Gradio测试--＞Gradio映射--＞可视化GPT4V API--＞Gemini Pro、Claude和Qwen的API

Gradio测试

import gradio as gr
import time

def demo_test(text, image=None):
    time.sleep(1)  # 正确的暂停调用
    return text, image if image is not None else None

# 创建 Gradio 接口
iface = gr.Interface(
    fn=demo_test,
    inputs=[gr.Textbox(label="输入文本"), gr.Image(type="pil", label="上传图片")],
    outputs=[gr.Textbox(label="输出文本"), gr.Image(type="pil", label="输出图片")]
)

iface.launch(server_name="0.0.0.0", server_port=1234)
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Gradio映射

本地机器运行：
-L：指定远程机器端口是1234，本地机器的端口号的8888。
用户名：远程机器的用户名
ip地址：远程机器的IP地址

ssh -CNg -L localhost:8888:0.0.0.0:1234  用户名@ip地址 -p PID
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Gradio可视化GPT4V API

1. GPT4V

注意：需要设置代理端口

import gradio as gr
import requests
import os
import base64
import io

# 设置代理，以确保能够连接到 API
# os.environ["http_proxy"] = "127.0.0.1:58591"
# os.environ["https_proxy"] = "127.0.0.1:58591"

# 你的 OpenAI API 密钥
api_key = "sk-"

# 函数：将 PIL 图像对象编码为 base64 格式
def encode_image(image):
    if image is None:
        return None  # 如果没有图片，则返回 None
    buffered = io.BytesIO()
    try:
        image.save(buffered, format="JPEG")
        img_str = base64.b64encode(buffered.getvalue()).decode('utf-8')
        return img_str
    except Exception as e:
        print(f"编码图像时出错: {e}")
        return None

# 函数：处理 GPT-4 API 请求
def demo_test(text, image=None):
    message_content = [{"type": "text", "text": text}]

    if image is not None:
        base64_image = encode_image(image)
        if base64_image is not None:
            message_content.append({
                "type": "image_url",
                "image_url": {"url": f"data:image/jpeg;base64,{base64_image}"}
            })

    headers = {
        "Content-Type": "application/json",
        "Authorization": f"Bearer {api_key}"
    }

    payload = {
        "model": "gpt-4-vision-preview",
        "messages": [{"role": "user", "content": message_content}],
        "max_tokens": 3000
    }

    response = requests.post("https://api.openai.com/v1/chat/completions", headers=headers, json=payload)
    response_text = response.json()["choices"][0]["message"]["content"] if response.ok else "处理请求时出错"

    print(response_text)
    return response_text, image if image is not None else None

# 创建 Gradio 接口
iface = gr.Interface(
    fn=demo_test,
    inputs=[gr.Textbox(label="输入文本"), gr.Image(type="pil", label="上传图片")],
    outputs=[gr.Textbox(label="输出文本"), gr.Image(type="pil", label="输出图片")]
)

iface.launch()
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Gemini Pro、Claude和Qwen的API

1. Gemini Pro

# setup
import google.generativeai as genai

genai.configure(api_key='')  # 填入自己的api_key

# 查询模型
for m in genai.list_models():
    print(m.name)
    print(m.supported_generation_methods)
import PIL.Image
import os
# 创建模型实例
model = genai.GenerativeModel('gemini-pro-vision')
# 文件夹路径
folder_path = ''

# 结果文件
results_file = ''

count =0

# 遍历文件夹中的图像
for filename in os.listdir(folder_path):
    if filename.endswith('.jpg') or filename.endswith('.png'):  # 检查文件是否为图像
        # 图像路径
        image_path = os.path.join(folder_path, filename)

        img = PIL.Image.open(image_path)

        # 使用模型进行提问
        question = "描述一下这张图像"
        response = model.generate_content([question, img], stream=True)
        response.resolve()

        # 将结果写入文件
        with open(results_file, 'a') as file:
            file.write(f"{filename} {response.text}\n")

        # 更新计数器
        count += 1

        # 每处理100张图像打印一次
        if count % 5 == 0:
            print(f"已处理 {count} 张图像")

print("处理完成！")
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2. Claude

2.1 注册

1. 注册可以免费使用chat网页版
2. 5刀Claude 3 Opus使用额度
   支付宝购买虚拟手机卡：https://sms-man.com/cn

2.2 API

…

3.Qwen

import os

# 设置环境变量
os.environ['DASHSCOPE_API_KEY'] = 'sk-'

# 之后您可以使用这个环境变量
api_key = os.environ['DASHSCOPE_API_KEY']

from dashscope import MultiModalConversation


def call_with_local_file():
    """Sample of use local file.
       linux&mac file schema: file:///home/images/test.png
       windows file schema: file://D:/images/abc.png
    """
    local_file_path1 = 'file:///opt/data/private/434103892.jpg'
    messages = [{
        'role': 'system',
        'content': [{
            'text': 'You are a helpful assistant.'
        }]
    }, {
        'role':
        'user',
        'content': [
            {
                'image': local_file_path1
            },
            {
                'text': '图片里有什么东西?'
            },
        ]
    }]
    response = MultiModalConversation.call(model='qwen-vl-max', messages=messages)
    # print(response)
    text_content = response['output']['choices'][0]['message']['content'][0]['text']
    print(text_content)


if __name__ == '__main__':
    call_with_local_file()
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