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deepseek视觉模型调用

2025.03.13 jinance 学习  热度 ℃

通过前端上传图片以及描述，提交数据给后端，然后调用deepseek视觉模型分析数据并返回给前端。

前端代码：

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<!DOCTYPE html>
<html lang="zh-CN">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>图片上传与分析</title>
    <style>
        body {
            font-family: Arial, sans-serif;
            margin: 20px;
        }
        input, button {
            margin: 10px 0;
            padding: 8px;
            width: 300px;
        }
        button {
            cursor: pointer;
        }
        #result {
            margin-top: 20px;
            padding: 10px;
            background-color: #f9f9f9;
            border: 1px solid #ddd;
        }
    </style>
</head>
<body>
    <h1>图片上传与分析</h1>
    <div>
        <label for="imageFile">请选择图片文件：</label>
        <input type="file" id="imageFile" accept="image/*" />
    </div>
    <div>
        <label for="text">请输入描述文本：</label>
        <input type="text" id="text" placeholder="请输入描述文本" />
    </div>
    <button onclick="uploadImage()" id="submitBtn">提交</button>

    <div id="result"></div>

    <script>
        async function uploadImage() {
            const imageFile = document.getElementById('imageFile').files[0];
            const text = document.getElementById('text').value;
            const submitBtn = document.getElementById('submitBtn');
            const resultDiv = document.getElementById('result');

            // 验证输入
            if (!imageFile || !text) {
                alert('请选择图片文件并输入描述文本');
                return;
            }

            // 禁用按钮并显示加载状态
            submitBtn.disabled = true;
            submitBtn.innerText = '提交中...';

            // 构造 FormData
            const formData = new FormData();
            formData.append('image', imageFile);
            formData.append('text', text);

            try {
                const response = await fetch('/api/process-image', {
                    method: 'POST',
                    body: formData,
                });

                if (response.ok) {
                    const result = await response.json();
                    resultDiv.innerHTML = `<h3>返回结果：</h3><pre>${JSON.stringify(result, null, 2)}</pre>`;
                } else {
                    resultDiv.innerHTML = '服务器错误，请稍后重试。';
                }
            } catch (error) {
                resultDiv.innerHTML = '请求失败，请检查网络或稍后重试。';
            } finally {
                // 重新启用按钮
                submitBtn.disabled = false;
                submitBtn.innerText = '提交';
            }
        }
    </script>
</body>
</html>

后端代码：

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from flask import Flask, render_template, request, jsonify
import requests
import os
from werkzeug.utils import secure_filename
import logging
import base64

app = Flask(__name__)

# 配置日志
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# 临时保存图片的目录
UPLOAD_FOLDER = 'uploads'
if not os.path.exists(UPLOAD_FOLDER):
    os.makedirs(UPLOAD_FOLDER)

app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER

@app.route('/')
def index():
    return render_template('deepseek.html')  # 渲染主页

@app.route('/api/process-image', methods=['POST'])
def process_image():
    try:
        # 获取上传的图片文件和文本
        if 'image' not in request.files or 'text' not in request.form:
            return jsonify({"error": "缺少图片文件或描述文本"}), 400

        image_file = request.files['image']
        text = request.form['text']

        # 验证文件
        if image_file.filename == '':
            return jsonify({"error": "未选择图片文件"}), 400

        # 保存图片到临时目录
        filename = secure_filename(image_file.filename)
        image_path = os.path.join(app.config['UPLOAD_FOLDER'], filename)
        image_file.save(image_path)

        # 调用外部 API 进行处理
        api_url = "https://api.siliconflow.cn/v1/chat/completions"
        headers = {
            "Authorization": "Bearer sk-xxxxxxxxxxxxxxxxxx",  # 直接硬编码 API 密钥
            "Content-Type": "application/json"
        }

        # 读取图片文件并转换为 Base64
        with open(image_path, "rb") as image_file:
            image_base64 = base64.b64encode(image_file.read()).decode('utf-8')

        payload = {
            "model": "deepseek-ai/deepseek-vl2",
            "frequency_penalty": 0,
            "temperature": 0.2,
            "messages": [
                {
                    "role": "user",
                    "content": [
                        {
                            "image_url": {
                                "url": f"data:image/jpeg;base64,{image_base64}",
                                "detail": "auto"
                            },
                            "type": "image_url"
                        },
                        {
                            "text": text,  # 使用用户输入的文本
                            "type": "text"
                        }
                    ]
                }
            ]
        }

        # 发送请求到外部 API
        response = requests.post(api_url, json=payload, headers=headers)

        # 删除临时图片文件
        os.remove(image_path)

        if response.status_code == 200:
            return jsonify(response.json())  # 返回外部 API 的响应
        else:
            logger.error(f"外部 API 请求失败，状态码: {response.status_code}")
            return jsonify({"error": "外部 API 请求失败"}), response.status_code

    except Exception as e:
        logger.error(f"发生错误: {str(e)}")
        return jsonify({"error": str(e)}), 500

if __name__ == '__main__':
    app.run(debug=True)
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