diff --git a/reports/docs/ernie_tutorial/Pi5Translator.md b/reports/docs/ernie_tutorial/Pi5Translator.md
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+# Pi5 Fun Text Interpreter
+
+This project demonstrates how to build a fun text interpreter on Raspberry Pi 5 using ERNIE-4.5-0.3B. Use this repository as a minimal demo to quickly prototype a hardware project based on ERNIE-4.5-0.3B.
+
+This project mainly covers:
+- Running ERNIE-4.5-0.3B on Raspberry Pi 5 via llama.cpp
+- Performing photo-based OCR and fun text interpretation on Raspberry Pi 5
+
+> Because Raspberry Pi 5's system-level Python package management may not allow installing global dependencies directly, you should use a virtual environment to manage project dependencies. Create and activate a virtual environment first:
+```bash
+python -m venv venv
+source venv/bin/activate
+```
+
+![alt text](hardware.jpg)
+
+## Install and deploy ERNIE-4.5-0.3B
+> Nearly all end-side devices can run ERNIE-4.5-0.3B using llama.cpp. 
+
+### Install dependencies
+Installing llama-cpp-python via pip may take a long time. If your hardware is low-end, consider using brew or building from source.
+```bash
+pip install llama-cpp-python uvicorn anyio starlette fastapi sse_starlette starlette_context pydantic_settings
+```
+
+### Prepare gguf model file
+llama.cpp requires models in gguf format. You can download ERNIE-4.5-0.3B gguf files from Hugging Face or ModelScope.
+
+```bash
+# Download from Hugging Face
+pip install -U "huggingface_hub[cli]"
+hf download unsloth/ERNIE-4.5-0.3B-PT-GGUF ERNIE-4.5-0.3B-PT-Q4_K_M.gguf --local-dir .
+
+# Download from ModelScope
+pip install -U "modelscope"
+modelscope download --model unsloth/ERNIE-4.5-0.3B-PT-GGUF ERNIE-4.5-0.3B-PT-Q4_K_M.gguf --local_dir .
+```
+
+### Start an OpenAI-compatible API server
+```bash
+python -m llama_cpp.server --model ERNIE-4.5-0.3B-PT-Q4_K_M.gguf --host 0.0.0.0
+```
+
+### Call the service
+You can call the server via an OpenAI-compatible client. Open a new terminal, reactivate the virtual environment and install dependencies:
+```bash
+source venv/bin/activate
+pip install openai
+```
+
+Example usage:
+```python
+import openai
+
+server_url = "http://localhost:8000/v1"
+
+client = openai.OpenAI(
+    base_url=server_url,
+    api_key="anyvalue"
+)
+
+response = client.chat.completions.create(
+    model="anyvalue",
+    messages=[
+        {"role": "user", "content": "hi, how are you?"}
+    ],
+    temperature=0.7,
+    max_tokens=500
+)
+
+assistant_reply = response.choices[0].message.content
+print(assistant_reply)
+```
+
+## Build the Fun Text Interpreter
+### Install OCR tools
+This project uses RapidOCR as the OCR tool. RapidOCR is an open-source OCR tool based on [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR), which is Baidu's PaddlePaddle open-source OCR library supporting multi-language text recognition with high accuracy and efficiency. Activate the virtual environment in a new terminal and install dependencies:
+```bash
+source venv/bin/activate
+pip install rapidocr onnxruntime
+```
+
+Place an image named `input.jpg` in the project root to test OCR. Run:
+```python
+from rapidocr import EngineType, LangDet, ModelType, OCRVersion, RapidOCR
