import streamlit as st
import pandas as pd
import numpy as np
from streamlit_echarts import st_echarts
# from streamlit_echarts import JsCode
from streamlit_javascript import st_javascript
# from PIL import Image 

# links_dic = {"random": "https://seaeval.github.io/", 
#              "meta_llama_3_8b": "https://huggingface.co/meta-llama/Meta-Llama-3-8B", 
#              "mistral_7b_instruct_v0_2": "https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2", 
#              "sailor_0_5b": "https://huggingface.co/sail/Sailor-0.5B", 
#              "sailor_1_8b": "https://huggingface.co/sail/Sailor-1.8B", 
#              "sailor_4b": "https://huggingface.co/sail/Sailor-4B", 
#              "sailor_7b": "https://huggingface.co/sail/Sailor-7B", 
#              "sailor_0_5b_chat": "https://huggingface.co/sail/Sailor-0.5B-Chat", 
#              "sailor_1_8b_chat": "https://huggingface.co/sail/Sailor-1.8B-Chat", 
#              "sailor_4b_chat": "https://huggingface.co/sail/Sailor-4B-Chat", 
#              "sailor_7b_chat": "https://huggingface.co/sail/Sailor-7B-Chat", 
#              "sea_mistral_highest_acc_inst_7b": "https://seaeval.github.io/", 
#              "meta_llama_3_8b_instruct": "https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct", 
#              "flan_t5_base": "https://huggingface.co/google/flan-t5-base", 
#              "flan_t5_large": "https://huggingface.co/google/flan-t5-large", 
#              "flan_t5_xl": "https://huggingface.co/google/flan-t5-xl", 
#              "flan_t5_xxl": "https://huggingface.co/google/flan-t5-xxl", 
#              "flan_ul2": "https://huggingface.co/google/flan-t5-ul2", 
#              "flan_t5_small": "https://huggingface.co/google/flan-t5-small", 
#              "mt0_xxl": "https://huggingface.co/bigscience/mt0-xxl", 
#              "seallm_7b_v2": "https://huggingface.co/SeaLLMs/SeaLLM-7B-v2", 
#              "gpt_35_turbo_1106": "https://openai.com/blog/chatgpt", 
#              "meta_llama_3_70b": "https://huggingface.co/meta-llama/Meta-Llama-3-70B", 
#              "meta_llama_3_70b_instruct": "https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct", 
#              "sea_lion_3b": "https://huggingface.co/aisingapore/sea-lion-3b", 
#              "sea_lion_7b": "https://huggingface.co/aisingapore/sea-lion-7b", 
#              "qwen1_5_110b": "https://huggingface.co/Qwen/Qwen1.5-110B", 
#              "qwen1_5_110b_chat": "https://huggingface.co/Qwen/Qwen1.5-110B-Chat", 
#              "llama_2_7b_chat": "https://huggingface.co/meta-llama/Llama-2-7b-chat-hf", 
#              "gpt4_1106_preview": "https://openai.com/blog/chatgpt", 
#              "gemma_2b": "https://huggingface.co/google/gemma-2b", 
#              "gemma_7b": "https://huggingface.co/google/gemma-7b", 
#              "gemma_2b_it": "https://huggingface.co/google/gemma-2b-it", 
#              "gemma_7b_it": "https://huggingface.co/google/gemma-7b-it", 
#              "qwen_1_5_7b": "https://huggingface.co/Qwen/Qwen1.5-7B", 
#              "qwen_1_5_7b_chat": "https://huggingface.co/Qwen/Qwen1.5-7B-Chat", 
#              "sea_lion_7b_instruct": "https://huggingface.co/aisingapore/sea-lion-7b-instruct", 
#              "sea_lion_7b_instruct_research": "https://huggingface.co/aisingapore/sea-lion-7b-instruct-research", 
#              "LLaMA_3_Merlion_8B": "https://seaeval.github.io/", 
#              "LLaMA_3_Merlion_8B_v1_1": "https://seaeval.github.io/"}

# links_dic = {k.lower().replace('_', '-') : v for k, v in links_dic.items()}

# # huggingface_image = Image.open('style/huggingface.jpg')

# def nav_to(value):
#     try:
#         url = links_dic[str(value).lower()]
#         js = f'window.open("{url}", "_blank").then(r => window.parent.location.href);'
#         st_javascript(js)
#     except:
#         pass

