import gradio as gr
import requests
import os
import json
from google.oauth2 import service_account
from google.cloud import storage




# 读取图片
import base64
# Function to encode the image
def encode_image(image_path):
    with open(image_path, "rb") as image_file:
        return base64.b64encode(image_file.read()).decode('utf-8')


openai_api_key = os.environ.get('openai_api_key')

# 将图片上传到google cloud storage
def upload_image_to_gcs_blob(image):

    google_creds = os.environ.get("GOOGLE_APPLICATION_CREDENTIALS_JSON")

    creds_json = json.loads(google_creds)
    credentials = service_account.Credentials.from_service_account_info(creds_json)

    # 现在您可以使用这些凭证对Google Cloud服务进行认证
    storage_client = storage.Client(credentials=credentials, project=creds_json['project_id'])

    bucket_name = os.environ.get('bucket_name')
    bucket = storage_client.bucket(bucket_name)
    
    destination_blob_name = os.path.basename(image)
    blob = bucket.blob(destination_blob_name)

    blob.upload_from_filename(image)

    public_url = blob.public_url
    
    return public_url

from supabase import create_client, Client
def get_supabase_client():
    url = os.environ.get('supabase_url')
    key = os.environ.get('supbase_key')
    supabase = create_client(url, key)
    return supabase

def supabase_insert_ask_image(question,image,response_content):
    supabase = get_supabase_client()
    data, count = supabase.table('ask_image').insert({"question": question, "image": image,"response_content":response_content}).execute()




def ask_image(text,image,api_token=openai_api_key):
    public_url = upload_image_to_gcs_blob(image)
    print(text)
    print(public_url)
    # print('-----------------------\n')
    # messages=[
    #   {
    #     "role": "user",
    #     "content": [
    #       {"type": "text", "text": text},
    #       {
    #         "type": "image_url",
    #         "image_url": {
    #             # "url":f"data:image/jpeg;base64,{base64_image}"
    #             "url": public_url
    #         },
    #       },
    #     ],
    #   }
    # ]
    
    # # 请求头部信息
    # headers = {
    #   'Authorization': f'Bearer {api_token}'
    # }
    
    # # 请求体信息
    # data = {
    #   'model': 'gpt-4o',  # 可以根据需要更换其他模型
    #   'messages': messages,
    #   'temperature': 0.7  # 可以根据需要调整
    # }
    
    
    # # 设定最大重试次数
    # max_retry = 3
    
    # for i in range(max_retry):
    #     try:
    #         # 发送请求
    #         response = requests.post('https://burn.hair/v1/chat/completions', headers=headers, json=data)
            
    #         # 解析响应内容
    #         response_data = response.json()
    #         response_content = response_data['choices'][0]['message']['content']
    #         usage = response_data['usage']
            
    #         supabase_insert_ask_image(text,public_url,response_content)
    #         return response_content
        
    #     except Exception as e:
    #         # 如果已经达到最大重试次数，那么返回空值
    #         if i == max_retry - 1:
    #             print(f'重试次数已达上限，仍未能成功获取数据，错误信息：{e}')
    #             response_content = ''
    #             usage = {}
    #             return response_content
    #         else:
    #             # 如果未达到最大重试次数，打印错误信息，并继续下一次循环
    #             print(f'第{i+1}次请求失败，错误信息：{e}，准备进行第{i+2}次尝试')

    res =  "**Important Announcement:**  \n\nThis space is shutting down now. \n\nVisit [chatgpt-4o](https://chatgpt-4o.streamlit.app/) for an improved UI experience and future enhancements."
    return res


# gradio demo

title = "Ask Image with GPT-4o"
description = "Ask anything about your Image with GPT-4o"

demo = gr.Interface(
    fn=ask_image,
    inputs=[gr.Text(label="Question"),gr.Image(label='',type='filepath')],
    outputs=[gr.Markdown(label="Answer")],
    title = title,
    description = description
)
demo.queue(max_size = 20)

demo.launch(share = True)