+
+engine = RapidOCR(
+    params={
+        "Det.engine_type": EngineType.ONNXRUNTIME,
+        "Det.model_type": ModelType.MOBILE,
+        "Det.ocr_version": OCRVersion.PPOCRV4
+    }
+)
+
+img_url = "input.jpg"
+result = engine(img_url)
+
+print(result.txts)
+```
+
+### Wrap OCR as a service
+To facilitate usage, wrap OCR into a REST API:
+```python
+# ocr_server.py
+from fastapi import FastAPI, UploadFile, File, HTTPException
+from fastapi.responses import JSONResponse
+from rapidocr import EngineType, ModelType, OCRVersion, RapidOCR
+import uvicorn
+import os
+from typing import List
+
+# Initialize OCR engine
+ocr_engine = RapidOCR(
+    params={
+        "Det.engine_type": EngineType.ONNXRUNTIME,
+        "Det.model_type": ModelType.MOBILE,
+        "Det.ocr_version": OCRVersion.PPOCRV4,
+    }
+)
+
+app = FastAPI(title="RapidOCR Service")
+
+@app.post("/ocr")
+async def ocr_service(image: UploadFile = File(...)) -> dict:
+    """
+    Accepts an uploaded image and returns OCR results (list of strings)
+    - Input: uploaded image file (PNG/JPG/JPEG supported)
+    - Output: {"result": ["text1", "text2", ...]}
+    """
+    try:
+        # Validate file type
+        if image.content_type not in ["image/png", "image/jpeg", "image/jpg"]:
+            raise HTTPException(status_code=400, detail="Only PNG/JPG/JPEG images are supported")
+        
+        # Save temporary file
+        temp_path = f"temp_{image.filename}"
+        with open(temp_path, "wb") as f:
+            f.write(await image.read())
+        
+        # Run OCR
+        ocr_result = ocr_engine(temp_path)
+        texts = ocr_result.txts
+        
+        # Clean up
+        os.remove(temp_path)
+        
+        return {"result": texts}
+    
+    except Exception as e:
+        # Remove temp file on error if it exists
+        if os.path.exists(temp_path):
+            os.remove(temp_path)
+        raise HTTPException(status_code=500, detail=str(e))
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=8001, log_level="info")
+```
+
+Run the OCR service:
+```bash
+python ocr_server.py
+```
+
+### Capture images from the Pi5 CSI camera (or USB camera)
+The CSI camera is compact and suitable for embedded use. The following is for Raspberry Pi 5; these steps should be run outside the virtual environment in a new terminal.
+
+Install camera dependency:
+```bash
+sudo apt install python3-picamera2
+```
+
+Example using Picamera2:
+```python
+from picamera2 import Picamera2
+
+picam2 = Picamera2()
+picam2.start()  # Must start the camera (even without preview)
+picam2.capture_file("test_picam2.jpg")  # Capture
+picam2.stop()
+print("Photo saved as test_picam2.jpg")
+```
+
+If using a USB camera, use OpenCV:
+```python
+import cv2
+
+cap = cv2.VideoCapture(0)
+
+if not cap.isOpened():
+    print("Cannot open camera")
+    exit()
+
+ret, frame = cap.read()
+if ret:
+    cv2.imwrite("usb_camera_image.jpg", frame)
+    print("Image saved")
+else:
+    print("Capture failed")
+
+cap.release()
+```
+
+### Build the Fun Text Interpreter UI
+Install PyQt5 and build a simple UI to continuously show the camera stream and trigger capture + OCR + fun text interpretation on click.
