Java学习｜JSON 处理库：Gson、FastJson、Jackson的比较与使用指南

一、安装

   pip install -U langchain-google-vertexai

二、设置访问权限

        申请服务账号json格式key

三、完整代码

import gradio as gr
import json
import base64
from pathlib import Path
import os
import time
import requests
from fastapi import FastAPI, UploadFile, File
from fastapi.middleware.cors import CORSMiddleware
import uvicorn
from langchain_core.messages import HumanMessage
from langchain_google_vertexai import ChatVertexAI
from langchain_core.output_parsers import StrOutputParser

os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "xxx.json"
app = FastAPI()
app.add_middleware(
        CORSMiddleware,
        allow_origins=["*"],
        allow_credentials=True,
        allow_methods=["*"],
        allow_headers=["*"],
    )

def encode_image(image_path):
    with open(image_path, "rb") as image_file:
        return base64.b64encode(image_file.read()).decode("utf-8")

def generate(model, prompt, images_base64):
    llm = ChatVertexAI(model_name=model)
    # example
    message = HumanMessage(
        content=[
            {
                "type": "text",
                "text": prompt,
            },
            {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{images_base64}"}},
        ]
    )
    parser = StrOutputParser()
    result = llm.invoke([message])
    parserResult = parser.invoke(result)
    return parserResult

def respond(model, image_path, prompt, chat_history):
    print(model, image_path, prompt)
    images_base64 = [encode_image(image_path)]
    bot_message = generate(model, prompt, images_base64)
    chat_history.append((prompt, bot_message))
    time.sleep(1)
    return "", chat_history

with gr.Blocks() as demo:
    gr.Image(value='xxx.png',height=30,width=70, interactive=False, show_download_button=False, show_label=False)
    gr.HTML("""<h1 align="center">图片问答</h1>""")
    
    model = gr.Textbox(value="gemini-pro-vision",label="gemini多模态模型：")
    with gr.Row():
        with gr.Column(scale=1):
            image_path = gr.Image(label="上传图片：",type="filepath", value='Picture1.png')
        with gr.Column(scale=3):
            chatbot = gr.Chatbot()
    prompt = gr.Textbox(label="用户：",value="大童在保险行业的地位如何？使用中文回答。")
    
    clear = gr.ClearButton([prompt, chatbot])
            
    prompt.submit(respond, [model, image_path, prompt, chatbot], [prompt, chatbot])

app = gr.mount_gradio_app(app, demo, path="/")

if __name__ == '__main__':
    uvicorn.run(app='web_gemini:app', host='0.0.0.0', port=8500, workers=1)

四、运行效果