import os

import gradio as gr
import pandas as pd
from dotenv import load_dotenv
import jieba
jieba.cut('你好')
from wordcloud import WordCloud
from PIL import Image
import matplotlib.pyplot as plt
from loguru import logger

from sheet import compose_query, get_serp, get_condensed_result, extract_results, postprocess_result, format_output, category2supercategory

load_dotenv()

# logger = logging.getLogger(__name__) 
# logger.setLevel(logging.DEBUG)

classes = list([ x for x in category2supercategory.keys() if len(x)>0])

def plot_wordcloud( text):
    """
    """
    if os.getenv("FONT_PATH", None) is not None:
        wc_generator = WordCloud(font_path=os.getenv("FONT_PATH"))
    else:
        wc_generator = WordCloud()
    img = wc_generator.generate( " ".join(jieba.cut(text)))
    # fig, ax = plt.subplots()
    # ax.imshow(wordcloud, interpolation='bilinear')
    # ax.axis("off")
    return img.to_image()

def format_category( formatted_results):
    """
    """
    return "\n\n".join([
        f"> 大類別：{formatted_results['supercategory'].values[0]}",
        f"> 小類別：{formatted_results['category'].values[0]}",
        f"> 推測提供酒品：{ '是' if formatted_results['provide_alcohol'].values[0] else '否' }",
        f"> 商家名稱：{formatted_results['store_name'].values[0]}",
        f"> 電話：{formatted_results['phone_number'].values[0]}",
        f"> 描述：{formatted_results['description'].values[0]}"
    ])

def do( business_name: str, address: str):
    """
    """

    crawled_results = []
    provider = os.environ.get("DEFAULT_PROVIDER", "openai")
    model = os.environ.get("DEFAULT_MODEL", "'gpt-4o'") 

    google_domain = "google.com.tw"
    gl = 'tw'
    lr  = 'lang_zh-TW'
    business_id = 12345678
    
    query = compose_query(address, business_name)
    try:
        res = get_serp( query, google_domain, gl, lr)
    except Exception as e:
        return f"Error: {e}"
    
    cond_res = get_condensed_result(res)

    crawled_results.append( { 
        "index": 0, 
        "business_id": business_id, 
        "business_name": business_name, 
        "serp": res,
        "evidence": cond_res, 
        "address": address
    } )

    crawled_results = pd.DataFrame(crawled_results)
    # logger.debug(crawled_results)
    extracted_results = extract_results( crawled_results, classes=classes, provider = provider, model = model)
    # logger.error(extracted_results['extracted_results'].columns)
    extracted_results = extracted_results['extracted_results'][ [ 'business_id', 'business_name', 'address', 'category', 'evidence', 'phone_number', 'description', 'store_name', 'provide_alcohol'] ]
    logger.debug( extracted_results['category'])
    
    postprocessed_results = postprocess_result( extracted_results, postprocessed_results_path="/tmp/postprocessed_results.joblib", category_hierarchy=category2supercategory)
    os.remove("/tmp/postprocessed_results.joblib")

    formatted_results = format_output( postprocessed_results)
    logger.debug( formatted_results)
    
    formatted_output = format_category( formatted_results)

    img = plot_wordcloud(formatted_results['formatted_evidence'].values[0])
    return f"【搜尋結果】\n{formatted_results['formatted_evidence'].values[0][6:]}", img, f"【判斷結果】\n{formatted_output}"

def load( blob, progress=gr.Progress()):
    """
    """
    if isinstance(blob, str):
    #   df = pd.read_csv(StringIO(temp_file), parse_dates=[ "Start", "Finish"])
        df = pd.read_csv(blob, names=COLUMNS, header=None) # parse_dates=[ "Start", "Finish"]
    else:
      df = pd.read_csv(blob.name, names=COLUMNS, header=None) # parse_dates=[ "Start", "Finish"]
    print( df.head() )
    return df

## --- interface --- ##
# outputs = [gr.Dataframe(row_count = (1, "dynamic"), col_count=(6,"dynamic"), label="output data", interactive=1)]
# demo = gr.Interface(
#         fn=do,
#         inputs=[ "text", "text", "text"],
#         outputs=outputs,
#     )

COLUMNS = ['營業地址', '統一編號', '總機構統一編號', '營業人名稱', '資本額', '設立日期', '組織別名稱', '使用統一發票',
        '行業代號', '名稱', '行業代號1', '名稱1', '行業代號2', '名稱2', '行業代號3', '名稱3']
CSS = """
h1 {
    text-align: center;
    display:block;
}
"""
## --- block --- ##
with gr.Blocks(css=CSS) as demo:
    gr.Markdown("# 🌟 自動分類餐廳型態 🌟")
    with gr.Tab('單筆'):
        with gr.Row():
            inputs = [ gr.Textbox( label="商家名稱", placeholder="輸入商家或公司名稱"), gr.Textbox(label="地址", placeholder="至少輸入縣市，完整地址更好")]
        with gr.Row():
            btn = gr.Button("Submit")
        with gr.Row():
            outputs = [ gr.Markdown( label="參考資料（google search）"), gr.Image( label="文字雲"), gr.Markdown( label="類別", )]
        btn.click(fn=do, inputs=inputs, outputs=outputs)
    with gr.Tab('批次'):
        with gr.Row():
            batch_inputs = [ gr.UploadButton("上傳檔案", file_count="single")]
        with gr.Row():
            batch_btn = gr.Button("批量處理")
        with gr.Row():
            batch_outputs = [ gr.Dataframe(
                headers=COLUMNS,
                datatype=["str"] * 16
            )]
        batch_btn.click(fn=load, inputs=batch_inputs, outputs=batch_outputs)
        


if __name__ == "__main__":
    
    demo.launch(
        # share=True, 
        server_name = '0.0.0.0', auth=( os.environ.get('USERNAME'), os.environ.get('PASSWORD'))
    )