#     # nav_script = """
#     #     <meta http-equiv="refresh" content="0; url='%s'">
#     # """ % (url)
#     # st.write(nav_script, unsafe_allow_html=True)

# def highlight_table_line(model_name):

#     st.write(model_name)


def draw_cross_lingual(category_one, category_two, sort, sorted):

    folder = "./results/cross_lingual/"
    subtitle = ''
    data_path = f'{folder}/{category_one}/{category_two}.csv'
    chart_data = pd.read_csv(data_path).dropna(axis='columns').round(3)

    if sorted == 'Ascending':
        ascend = True 
    else:
        ascend = False
    
    chart_data = chart_data.sort_values(by=[sort], ascending=ascend)

    min_value = round(chart_data.iloc[:, 1::].min().min() - 0.1, 1) 
    max_value = round(chart_data.iloc[:, 1::].max().max() + 0.1, 1) 
    
    st.markdown("""
                <style>
                    .stMultiSelect [data-baseweb=select] span{
                        max-width: 800px;
                        font-size: 0.9rem;
                    }
                </style>
                """, unsafe_allow_html=True)
    models = st.multiselect("Please choose the models", chart_data['Model'].tolist(), default = chart_data['Model'].tolist())

    if category_two in ['cross_mmlu', 'cross_logiqa']:  
        # print(category_two)
        
        if category_two == 'cross_mmlu':
            subtitle = 'Cross-MMLU'
        
        elif category_two == 'cross_logiqa':
            subtitle = 'Cross-LogiQA'
        
        options = {
            "title": {"text": f"{subtitle}"},
            "tooltip": {
                "trigger": "axis",
                "axisPointer": {"type": "cross", "label": {"backgroundColor": "#6a7985"}},
                "triggerOn": 'mousemove',
            },
            "legend": {"data": ['Overall Accuracy','Cross-Lingual Consistency', 'AC3',
                                'English', 'Chinese', 'Spanish', 'Vietnamese', 'Indonesian', 'Malay', 'Filipino']},
            "toolbox": {"feature": {"saveAsImage": {}}},
            "grid": {"left": "3%", "right": "4%", "bottom": "3%", "containLabel": True},
            "xAxis": [
                {
                    "type": "category",
                    "boundaryGap": True,
                    "triggerEvent": True,
                    "data": models,
                }
            ],
            "yAxis": [{"type": "value", 
                    "min": min_value,
                    "max": max_value, 
                    "boundaryGap": True
                    # "splitNumber": 10
                    }],
            "series": [
                {
                    "name": "Overall Accuracy",
                    "type": "bar", # "line"
                    "data": chart_data['Accuracy'].tolist(),
                },
                {
                    "name": "Cross-Lingual Consistency",
                    "type": "bar",
                    "data": chart_data["Cross-Lingual Consistency"].tolist(),
                },
                {
                    "name": "AC3",
                    "type": "bar",
                    "data": chart_data["AC3"].tolist(),
                },
                {
                    "name": "English",
                    "type": "bar",
                    "data": chart_data["English"].tolist(),
                },
                {
                    "name": "Chinese",
                    "type": "bar",
                    "data": chart_data["Chinese"].tolist(),
                },
                {
                    "name": "Spanish",
                    "type": "bar",
                    "data": chart_data["Spanish"].tolist(),
                },
                {
                    "name": "Vietnamese",
                    "type": "bar",
                    "data": chart_data["Vietnamese"].tolist(),
                },
                {
                    "name": "Indonesian",
                    "type": "bar",
                    "data": chart_data["Indonesian"].tolist(),
                },
                {
                    "name": "Malay",
                    "type": "bar",
                    "data": chart_data["Malay"].tolist(),
                },
                {
                    "name": "Filipino",
                    "type": "bar",
                    "data": chart_data["Filipino"].tolist(),
                },
            ],
        }
        