+
+Note: On Raspberry Pi 5, you may need to install PyQt5 outside the virtual environment:
+```bash
+sudo apt install python3-pyqt5
+```
+
+Save the following as `show.py`:
+```python
+import sys
+import requests
+import numpy as np
+from PyQt5.QtWidgets import (
+    QApplication, QLabel, QVBoxLayout, QWidget, 
+    QMessageBox
+)
+from PyQt5.QtGui import QImage, QPixmap
+from PyQt5.QtCore import QTimer, Qt
+from picamera2 import Picamera2
+from io import BytesIO
+from PIL import Image
+
+class CameraApp(QWidget):
+    def __init__(self):
+        super().__init__()
+        self.setWindowTitle("Raspberry Pi Camera + OCR + Fun Text Interpreter")
+        self.resize(800, 600)
+        
+        # Initialize camera (RGB)
+        self.picam2 = Picamera2()
+        camera_config = self.picam2.create_preview_configuration(
+            main={"format": "RGB888", "size": (640, 480)}
+        )
+        self.picam2.configure(camera_config)
+        self.picam2.start()
+        
+        # UI
+        self.label = QLabel()
+        self.label.setAlignment(Qt.AlignCenter)
+        
+        layout = QVBoxLayout()
+        layout.addWidget(self.label)
+        self.setLayout(layout)
+        
+        # Timer to update preview
+        self.timer = QTimer()
+        self.timer.timeout.connect(self.update_frame)
+        self.timer.start(30)
+    
+    def mousePressEvent(self, event):
+        """On mouse click -> capture and interpret text funnily"""
+        if event.button() == Qt.LeftButton:
+            self.capture_and_translate()
+    
+    def update_frame(self):
+        frame = self.picam2.capture_array()
+        if frame is not None:
+            h, w, ch = frame.shape
+            bytes_per_line = ch * w
+            qimg = QImage(frame.data, w, h, bytes_per_line, QImage.Format_RGB888)
+            pixmap = QPixmap.fromImage(qimg).scaled(
+                self.label.width(), self.label.height(), Qt.KeepAspectRatio
+            )
+            self.label.setPixmap(pixmap)
+    
+    def capture_and_translate(self):
+        try:
+            # 1. Capture and call OCR service
+            frame = self.picam2.capture_array()
+            pil_image = Image.fromarray(frame)
+            img_buffer = BytesIO()
+            pil_image.save(img_buffer, format="JPEG")
+            img_buffer.seek(0)
+            
+            ocr_url = "http://localhost:8001/ocr"  # OCR service endpoint
+            ocr_response = requests.post(
+                ocr_url, 
+                files={"image": ("capture.jpg", img_buffer, "image/jpeg")}
+            )
+            
+            if ocr_response.status_code != 200:
+                raise Exception(f"OCR service error: {ocr_response.text}")
+            
+            ocr_text = ocr_response.json()["result"][0]  # take first line
+            
+            # 2. Call llama.cpp interpretation via OpenAI-compatible API
+            translate_url = "http://localhost:8000/v1/chat/completions"
+            headers = {
+                "Content-Type": "application/json",
+                "Authorization": "Bearer sk-dummy"
+            }
+            data = {
+                "model": "ernie-4.5-0.3b",  # Use ERNIE-4.5-0.3B model
+                "messages": [
+                    {
+                        "role": "system",
+                        "content": "You are a fun assistant. Explain the content of the following text in a fun way."
+                    },
+                    {
+                        "role": "user",
+                        "content": f"Please explain the following text in a fun way: {ocr_text}"
+                    }
+                ]
+            }
+            
+            translate_response = requests.post(
+                translate_url, 
+                headers=headers, 
+                json=data
+            )
+            
+            if translate_response.status_code != 200:
+                raise Exception(f"Interpretation service error: {translate_response.text}")
+            
+            # 3. Parse interpretation
+            translation = translate_response.json()["choices"][0]["message"]["content"]
+            
+            # 4. Show original and interpreted text
+            result_text = f"【Original】{ocr_text}\n\n【Fun Interpretation】{translation}"
+            QMessageBox.information(self, "Fun Text Interpretation Result", result_text)
+        
+        except Exception as e:
+            QMessageBox.critical(self, "Error", f"Processing failed: {str(e)}")
+    
+    def closeEvent(self, event):
+        self.timer.stop()
+        self.picam2.stop()
+        event.accept()
+
+if __name__ == "__main__":
+    app = QApplication(sys.argv)
+    window = CameraApp()
+    window.show()
+    sys.exit(app.exec())
+```
+
+
+Run `show.py` to start the app. Ensure the OCR service and the llama.cpp server are running beforehand. After running, a camera preview will appear — point it at text and click to trigger capture, OCR, and fun text interpretation.
+
+## Future Outlook
+
+This project demonstrates the feasibility of building a fun text interpreter on Raspberry Pi 5 using the ERNIE-4.5-0.3B model. As a minimal demo, it inspires end-side AI applications. In the future, we can expand and improve in the following areas:
+
+### Model Optimization
+- **Parameter Upgrade**: Try using larger ERNIE models (e.g., ERNIE-3.5-8B or higher) to improve text interpretation accuracy and fun factor.
