+## 使用示例
+
+輸入: `"你好，我想訂明天晚上7點的位子，四位成人，電話是0912-345-678"`
+
+輸出:
+```json
+{
+  "num_people": "4",
+  "reservation_date": "明天晚上7點",
+  "phone_num": "0912-345-678"
+}
+```
+
+## 技術細節
+
+- **模型**: `Luigi/gemma-3-270m-it-dinercall-ner` (基於 Gemma-3-270M 微調)
+- **框架**: Gradio v5 + Transformers
+- **部署**: Hugging Face Spaces (CPU)
+
+## 本地運行
+
+```bash
+pip install -r requirements.txt
+python src/app.py
+```
+
+## 作者
+
+由 [Together AI](https://together.ai) 提供技術支持
+```
+
+## Key Features of This Gradio v5 Solution:
+
+1. **Modern UI**: Uses Gradio v5's enhanced styling capabilities
+2. **Interactive Examples**: Clickable examples that automatically populate the input field
+3. **Dual Output Display**: Shows both structured JSON and raw model output
+4. **Statistics Panel**: Displays extracted information in an easy-to-read format
+5. **Custom CSS**: Enhanced styling with gradients and hover effects
+6. **Responsive Design**: Works well on both desktop and mobile devices
+7. **JavaScript Integration**: For smoother interaction with example clicks
+
+## Deployment Instructions:
+
+1. **Create a new Space** on Hugging Face:
+   - Go to https://huggingface.co/spaces
+   - Click "Create new Space"
+   - Select "Gradio" as SDK
+   - Name: `dinercall-ner-demo`
+   - Visibility: Public
+
+2. **Upload all files** to your Space
+
+3. **The Space will automatically build** and be available at:
+   `https://huggingface.co/spaces/your-username/dinercall-ner-demo`
+
+This Gradio v5 solution provides a modern, feature-rich interface for your NER model that will work reliably on Hugging Face Spaces without permission issues. The interface is user-friendly and provides all the functionality you need for testing your model.

