#pip install fastapi   ###for fastapi
#pip install uvicorn   ###for server. to run the api serice from terminal: uvicorn main:app --reload
#pip install gunicorn  ###gunicorn --bind 0.0.0.0:8000 -k uvicorn.workers.UvicornWorker main:app
#pip install python-multipart     ###for UploadFile
#pip install pillow    ###for PIL
#pip install transformers          ###for transformers
#pip install torch     ###for torch
#pip install sentencepiece        ###for AutoTokenizer
#pip install -U cos-python-sdk-v5      ###腾讯云对象存储SDK（COS-SDK）
#pip install -q -U google-generativeai

# from typing import Optional
# from fastapi import FastAPI, Header
# #from transformers import pipeline, EfficientNetImageProcessor, EfficientNetForImageClassification, AutoTokenizer, AutoModelForSeq2SeqLM
# import torch
# from transformers import EfficientNetImageProcessor, EfficientNetForImageClassification, pipeline
# from models import ItemInHistory, ItemUploaded, ServiceLoginInfo
# from openai import OpenAI


# import sys, os, logging
# import json, requests


from fastapi import FastAPI
import sys, logging, os
from models import Item2AI, AI2Item
import google.generativeai as genai
import urllib.parse as urlparse
from qcloud_cos import CosConfig, CosS3Client
from PIL import Image

# init app and logging
app = FastAPI()
logging.basicConfig(level=logging.INFO, stream=sys.stdout)

# load google gemini models
genai.configure(api_key=os.environ.get("GOOGLE_API_KEY"))
gemini_pro = genai.GenerativeModel('gemini-pro')
logging.info("google gemini-pro model loaded successfully.")
gemini_pro_vision = genai.GenerativeModel('gemini-pro-vision')
logging.info("google gemini-pro-vision model loaded successfully.")

# init tencent cos
cos_secret_id = os.environ['COS_SECRET_ID']
cos_secret_key = os.environ['COS_SECRET_KEY']
cos_region = 'ap-shanghai'
cos_bucket = '7072-prod-3g52ms9o7a81f23c-1324125412'
token = None
scheme = 'https'
config = CosConfig(Region=cos_region, SecretId=cos_secret_id, SecretKey=cos_secret_key, Token=token, Scheme=scheme)
client = CosS3Client(config)
logging.info(f"tencent cos init succeeded.")

# Route to create an item
@app.post("/firstturn/")
async def ai_first_turn(item: Item2AI):
    logging.info("ai_first_turn...")
    logging.info("item:", item)

    # response = gemini_pro.generate_content("What is the meaning of life?")
    # logging.info(response.text)

    url = urlparse.urlparse(item.item_fileurl)
    key = url[2][1::]
    bucket = url[1].split('.')[1]
    contentfile = key.split('/')[1]
    historyid = contentfile.split('.')[0]

    response = client.get_object(
        Bucket = bucket,
        Key = key
    )
    response['Body'].get_stream_to_file(contentfile)

    ai2item = AI2Item(
        upload_id = item.upload_id,
        union_id = item.union_id,
        item_fileurl = item.item_fileurl,
        item_mediatype = item.item_mediatype,
        upload_datetime = item.upload_datetime,
        ai_feedback = ""
    )
    if item.item_mediatype == "image":
        # Opening the image using PIL
        img = Image.open(contentfile)
        logging.info(f"image file {contentfile} is opened.")
        response = gemini_pro_vision.generate_content(["Describe this picture in Chinese with plenty of details.", img])
        ai2item.ai_feedback = response.text
    else:
        ai2item.ai_feedback = "不是image类型，暂不能识别"

    logging.info(ai2item)

    return(ai2item)


# try:
#     ai_model_bc_preprocessor = EfficientNetImageProcessor.from_pretrained("./birds-classifier-efficientnetb2")
#     ai_model_bc_model = EfficientNetForImageClassification.from_pretrained("./birds-classifier-efficientnetb2")
#     logging.info(f"local model dennisjooo/Birds-Classifier-EfficientNetB2 loaded.")
    
# except Exception as e:
#     logging.error(e)

# try:
#     openai_client = OpenAI(
#         api_key=os.environ.get("OPENAI_API_KEY"),
#     )
#     # prompt = """你是一个鸟类学家，用中文回答关于鸟类的问题。你的回答需要满足以下要求:
#     # 1. 你的回答必须是中文
#     # 2. 回答限制在100个字以内"""
#     # conv = Conversation(open_client, prompt, 3)
#     logging.info(f"openai chat model loaded.")
# except Exception as e:
#     logging.error(e)

# try:
#    ai_model_bc_pipe= pipeline("image-classification", model="dennisjooo/Birds-Classifier-EfficientNetB2")
#    logging.info(f"remote model dennisjooo/Birds-Classifier-EfficientNetB2 loaded.")

