import gradio as gr
from PIL import Image
from io import BytesIO
import requests
import time
import openai
import os
from openai import OpenAI
from dotenv import load_dotenv

load_dotenv()

client = OpenAI(
    # This is the default and can be omitted
    api_key = os.getenv("OPENAI_API_KEY")
    #api_key=os.environ.get("OPENAI_API_KEY"),
)

# 设置OpenAI API密钥
openai.api_key = "sk-ut4AJ8UzqsoTVaeIATrXBGfLRcYhZbba04jnSZyOwlT3BlbkFJsEa4UxwYuflmBYBi2oLQnZJIB8-zCzTWJ9QQLMzt8A"
engine = "gpt-4o-mini"

class ImageProcessor:
    def __init__(self, api_key):
        self.api_key = api_key
        self.submit_url = 'https://test.aitanzou.com/web/api/task/submit'
        self.result_url = 'https://test.aitanzou.com/web/api/getResult'
        self.headers = {
            'API-Key': self.api_key
        }

    def submit_images(self, image_bytes_list):
        files = [('images', ('image.png', image_bytes, 'image/png')) for image_bytes in image_bytes_list]
        response = requests.post(self.submit_url, headers=self.headers, files=files)
        
        if response.status_code == 200:
            data = response.json()
            if 'data' in data and 'taskId' in data['data']:
                task_id = data['data']['taskId']
                return task_id
            else:
                raise Exception(f'Unexpected response format: {data}')
        else:
            raise Exception(f'Error: {response.status_code}, {response.text}')

    def get_result(self, task_id):
        params = {'taskId': task_id}
        while True:
            result_response = requests.get(self.result_url, params=params)
            if result_response.status_code == 200:
                result_data = result_response.json()
                if 'data' in result_data and 'abcPath' in result_data['data']:
                    if result_data['data']['abcPath'] is None:
                        print('Task is still pending...')
                        time.sleep(10)
                    else:
                        url = result_data['data']['abcPath']
                        response = requests.get(url)
                        if response.status_code == 200:
                            return response.text
                        else:
                            raise Exception(f'Error retrieving file content: {response.status_code}, {response.text}')
                else:
                    raise Exception(f'Unexpected result format: {result_data}')
            else:
                raise Exception(f'Error: {result_response.status_code}, {result_response.text}')
    
    def process_images(self, image_bytes_list):
        task_id = self.submit_images(image_bytes_list)
        return self.get_result(task_id)

# 设置Music API密钥
api_key = 'ddc85b14-bd83-4757-9bc4-8a11194da536'
image_processor = ImageProcessor(api_key)

# 定义处理函数
def process_input(text=None, images=None, audio=None):
    # 创建GPT请求的描述
    
    system = "1.你是一个专业的钢琴音乐教师，只能回答音乐知识，回复的内容为普通文本格式。如果提供的乐谱是abc记谱法,则回复时不要用abc记谱法,需要转换为传统的普通记谱法使用专业词汇进行回答问题2.你将根据下面指令回答问题，但是不能违反第一条指令,也不能在回复中提及。"
    messages = [{"role": "system", "content": system}]
    prompt = ""
    if text:
        prompt += f"\nText input: {text}"
    
    if images:
        # 使用ImageProcessor处理图像
        image_bytes_list = []
        for image in images:
            img = Image.open(image.name)
            image_bytes = BytesIO()
            img.save(image_bytes, format="PNG")
            image_bytes.seek(0)
            image_bytes_list.append(image_bytes.getvalue())
        
        try:
            processed_image_result = image_processor.process_images(image_bytes_list)
            #prompt += f"\n乐谱的内容如下,这是一首杜维诺伊的曲子，请你根据他的曲风回答问题: {processed_image_result}"
            prompt += f"\n乐谱的内容如下,这是一首杜维诺伊的曲子，请你根据他的曲风回答问题: {processed_image_result}"
        
        except Exception as e:
            return f"Error processing image: {e}", None
    
    if audio:
        # 将音频转换为描述（这里简单地用占位符）
        prompt += "\nAudio input: A description of the audio has been generated."
    
    # 使用GPT API进行处理
    try:
        messages.append({"role": "user", "content": prompt})
        response = client.chat.completions.create(
            model=engine,
            messages=messages,
            temperature=0.2,
            max_tokens=4096,
            top_p=0.95,
            frequency_penalty=0,
            presence_penalty=0,
            stop=None
        )
        print(response.choices[0].message.content)
        return response.choices[0].message.content
    except Exception as e:
        return f"Error: {e}", None

# 创建Gradio接口
iface = gr.Interface(
    fn=process_input,
    inputs=[
        gr.Textbox(label="Input Text", placeholder="Enter text here", lines=2),  # Text input with submit button
        gr.File(label="Input Images", file_count="multiple", type="file"),  # 支持多文件上传
        # gr.Audio(label="Input Audio", type="filepath"),
        
    ],
    outputs=[
        gr.Textbox(label="Output Text"),
        # gr.Audio(label="Output Audio")  # 目前示例中未处理音频输出
    ],
    live=False,
)

# 启动Gradio应用
iface.launch()