+- **Model Fine-tuning**: Fine-tune for specific domains (e.g., education, entertainment) to make interpretations more tailored.
+- **Quantization Optimization**: Further optimize model quantization (e.g., using GGUF Q2_K or lower precision) for lower-power devices.
+
+### Feature Expansion
+- **Multi-language Support**: Integrate multi-language OCR and translation for global users.
+- **Voice Interaction**: Add voice recognition input and speech synthesis output for full voice interaction.
+- **Enhanced Interaction**: Support continuous capture, batch processing, or user feedback to improve interpretation quality.
+- **Cloud Collaboration**: Collaborate with cloud large models when network is available to enhance interpretation.
+
+### Hardware Adaptation
+- **More Devices**: Adapt to other edge devices with compute acceleration to leverage hardware acceleration (e.g., GPU, TPU) for faster inference and lower latency.
+
+### Application Scenarios
+- **Educational Tools**: For children's learning, helping understand complex text.
+- **Entertainment Apps**: Provide fun interpretations in games or social settings.
+- **Assistive Tools**: Help visually impaired people understand text or language learners practice reading.
+
+We encourage the open-source community to contribute. If you have ideas or suggestions, feel free to submit Issues or Pull Requests!
+
+---
+
+*This document is based on the PaddlePaddle community open-source project, aiming to promote the popularization and application of AI technology.*
diff --git a/reports/docs/ernie_tutorial/Pi5Translator_cn.md b/reports/docs/ernie_tutorial/Pi5Translator_cn.md
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+# Pi5Translator
+
+本项目介绍在树莓派5上基于 ERNIE-4.5-0.3B 构建文字趣味解释器，你可以以这个项目作为最小DEMO，快速构造一个基于ERNIE-4.5-0.3B的硬件项目。
+
+本项目主要包含：
+- 如何在树莓派5上基于llama.cpp运行ERNIE-4.5-0.3B
+- 如何使用树莓派5进行拍照文字趣味解释
+
+> 由于树莓派5对于Python环境的管理机制不允许用户直接安装系统级别的依赖，因此需要使用虚拟环境来管理项目依赖。即您首先需要创建虚拟环境。
+```bash
+python -m venv venv
+source venv/bin/activate
+```
+
+![alt text](hardware.jpg)
+
+## 安装和部署Ernie0.3B
+> 几乎所有的端侧设备都可以通过llama.cpp运行Ernie0.3B。
+### 安装依赖
+通过pip安装 llama-cpp-python 需要消耗较长时间，如果你的硬件设备性能较低，推荐采用 brew 或从源码安装。
+```bash
+pip install llama-cpp-python uvicorn anyio starlette fastapi sse_starlette starlette_context pydantic_settings
+```
+
+### 准备gguf文件
+llama.cpp 需要使用 gguf 格式的模型文件，Ernie0.3B 的 gguf 模型文件可以从 [huggingface](https://huggingface.co/) 或 [魔搭社区](https://www.modelscope.cn/home) 下载。
+
+```bash
+# 从huggingface下载
+pip install -U "huggingface_hub[cli]"
+hf download unsloth/ERNIE-4.5-0.3B-PT-GGUF ERNIE-4.5-0.3B-PT-Q4_K_M.gguf --local-dir .
+
+# 从魔搭社区下载
+pip install -U "modelscope"
+modelscope download --model unsloth/ERNIE-4.5-0.3B-PT-GGUF ERNIE-4.5-0.3B-PT-Q4_K_M.gguf --local_dir .
