参考链接地址：如何使用Qwen-VL模型_大模型服务平台百炼(Model Studio)-阿里云帮助中心

在windows下，使用python语言测试，版本：Python 3.8.9

import os
import base64
import requests

#  base 64 编码格式
def encode_image(image_path):
    with open(image_path, "rb") as image_file:
        return base64.b64encode(image_file.read()).decode("utf-8")

# 将xxxx/test.png替换为你本地图像的绝对路径
base64_image = encode_image("D:\\AI\\Ali_AI\\test1.png")
# 若没有配置环境变量，请用百炼API Key将下行替换为：api_key="sk-xxx"
#api_key = os.getenv("DASHSCOPE_API_KEY")
#qwen-vl-max-latest
api_key="sk-cb1a12c560fa441eabefdb2938c6cdff"
headers = {"Content-Type": "application/json", "Authorization": f"Bearer {api_key}"}
payload = {
    "model": "qwen-vl-max-latest",
    "messages": [
        {"role": "system",
         "content": [{"type":"text","text": "You are a helpful assistant."}]},
        {
            "role": "user",
            "content": [
                {
                    "type": "image_url",
                    # 需要注意，传入Base64，图像格式（即image/{format}）需要与支持的图片列表中的Content Type保持一致。"f"是字符串格式化的方法。
                    # PNG图像：  f"data:image/png;base64,{base64_image}"
                    # JPEG图像： f"data:image/jpeg;base64,{base64_image}"
                    # WEBP图像： f"data:image/webp;base64,{base64_image}"
                    "image_url": {"url": f"data:image/png;base64,{base64_image}"},
                },
                {"type": "text", "text": "识别图片中的内容并输出"},
            ],
        }
    ],
}
response = requests.post(
    "https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions",
    headers=headers,
    json=payload,
)

content1=response.json()["choices"][0]["message"]["content"]
print(content1)
payload = {
    "model": "qwen2-math-72b-instruct",
    "messages": [
        {
            "role": "system",
            "content": "You are a helpful assistant."
        },
        {
            "role": "user", 
            "content": f"{content1},给出正确的答案，不需要过程分析"
        }
    ],
}
response = requests.post(
    "https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions",
    headers=headers,
    json=payload,
)
print(response.json()["choices"][0]["message"]["content"]) 

1.先获取图片内容

使用模型：qwen-vl-max-latest

2.对数学难题进行解答

使用模型：qwen2-math-72b-instruct

二. 调用API实现图片内容提取

测试源码如下：

import os
import base64
import requests

#  base 64 编码格式
def encode_image(image_path):
    with open(image_path, "rb") as image_file:
        return base64.b64encode(image_file.read()).decode("utf-8")

# 将xxxx/test.png替换为你本地图像的绝对路径
base64_image = encode_image("D:\\AI\\Ali_AI\\test.jpg")
# 若没有配置环境变量，请用百炼API Key将下行替换为：api_key="sk-xxx"
#api_key = os.getenv("DASHSCOPE_API_KEY")
api_key="sk-d9b2178c495e4ea7bd3a11e0fc71984a"
headers = {"Content-Type": "application/json", "Authorization": f"Bearer {api_key}"}
payload = {
    "model": "qwen-vl-max-latest",
    "messages": [
        {"role": "system",
         "content": [{"type":"text","text": "You are a helpful assistant."}]},
        {
            "role": "user",
            "content": [
                {
                    "type": "image_url",
                    # 需要注意，传入Base64，图像格式（即image/{format}）需要与支持的图片列表中的Content Type保持一致。"f"是字符串格式化的方法。
                    # PNG图像：  f"data:image/png;base64,{base64_image}"
                    # JPEG图像： f"data:image/jpeg;base64,{base64_image}"
                    # WEBP图像： f"data:image/webp;base64,{base64_image}"
                    "image_url": {"url": f"data:image/png;base64,{base64_image}"},
                },
                {"type": "text", "text": "请扫描图片中的文字并输出?"},
            ],
        }
    ],
}
response = requests.post(
    "https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions",
    headers=headers,
    json=payload,
)
print(response.json()["choices"][0]["message"]["content"])

文件放置路径：

运行结果如下：