        # events = {
        #     "click": "function(params) { return params.value }",
        #     # "dblclick": "function(params) { return params.value }"
        # }

        value = st_echarts(options=options,  height="500px") #events=events,

        
        # if value != None:
        #     # print(value)
        #     nav_to(value)
        
        # if value != None:
        #     highlight_table_line(value)


    elif category_two == 'cross_xquad':
        
        subtitle = 'Cross-XQUAD'

        options = {
            "title": {"text": f"{subtitle}"},
            "tooltip": {
                "trigger": "axis",
                "axisPointer": {"type": "cross", "label": {"backgroundColor": "#6a7985"}},
                "triggerOn": 'mousemove',
            },
            "legend": {"data": ['Overall Accuracy','Cross-Lingual Consistency', 'AC3',
                                'English', 'Chinese', 'Spanish', 'Vietnamese', 'Indonesian', 'Malay', 'Filipino']},
            "toolbox": {"feature": {"saveAsImage": {}}},
            "grid": {"left": "3%", "right": "4%", "bottom": "3%", "containLabel": True},
            "xAxis": [
                {
                    "type": "category",
                    "boundaryGap": True,
                    "data": models,
                }
            ],
            "yAxis": [{"type": "value",
                    "min": min_value,
                    "max": max_value, 
                    "boundaryGap": True
                    # "splitNumber": 10
                    }],
            "series": [
                {
                    "name": "Overall Accuracy",
                    "type": "bar",
                    "data": chart_data['Accuracy'].tolist(),
                },
                {
                    "name": "Cross-Lingual Consistency",
                    "type": "bar",
                    "data": chart_data["Cross-Lingual Consistency"].tolist(),
                },
                {
                    "name": "AC3",
                    "type": "bar",
                    "data": chart_data["AC3"].tolist(),
                },
                {
                    "name": "English",
                    "type": "bar",
                    "data": chart_data["English"].tolist(),
                },
                {
                    "name": "Chinese",
                    "type": "bar",
                    "data": chart_data["Chinese"].tolist(),
                },
                {
                    "name": "Spanish",
                    "type": "bar",
                    "data": chart_data["Spanish"].tolist(),
                },
                {
                    "name": "Vietnamese",
                    "type": "bar",
                    "data": chart_data["Vietnamese"].tolist(),
                },
            ],
        }

        # events = {
        #     "click": "function(params) { return params.value }"
        # }

        value = st_echarts(options=options, height="500px")
        
        # if value != None:
        #     # print(value)
        #     nav_to(value)

        # if value != None:
        #     highlight_table_line(value)
    
    ### create table
    st.divider()
    # chart_data['Link'] = chart_data['Model'].map(links_dic)
    st.dataframe(chart_data,
                #  column_config = {
                #      "Link": st.column_config.LinkColumn(
                #          display_text= st.image(huggingface_image)
                #      ),
                #  }, 
                    hide_index = True, 
                    use_container_width=True)
    
    

def draw_only_acc(folder_name, category_one, category_two, sorted):
    # Cultural Reasonling / General Reasoning / Emotion / Fundamental NLP Tasks
    
    folder = f"./results/{folder_name}/"
    category_two_dict = {}
    
    if folder_name == 'cultural_reasoning':
        category_two_dict = {'SG EVAL': 'sg_eval',
                             'SG EVAL V1 Cleaned': 'sg_eval_v1_cleaned',
                             'SG EVAL V2 MCQ': 'sg_eval_v2_mcq',
                             'SG EVAL V2 Open Ended': 'sg_eval_v2_open',
                             'US EVAL': 'us_eval', 
                             'CN EVAL': 'cn_eval', 
                             'PH EVAL': 'ph_eval'}
    elif folder_name == 'general_reasoning':
        category_two_dict = {'MMLU': 'mmlu', 
                         'C Eval': 'c_eval', 
                         'CMMLU': 'cmmlu', 
                         'ZBench': 'zbench', 
                         'IndoMMLU': 'indommlu'}
    
    elif folder_name == 'emotion':
        category_two_dict = {'Indonesian Emotion  Classification': 'ind_emotion', 
                             'SST2': 'sst2'}
    
    elif folder_name == 'fundamental_nlp_tasks':
        category_two_dict = {'OCNLI': 'ocnli', 
                             'C3': 'c3', 
                             'COLA': 'cola', 
                             'QQP': 'qqp', 
                             'MNLI': 'mnli', 
                             'QNLI': 'qnli', 
                             'WNLI': 'wnli', 
                             'RTE': 'rte', 
                             'MRPC': 'mrpc'}
    
    subtitle = category_two_dict[category_two]
    data_path = f'{folder}/{category_one}/{subtitle}.csv'
    chart_data = pd.read_csv(data_path).round(3)

    st.markdown("""
                <style>
                    .stMultiSelect [data-baseweb=select] span{
                        max-width: 800px;
                        font-size: 0.9rem;
                    }
                </style>
                """, unsafe_allow_html=True)
    models = st.multiselect("Please choose the models", chart_data['Model'].tolist(), default = chart_data['Model'].tolist())
    
