# !/usr/bin/env python
# coding: utf-8
import tkinter as tk
from tkinter import filedialog, messagebox
import os
import json
from google.protobuf.json_format import MessageToJson
from clarifai_grpc.channel.clarifai_channel import ClarifaiChannel
from clarifai_grpc.grpc.api import resources_pb2, service_pb2, service_pb2_grpc
from clarifai_grpc.grpc.api.status import status_code_pb2
# 授權(quán)密鑰
API_KEY = '5************************************'
USER_ID = '3*********h'
APP_ID = 'my***************'
# 注冊(cè)網(wǎng)址：https://www.clarifai.com/
# 網(wǎng)址注冊(cè)后，拿授權(quán)密鑰，每月1000次免費(fèi)
def analyze_image(image_path):
    # 使用 Clarifai API 進(jìn)行圖像分析
    results = []
    channel = ClarifaiChannel.get_grpc_channel()
    stub = service_pb2_grpc.V2Stub(channel)
    metadata = (('authorization', 'Key ' + API_KEY),)
    userDataObject = resources_pb2.UserAppIDSet(user_id=USER_ID, app_id=APP_ID)
    with open(image_path, 'rb') as f:
        file_bytes = f.read()
    try:
        print(f"開始分析圖像: {image_path}")  # 添加調(diào)試語(yǔ)句
        post_model_outputs_response = stub.PostModelOutputs(
            service_pb2.PostModelOutputsRequest(
                user_app_id=userDataObject,
                model_id='general-image-recognition',
                inputs=[
                    resources_pb2.Input(
                        data=resources_pb2.Data(
                            image=resources_pb2.Image(
                                base64=file_bytes
                            )
                        )
                    )
                ]
            ),
            metadata=metadata
        )
        print(f"圖像分析完成: {image_path}")  # 添加調(diào)試語(yǔ)句
    except Exception as e:
        messagebox.showerror("錯(cuò)誤", "圖像分析失敗，原因: " + str(e))
        return
    if post_model_outputs_response.status.code != status_code_pb2.SUCCESS:
        messagebox.showerror("錯(cuò)誤", "模型輸出提交失敗，狀態(tài): " + post_model_outputs_response.status.description)
        return
        # 將獲取到的結(jié)果轉(zhuǎn)為 JSON
    json_result = MessageToJson(post_model_outputs_response)
    json_output = json.loads(json_result)
    print(json_output)
    # 從 JSON 結(jié)果中提取并顯示關(guān)鍵信息
    for output in json_output["outputs"]:
        for concept in output['data']['concepts']:
            object_name = concept['name']
            results.append(object_name)
    return results
if __name__ == '__main__':
    image_path = r"D:\Desktop\tp.png"
    ret = analyze_image(image_path)