+```
+
+### 启动OpenAI兼容的API服务
+```bash
+python -m llama_cpp.server --model ERNIE-4.5-0.3B-PT-Q4_K_M.gguf --host 0.0.0.0
+```
+
+### 调用服务
+可以通过OpenAI接口调用服务，首先，你需要新开一个终端，再次激活虚拟环境并安装依赖
+```bash
+source venv/bin/activate
+pip install openai
+```
+
+执行以下代码即可调用模型
+```python
+import openai
+
+server_url = "http://localhost:8000/v1"
+
+client = openai.OpenAI(
+    base_url=server_url,
+    api_key="anyvalue"
+)
+
+response = client.chat.completions.create(
+    model="anyvalue",
+    messages=[
+        {"role": "user", "content": "hi, how are you?"}
+    ],
+    temperature=0.7,
+    max_tokens=500
+)
+
+assistant_reply = response.choices[0].message.content
+print(assistant_reply)
+```
+
+## 构建翻译机
+### 安装OCR工具
+本项目采用 RapidOCR 作为OCR工具。RapidOCR 是基于 [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR) 的开源 OCR 工具，PaddleOCR 是百度飞桨开源的 OCR 工具库，支持多种语言的文字识别，具有高精度和高效性能。请在新的终端中激活虚拟环境并安装依赖
+```bash
+source venv/bin/activate
+pip install rapidocr onnxruntime
+```
+
+准备一张图片用于OCR识别，命名为 `input.jpg`，放在项目根目录下。执行以下代码即可完成OCR推理
+
+```python
+from rapidocr import EngineType, LangDet, ModelType, OCRVersion, RapidOCR
+
+engine = RapidOCR(
+    params={
+        "Det.engine_type": EngineType.ONNXRUNTIME,
+        "Det.model_type": ModelType.MOBILE,
+        "Det.ocr_version": OCRVersion.PPOCRV4
+    }
+)
+
+img_url = "input.jpg"
+result = engine(img_url)
+
+print(result.txts)
+```
+
+### 封装接口
+为了方便后续调用，将OCR封装为服务接口
+```
+# ocr_server.py
+from fastapi import FastAPI, UploadFile, File, HTTPException
+from fastapi.responses import JSONResponse
+from rapidocr import EngineType, ModelType, OCRVersion, RapidOCR
+import uvicorn
+import os
+from typing import List
+
+# 初始化 OCR 引擎
+ocr_engine = RapidOCR(
+    params={
+        "Det.engine_type": EngineType.ONNXRUNTIME,
+        "Det.model_type": ModelType.MOBILE,
+        "Det.ocr_version": OCRVersion.PPOCRV4,
+    }
+)
+
+app = FastAPI(title="RapidOCR Service")
+
+@app.post("/ocr")
+async def ocr_service(image: UploadFile = File(...)) -> dict:
+    """
+    接收上传的图片，返回 OCR 识别结果（字符串列表）
+    - 输入: 上传的图片文件（支持 PNG/JPG/JPEG）
+    - 输出: {"result": ["识别文本1", "识别文本2", ...]}
+    """
+    try:
+        # 检查文件类型
+        if image.content_type not in ["image/png", "image/jpeg", "image/jpg"]:
+            raise HTTPException(status_code=400, detail="仅支持 PNG/JPG/JPEG 格式图片")
+        
+        # 保存临时文件
+        temp_path = f"temp_{image.filename}"
+        with open(temp_path, "wb") as f:
+            f.write(await image.read())
+        
+        # 执行 OCR
+        ocr_result = ocr_engine(temp_path)
+        texts = ocr_result.txts  # 获取识别文本列表
+        
+        # 清理临时文件
+        os.remove(temp_path)
+        
+        return {"result": texts}
+    
+    except Exception as e:
+        # 发生错误时删除临时文件（如果存在）
+        if os.path.exists(temp_path):
+            os.remove(temp_path)
+        raise HTTPException(status_code=500, detail=str(e))
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=8001, log_level="info")
+```
+
+运行下述代码以开启服务
+```bash
+python ocr_server.py
+```
+
+### 在Pi5调用CSI摄像头(可以更换为USB摄像头)