# except Exception as e:
#    print(e)

#try:
#    ai_model_ez_preprocessor = AutoTokenizer.from_pretrained("./opus-mt-en-zh")
#    ai_model_ez_model = AutoModelForSeq2SeqLM.from_pretrained("./opus-mt-en-zh")
#    print(f"local model Helsinki-NLP/opus-mt-en-zh loaded.")
#except Exception as e:
#    print(e)

#try:
#    ai_model_ez_pipe= pipeline(task="translation_en_to_zh", model="Helsinki-NLP/opus-mt-en-zh", device=0)
#    print(f"remote model Helsinki-NLP/opus-mt-en-zh loaded.")

#except Exception as e:
#    print(e)

# def bird_classifier(image_file: str) -> str:
#     # Opening the image using PIL
#     img = Image.open(image_file)
#     logging.info(f"image file {image_file} is opened.")

#     result:str = ""
#     try:
#         inputs = ai_model_bc_preprocessor(img, return_tensors="pt")

#         # Running the inference
#         with torch.no_grad():
#             logits = ai_model_bc_model(**inputs).logits

#         # Getting the predicted label
#         predicted_label = logits.argmax(-1).item()
#         result = ai_model_bc_model.config.id2label[predicted_label]
#         logging.info(f"{ai_model_bc_model.config.id2label[predicted_label]}:{ai_model_bc_pipe(img)[0]['label']}")
#     except Exception as e:
#         logging.error(e)
        

#     logging.info(result)
#     return result

# def text_en_zh(text_en: str) -> str:
#     text_zh = ""
#     if ai_model_ez_status is MODEL_STATUS.LOCAL:
#         input = ai_model_ez_preprocessor(text_en)
#         translated = ai_model_ez_model.generate(**ai_model_ez_preprocessor(text_en, return_tensors="pt", padding=True))
#         for t in translated:
#             text_zh += ai_model_ez_preprocessor.decode(t, skip_special_tokens=True)
#     elif ai_model_ez_status is MODEL_STATUS.REMOTE:
#         text_zh = ai_model_ez_pipe(text_en)

#     return text_zh


# # Route to list all items uploaded by a specific user by unionid
# # @app.get("/items/{user_unionid}")
# # def list_items(user_unionid: str) -> dict[str, list[ItemInHistory]]:
# #     logging.info("list_items")
# #     logging.info(user_unionid)

# #     items: list[ItemInHistory] = []

# #     response = client.list_objects(
# #         Bucket=cos_bucket,
# #         Prefix=f'{user_unionid}/history/'
# #     )

# #     logging.info(response['Contents'])
    
# #     for obj in response['Contents']:
# #         key:str = obj['Key']
# #         response = client.get_object(
# #             Bucket = cos_bucket,
# #             Key = key
# #         )
# #         localfile = key.split('/')[2]
# #         response['Body'].get_stream_to_file(localfile)

# #         item = itemFromJsonFile(localfile)
# #         items.append(item)

#     return {"items": items}

# # Route to list all items uploaded by a specific user by unionid from header
# @app.get("/items/")
# def list_items_byheader(x_wx_openid: Optional[str]=Header(None)) -> dict[str, list[ItemInHistory]]:
#     logging.info("list_items_byheader")
#     logging.info(x_wx_openid)

#     items: list[ItemInHistory] = []

#     response = client.list_objects(
#         Bucket=cos_bucket,
#         Prefix=f'{x_wx_openid}/history/'
#     )

#     logging.info(response['Contents'])
    
#     for obj in response['Contents']:
#         key:str = obj['Key']
#         response = client.get_object(
#             Bucket = cos_bucket,
#             Key = key
#         )
#         localfile = key.split('/')[2]
#         response['Body'].get_stream_to_file(localfile)

#         item = itemFromJsonFile(localfile)
#         items.append(item)

#     return {"items": items}


# def itemFromJsonFile(jsonfile: str) -> ItemInHistory:
#     f = open(jsonfile, 'r')
#     content = f.read()
#     a = json.loads(content)
#     f.close()
#     return ItemInHistory(history_id = a['history_id'],union_id = a['union_id'],
#                          item_fileurl = a['item_fileurl'],item_mediatype = a["item_mediatype"],
#                          upload_datetime = a["upload_datetime"],ai_feedback = a['ai_feedback'])


# def itemToJsonFile(item: ItemInHistory):
#     history_json = {
#         "history_id": item.history_id,
#         "union_id": item.union_id,
#         "item_fileurl": item.item_fileurl,
#         "item_mediatype": item.item_mediatype,
#         "upload_datetime": item.upload_datetime,
#         "ai_feedback": item.ai_feedback
#     }
#     b = json.dumps(history_json)
#     historyfile = f'{item.history_id}.json'
#     f = open(historyfile, 'w')
#     f.write(b)
#     f.close()

#     return historyfile