    if sorted == 'Ascending':
        ascend = True 
    else:
        ascend = False
    
    chart_data = chart_data.sort_values(by=['Accuracy'], ascending=ascend)
    
    min_value = round(chart_data.iloc[:, 1::].min().min() - 0.1, 1) 
    max_value = round(chart_data.iloc[:, 1::].max().max() + 0.1, 1) 

    options = {
        "title": {"text": f"{category_two}"},
        "tooltip": {
            "trigger": "axis",
            "axisPointer": {"type": "cross", "label": {"backgroundColor": "#6a7985"}},
            "triggerOn": 'mousemove',
        },
        "legend": {"data": ['Overall Accuracy']},
        "toolbox": {"feature": {"saveAsImage": {}}},
        "grid": {"left": "3%", "right": "4%", "bottom": "3%", "containLabel": True},
        "xAxis": [
            {
                "type": "category",
                "boundaryGap": True,
                "triggerEvent": True,
                "data": models,
            }
        ],
        "yAxis": [{"type": "value", 
                    "min": min_value,
                    "max": max_value, 
                    "boundaryGap": True
                    # "splitNumber": 10
                    }],
        "series": [
            {
                "name": "Overall Accuracy",
                "type": "bar",
                "data": chart_data['Accuracy'].tolist(),
            },

        ],
    }
    
    # events = {
    #     "click": "function(params) { return params.value }"
    # }

    value = st_echarts(options=options, height="500px")
    
    # if value != None:
    #     # print(value)
    #     nav_to(value)

    # if value != None:
    #     highlight_table_line(value)

    ### create table
    st.divider()
    # chart_data['Link'] = chart_data['Model'].map(links_dic)
    st.dataframe(chart_data,
                #  column_config = {
                #      "Link": st.column_config.LinkColumn(
                #          display_text= st.image(huggingface_image)
                #      ),
                #  }, 
                    hide_index = True, 
                    use_container_width=True)

def draw_flores_translation(category_one, category_two, sorted):
    folder = "./results/flores_translation/"
    category_two_dict = {'Indonesian to English': 'ind2eng', 
                         'Vitenamese to English': 'vie2eng', 
                         'Chinese to English': 'zho2eng', 
                         'Malay to English': 'zsm2eng'}
    
    subtitle = category_two_dict[category_two]
    
    data_path = f'{folder}/{category_one}/{subtitle}.csv'
    chart_data = pd.read_csv(data_path).round(3)

    if sorted == 'Ascending':
        ascend = True 
    else:
        ascend = False
    
    chart_data = chart_data.sort_values(by=['BLEU'], ascending=ascend)

    min_value = round(chart_data.iloc[:, 1::].min().min() - 0.1, 1) 
    max_value = round(chart_data.iloc[:, 1::].max().max() + 0.1, 1) 
    
    st.markdown("""
                <style>
                    .stMultiSelect [data-baseweb=select] span{
                        max-width: 800px;
                        font-size: 0.9rem;
                    }
                </style>
                """, unsafe_allow_html=True)
    models = st.multiselect("Please choose the models", chart_data['Model'].tolist(), default = chart_data['Model'].tolist())

    options = {
        "title": {"text": f"{category_two}"},
        "tooltip": {
            "trigger": "axis",
            "axisPointer": {"type": "cross", "label": {"backgroundColor": "#6a7985"}},
            "triggerOn": 'mousemove',
        },
        "legend": {"data": ['BLEU']},
        "toolbox": {"feature": {"saveAsImage": {}}},
        "grid": {"left": "3%", "right": "4%", "bottom": "3%", "containLabel": True},
        "xAxis": [
            {
                "type": "category",
                "boundaryGap": True,
                "triggerEvent": True,
                "data": models,
            }
        ],
        "yAxis": [{"type": "value", 
                    "min": min_value,
                    "max": max_value, 
                    "boundaryGap": True
                    # "splitNumber": 10
                    }],
        "series": [
            {
                "name": "BLEU",
                "type": "bar",
                "data": chart_data['BLEU'].tolist(),
            },

        ],
    }
    
    # events = {
    #     "click": "function(params) { return params.value }"
    # }

    value = st_echarts(options=options, height="500px")
    