+CSI摄像头相对于USB摄像头更加小巧，适合嵌入式应用。可以通过以下方式在树莓派5上调用CSI摄像头进行拍照。特别的，这一步需要在虚拟环境外运行，你可以新开一个窗口。
+
+首先需要安装依赖
+```bash
+sudo apt install python3-picamera2
+```
+
+安装完毕后即可使用
+```python
+from picamera2 import Picamera2
+
+picam2 = Picamera2()
+picam2.start()  # 必须启动摄像头（即使不预览）
+picam2.capture_file("test_picam2.jpg")  # 拍照
+picam2.stop()  # 关闭摄像头
+print("照片已保存为 test_picam2.jpg")
+```
+
+如果你不使用树莓派5或者CSI摄像头（例如你可能使用的是USB摄像头），你可以使用opencv库进行拍照
+```python
+import cv2
+
+# 打开 USB 摄像头（通常设备号为 0）
+cap = cv2.VideoCapture(0)
+
+if not cap.isOpened():
+    print("无法打开摄像头")
+    exit()
+
+# 捕获单帧图像
+ret, frame = cap.read()
+if ret:
+    cv2.imwrite("usb_camera_image.jpg", frame)
+    print("图像已保存")
+else:
+    print("捕获失败")
+
+# 释放资源
+cap.release()
+```
+
+### 构造文字趣味解释器
+最后，只需要安装PyQt5，再构建页面，循环读取摄像头，当用户点击界面时，即可触发拍照和OCR识别流程。
+
+首先，安装依赖，由于树莓派5的环境限制，需要在虚拟环境外安装PyQt5
+    
+```bash
+sudo apt install python3-pyqt5
+```
+
+将以下代码保存为 `show.py`
+```python
+import sys
+import requests
+import numpy as np
+from PyQt5.QtWidgets import (
+    QApplication, QLabel, QVBoxLayout, QWidget, 
+    QMessageBox
+)
+from PyQt5.QtGui import QImage, QPixmap
+from PyQt5.QtCore import QTimer, Qt
+from picamera2 import Picamera2
+from io import BytesIO
+from PIL import Image
+
+class CameraApp(QWidget):
+    def __init__(self):
+        super().__init__()
+        self.setWindowTitle("树莓派摄像头 + OCR + 文字趣味解释器")
+        self.resize(800, 600)
+        
+        # 初始化摄像头（RGB格式）
+        self.picam2 = Picamera2()
+        camera_config = self.picam2.create_preview_configuration(
+            main={"format": "RGB888", "size": (640, 480)}
+        )
+        self.picam2.configure(camera_config)
+        self.picam2.start()
+        
+        # UI 组件
+        self.label = QLabel()
+        self.label.setAlignment(Qt.AlignCenter)
+        
+        # 布局
+        layout = QVBoxLayout()
+        layout.addWidget(self.label)
+        self.setLayout(layout)
+        
+        # 定时器：实时更新摄像头画面
+        self.timer = QTimer()
+        self.timer.timeout.connect(self.update_frame)
+        self.timer.start(30)
+    
+    def mousePressEvent(self, event):
+        """鼠标点击事件 - 触发文字趣味解释"""
+        if event.button() == Qt.LeftButton:  # 只响应左键点击
+            self.capture_and_translate()
+    
+    def update_frame(self):
+        """更新摄像头画面"""
+        frame = self.picam2.capture_array()
+        if frame is not None:
+            h, w, ch = frame.shape
+            bytes_per_line = ch * w
+            qimg = QImage(frame.data, w, h, bytes_per_line, QImage.Format_RGB888)
+            pixmap = QPixmap.fromImage(qimg).scaled(
+                self.label.width(), self.label.height(), Qt.KeepAspectRatio
+            )
+            self.label.setPixmap(pixmap)
+    
+    def capture_and_translate(self):
+        """捕获画面 → OCR识别 → 文字趣味解释"""
+        try:
+            # 1. 捕获画面并调用OCR服务
+            frame = self.picam2.capture_array()
+            pil_image = Image.fromarray(frame)
+            img_buffer = BytesIO()
+            pil_image.save(img_buffer, format="JPEG")