app.py

@@ -0,0 +1,271 @@
+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import json
+import re
+import time
+from typing import Dict, Any
+
+# System prompt (must match training)
+SYSTEM_PROMPT = """你是一個助理，負責從用戶消息中提取預訂資訊並以JSON格式輸出。
+JSON必須包含三個字段: num_people, reservation_date, phone_num。
+如果某個字段沒有信息，使用空字符串。只輸出JSON，不要添加任何其他文字。"""
+
+# Load model and tokenizer with caching
+@gr.cache_resource()
+def load_model():
+    """Load the model and tokenizer with caching"""
+    try:
+        print("Loading model...")
+        model_name = "Luigi/gemma-3-270m-it-dinercall-ner"
+        
+        tokenizer = AutoTokenizer.from_pretrained(model_name)
+        model = AutoModelForCausalLM.from_pretrained(
+            model_name,
+            torch_dtype=torch.float32,
+            device_map="auto",
+            trust_remote_code=True
+        )
+        
+        # Set padding token if not set
+        if tokenizer.pad_token is None:
+            tokenizer.pad_token = tokenizer.eos_token
+            
+        print("Model loaded successfully!")
+        return model, tokenizer
+    except Exception as e:
+        print(f"Error loading model: {e}")
+        return None, None
+
+# Initialize model
+model, tokenizer = load_model()
+
+def validate_json(output: str) -> tuple:
+    """Validate and extract JSON from model output"""
+    try:
+        json_match = re.search(r'\{[\s\S]*\}', output)
+        if not json_match:
+            return False, None, "未找到JSON"
+        
+        json_str = json_match.group(0)
+        json_str = re.sub(r',\s*\}', '}', json_str)
+        parsed = json.loads(json_str)
+        return True, parsed, "有效的JSON"
+    except json.JSONDecodeError:
+        return False, None, "無效的JSON格式"
+    except Exception:
+        return False, None, "解析JSON時出錯"
+
+def extract_reservation_info(text: str):
+    """Extract reservation information from text"""
+    if model is None or tokenizer is None:
+        return {"error": "模型未加載成功，請刷新頁面重試"}, ""
+    
+    try:
+        # Create chat template
+        messages = [
+            {"role": "system", "content": SYSTEM_PROMPT},
+            {"role": "user", "content": text}
+        ]
+        
+        prompt = tokenizer.apply_chat_template(
+            messages, 
+            tokenize=False, 
+            add_generation_prompt=True
+        )
+        
+        # Tokenize input
+        inputs = tokenizer(prompt, return_tensors="pt", padding=True)
+        
+        # Generate response
+        with torch.no_grad():
+            outputs = model.generate(
+                **inputs,
+                max_new_tokens=64,
+                temperature=0.1,
+                pad_token_id=tokenizer.eos_token_id,
+                eos_token_id=tokenizer.eos_token_id,
+                do_sample=False,
+            )
+        
+        # Extract assistant's response
+        prompt_length = len(inputs.input_ids[0])
+        assistant_output = tokenizer.decode(outputs[0][prompt_length:], skip_special_tokens=True)
+        
+        # Validate and parse JSON
+        is_valid, parsed, message = validate_json(assistant_output)
+        
+        if is_valid:
+            return parsed, assistant_output
+        else:
+            return {"error": message}, assistant_output
+            
+    except Exception as e:
+        return {"error": f"處理時出錯: {str(e)}"}, ""
+
+# Create Gradio v5 interface
+def create_interface():
+    """Create the Gradio v5 interface"""
+    examples = [
+        "你好，我想訂明天晚上7點的位子，四位成人，電話是0912-345-678",
+        "週六下午三點，兩位，電話0987654321",
+        "預約下週三中午12點半，5人用餐，聯絡電話0912345678",
+        "我要訂位，3個人，今天下午6點"
+    ]
+    
+    # Define custom CSS for better styling
+    custom_css = """
+    .gradio-container {
+        max-width: 1000px !important;
+    }
+    .container {
+        max-width: 1000px;
+        margin: 0 auto;
+    }
+    .header {
+        text-align: center;
+        padding: 20px;
+        background: linear-gradient(135deg, #FF4B4B 0%, #FF8E53 100%);
+        border-radius: 10px;
+        margin-bottom: 20px;
+        color: white;
+    }
+    .example-box {
+        background-color: #f5f5f5;
+        padding: 15px;
+        border-radius: 10px;
+        margin-bottom: 15px;
+        cursor: pointer;
+    }
+    .example-box:hover {
+        background-color: #e8e8e8;
+    }
+    """
+    
+    with gr.Blocks(
+        title="🍽️ 餐廳訂位資訊提取器",
+        theme=gr.themes.Soft(),
+        css=custom_css
+    ) as demo:
+        # Header
+        gr.HTML("""
+        <div class="header">
+            <h1>🍽️ 餐廳訂位資訊提取器</h1>
+            <p>使用AI從中文訊息中自動提取訂位資訊</p>
+        </div>
+        """)
+        
+        with gr.Row():
+            with gr.Column(scale=2):
+                # Input section
+                input_text = gr.Textbox(
+                    label="輸入訂位訊息",
+                    placeholder="例如: 你好，我想訂明天晚上7點的位子，四位成人，電話是0912-345-678",
+                    lines=3,
+                    max_lines=5
+                )
+                
+                # Examples section
+                gr.Markdown("### 💡 示例訊息")
+                for i, example in enumerate(examples):
+                    gr.HTML(f"""
+                    <div class="example-box" onclick="document.getElementById('input-text').value = '{example}'; document.getElementById('input-text').dispatchEvent(new Event('input'));">
+                        {example}
+                    </div>
+                    """)
+                
+                submit_btn = gr.Button("提取資訊", variant="primary", size="lg")
+                
+            with gr.Column(scale=3):
+                # Output section
+                gr.Markdown("### 📋 提取結果")
+                
+                with gr.Tab("結構化結果"):
+                    json_output = gr.JSON(label="提取結果")
+                
+                with gr.Tab("原始輸出"):
+                    raw_output = gr.Code(
+                        label="模型原始輸出",
+                        language="json",
+                        interactive=False
+                    )
+                
+                # Stats section
+                with gr.Row():
+                    with gr.Column():
+                        people_count = gr.Number(label="👥 人數", interactive=False)
+                    with gr.Column():
+                        date_info = gr.Textbox(label="📅 預訂時間", interactive=False)
+                    with gr.Column():
+                        phone_info = gr.Textbox(label="📞 電話", interactive=False)
+        
+        # Info panel
+        with gr.Accordion("ℹ️ 使用說明", open=False):
+            gr.Markdown("""
+            **支援提取的資訊:**
+            - 👥 人數 (num_people)
+            - 📅 預訂日期/時間 (reservation_date)
+            - 📞 電話號碼 (phone_num)
+            
+            **注意事項:**
+            - 首次加載模型可能需要幾分鐘時間
+            - 如果遇到錯誤，請嘗試刷新頁面
+            - 模型會輸出JSON格式的結果
+            """)
+        
+        # Footer
+        gr.Markdown("---")
+        gr.HTML("""
+        <div style="text-align: center; color: #666;">
+            <p>由 <a href="https://together.ai" target="_blank">Together AI</a> 提供技術支持 | 模型: Luigi/gemma-3-270m-it-dinercall-ner</p>
+        </div>
+        """)
+        
+        # Hidden element for JavaScript interaction
+        gr.HTML("""
+        <input type="hidden" id="input-text">
+        <script>
+            document.addEventListener('DOMContentLoaded', function() {
+                const inputElement = document.querySelector('[aria-label="輸入訂位訊息"] textarea');
+                const hiddenInput = document.getElementById('input-text');
+                
+                hiddenInput.addEventListener('input', function() {
+                    inputElement.value = hiddenInput.value;
+                    inputElement.dispatchEvent(new Event('input', { bubbles: true }));
+                });
+            });
+        </script>
+        """)
+        
+        # Function to update stats
+        def update_stats(result):
+            """Update the statistics fields based on the result"""
+            if isinstance(result, dict) and "error" not in result:
+                return (
+                    result.get("num_people", "未提供"),
+                    result.get("reservation_date", "未提供"),
+                    result.get("phone_num", "未提供")
+                )
+            return ("", "", "")
+        
+        # Connect the function to the button
+        submit_btn.click(
+            fn=extract_reservation_info,
+            inputs=input_text,
+            outputs=[json_output, raw_output]
+        ).then(
+            fn=update_stats,
+            inputs=json_output,
+            outputs=[people_count, date_info, phone_info]
+        )
+    
+    return demo
+
+# Create and launch the interface
+if __name__ == "__main__":
+    demo = create_interface()
+    demo.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=False
+    )

requirements.txt

@@ -0,0 +1,6 @@
+gradio>=5.0.0
+torch>=2.0.0
+transformers>=4.30.0
+accelerate>=0.20.0
+sentencepiece>=0.1.99
+protobuf>=3.20.0