    # if value != None:
    #     # print(value)
    #     nav_to(value)


    ### create table
    st.divider()
    # chart_data['Link'] = chart_data['Model'].map(links_dic)
    st.dataframe(chart_data,
                #  column_config = {
                #      "Link": st.column_config.LinkColumn(
                #          display_text= st.image(huggingface_image)
                #      ),
                #  }, 
                    hide_index = True, 
                    use_container_width=True)
    

def draw_dialogue(category_one, category_two, sort, sorted):
    folder = "./results/dialogue"
    category_two_dict = {'DREAM': 'dream', 
                         'SAMSum': 'samsum', 
                         'DialogSum': 'dialogsum'}
    
    subtitle = category_two_dict[category_two]
    
    data_path = f'{folder}/{category_one}/{subtitle}.csv'
    chart_data = pd.read_csv(data_path).round(3)

    st.markdown("""
                <style>
                    .stMultiSelect [data-baseweb=select] span{
                        max-width: 800px;
                        font-size: 0.9rem;
                    }
                </style>
                """, unsafe_allow_html=True)
    models = st.multiselect("Please choose the models", chart_data['Model'].tolist(), default = chart_data['Model'].tolist())

    if sorted == 'Ascending':
        ascend = True 
    else:
        ascend = False
    
    chart_data = chart_data.sort_values(by=[sort], ascending=ascend)

    min_value = round(chart_data.iloc[:, 1::].min().min() - 0.1, 1) 
    max_value = round(chart_data.iloc[:, 1::].max().max() + 0.1, 1) 
    
    options = {}
    if category_two in ['SAMSum', 'DialogSum']:
        options = {
            "title": {"text": f"{category_two}"},
            "tooltip": {
                "trigger": "axis",
                "axisPointer": {"type": "cross", "label": {"backgroundColor": "#6a7985"}},
                "triggerOn": 'mousemove',
            },
            "legend": {"data": list(chart_data.columns)},
            "toolbox": {"feature": {"saveAsImage": {}}},
            "grid": {"left": "3%", "right": "4%", "bottom": "3%", "containLabel": True},
            "xAxis": [
                {
                    "type": "category",
                    "boundaryGap": True,
                    "triggerEvent": True,
                    "data": models,
                }
            ],
            "yAxis": [{"type": "value", 
                        "min": min_value,
                        "max": max_value, 
                        "boundaryGap": True
                        # "splitNumber": 10
                        }],
            "series": [
                {
                    "name": "Average",
                    "type": "bar",
                    "data": chart_data['Average'].tolist(),
                },
                {
                    "name": "ROUGE-1",
                    "type": "bar",
                    "data": chart_data["ROUGE-1"].tolist(),
                },
                {
                    "name": "ROUGE-2",
                    "type": "bar",
                    "data": chart_data["ROUGE-2"].tolist(),
                },
                {
                    "name": "ROUGE-L",
                    "type": "bar",
                    "data": chart_data["ROUGE-L"].tolist(),
                },

            ],
        }
    
    elif category_two == 'DREAM':
        options = {
            "title": {"text": f"{category_two}"},
            "tooltip": {
                "trigger": "axis",
                "axisPointer": {"type": "cross", "label": {"backgroundColor": "#6a7985"}},
                "triggerOn": 'mousemove',
            },
            "legend": {"data": list(chart_data.columns)},
            "toolbox": {"feature": {"saveAsImage": {}}},
            "grid": {"left": "3%", "right": "4%", "bottom": "3%", "containLabel": True},
            "xAxis": [
                {
                    "type": "category",
                    "boundaryGap": True,
                    "triggerEvent": True,
                    "data": models,
                }
            ],
            "yAxis": [{"type": "value", 
                        "min": min_value,
                        "max": max_value, 
                        # "splitNumber": 10
                        "boundaryGap": True
                        }],
            "series": [
                {
                    "name": "Accuracy",
                    "type": "bar",
                    "data": chart_data['Accuracy'].tolist(),
                },

            ],
        }

    # events = {
    #     "click": "function(params) { return params.value }"
    # }

    value = st_echarts(options=options, height="500px")
    
    # if value != None:
    #     # print(value)
    #     nav_to(value)


    ### create table
    st.divider()
    # chart_data['Link'] = chart_data['Model'].map(links_dic)
    st.dataframe(chart_data,
                #  column_config = {
                #      "Link": st.column_config.LinkColumn(
                #          display_text= st.image(huggingface_image)
                #      ),
                #  }, 
                    hide_index = True, 
                    use_container_width=True)