+            img_buffer.seek(0)
+            
+            ocr_url = "http://localhost:8001/ocr"  # 假设OCR服务运行在8000端口
+            ocr_response = requests.post(
+                ocr_url, 
+                files={"image": ("capture.jpg", img_buffer, "image/jpeg")}
+            )
+            
+            if ocr_response.status_code != 200:
+                raise Exception(f"OCR服务错误: {ocr_response.text}")
+            
+            ocr_text = ocr_response.json()["result"][0]  # 取第一行识别结果
+            
+            # 2. 调用 llama.cpp 的解释接口（OpenAI API兼容）
+            translate_url = "http://localhost:8000/v1/chat/completions"  # llama.cpp的API端点
+            headers = {
+                "Content-Type": "application/json",
+                "Authorization": "Bearer sk-dummy"  # 模拟OpenAI的API key
+            }
+            data = {
+                "model": "ernie-4.5-0.3b",  # 使用 ERNIE-4.5-0.3B 模型
+                "messages": [
+                    {
+                        "role": "system",
+                        "content": "你是一个有趣的助手，请用有趣的方式解释以下文字的内容。"
+                    },
+                    {
+                        "role": "user",
+                        "content": f"请有趣地解释以下文字：{ocr_text}"
+                    }
+                ]
+            }
+            
+            translate_response = requests.post(
+                translate_url, 
+                headers=headers, 
+                json=data
+            )
+            
+            if translate_response.status_code != 200:
+                raise Exception(f"解释服务错误: {translate_response.text}")
+            
+            # 3. 解析解释结果
+            translation = translate_response.json()["choices"][0]["message"]["content"]
+            
+            # 4. 弹窗显示原文和解释
+            result_text = f"【原文】{ocr_text}\n\n【解释】{translation}"
+            QMessageBox.information(self, "文字趣味解释结果", result_text)
+        
+        except Exception as e:
+            QMessageBox.critical(self, "错误", f"处理失败: {str(e)}")
+    
+    def closeEvent(self, event):
+        """关闭时释放资源"""
+        self.timer.stop()
+        self.picam2.stop()
+        event.accept()
+
+if __name__ == "__main__":
+    app = QApplication(sys.argv)
+    window = CameraApp()
+    window.show()
+    sys.exit(app.exec())
+```
+
+运行文件 `show.py`，即可启动应用程序。确保在运行之前，已经启动了 OCR 服务和 llama.cpp 服务。
+
+
+运行后，将会看到摄像头画面，对准文字，点击屏幕即可触发文字趣味解释功能。
+
+## 未来展望
+
+本项目展示了在树莓派5上使用 ERNIE-4.5-0.3B 模型构建文字趣味解释器的可行性。作为一个最小DEMO，它为端侧AI应用提供了灵感。未来，我们可以从以下几个方面进行扩展和改进：
+
+### 模型优化
+- **参数升级**：尝试使用更大参数的 ERNIE 模型（如 ERNIE-3.5-8B 或更高），以提升文字解释的准确性和趣味性。
+- **模型微调**：针对特定领域（如教育、娱乐）进行微调，使解释更加贴合用户需求。
+- **量化优化**：进一步优化模型量化（如使用 GGUF Q2_K 或更低精度），以在更低功耗设备上运行。
+
+### 功能扩展
+- **多语言支持**：集成多语言 OCR 和翻译功能，支持全球用户。
+- **语音交互**：添加语音识别输入和语音合成输出，实现全语音交互体验。
+- **交互增强**：支持连续拍照、批量处理，或添加用户反馈机制来改进解释质量。
+- **云端协同**：在网络可用时，与云端大模型协同，提升解释效果。
+
+### 硬件适配
+- **更多设备**：适配其他带有计算加速的边缘设备，以利用硬件加速（如 GPU、TPU）提升推理速度，降低延迟。
+
+### 应用场景
+- **教育工具**：用于儿童学习，帮助理解复杂文字。
+- **娱乐应用**：在游戏或社交中提供趣味解释。
+- **辅助工具**：帮助视障人士理解文字，或语言学习者练习阅读。
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