import streamlit as st
from streamlit_echarts import st_echarts
import pandas as pd
from annotated_text import annotated_text, annotation
from utils import load_skill_extractor, create_dfs
from utils import clean_text, default_text, predict_cat, load_model,get_match


model = load_model()
skill_extractor = load_skill_extractor()

def app():
    st.title("Enter or past the resume here")
    with st.form(key="text_val"):
        input_text = st.text_area('', value=default_text(), height=200)
        submit_button = st.form_submit_button(label="Submit")
    
    cls_text = clean_text(input_text)
    # st.write(cls_text)
    
    col1, col2,= st.columns(2)
    gaugeData = [
    {
        "value": 0,
        "name": 'Match %',
        "detail": {
        "valueAnimation": True,
        "offsetCenter": ['0%', '0%']
        }
    }]
    option = {
    "series": [
        {
        "type": "gauge",
        "startAngle": 90,
        "endAngle": -270,
        "pointer": {
            "show": False,
        },
        "progress": {
            "show": True,
            "overlap": False,
            "roundCap":False,
            "clip": False,
            "backgroundColor": '#11D1F9',
            "itemStyle": {
            "color": '#E96605', 
            "borderWidth": 0,
            "borderColor": "light blue"
            }
        },
        "axisLine": {
            "lineStyle": {
            "width": 40
            }
        },
        "splitLine": {
            "show": False,
            "distance": 0,
            "length": 20
        },
        "axisTick": {
            "show": False
        },
        "axisLabel": {
            "show": False,
            "distance": 50
        },
        "data": gaugeData,
        "detail": {
            "valueAnimation": True,
            "offsetCenter": ['0%', '0%'],
            "width": 40,
            "height": 14,
            "fontSize": 24,
            "color": 'inherit',
            "borderColor": 'inherit',
            "borderRadius": 0,
            "borderWidth": 0,
            "formatter": '{value}%'
        },
        }
    ]
    }

    prob,job_cat = predict_cat(model, cls_text)
    annotations = skill_extractor.annotate(cls_text,tresh=1)
    text = annotations['text']
    annotations = annotations['results']    
    df = create_dfs(annotations)
    match = get_match(job_cat,df)


    with st.form(key='result'):
        if submit_button:
            gaugeData[0]['value']=match
            with col1:
                st.markdown("<h1 style= text-align: center;>Job Category</h1>", unsafe_allow_html=True)
                html_str = f"""
                            <div  
                            style = text-align: center;
                                    max-width: 300px;
                                    font-size: 16px;
                                    font-weight: bold;
                                    word-wrap: break-word;>
                                    {job_cat}
                            </div>
                            """
                
                
                st.markdown(html_str, unsafe_allow_html=True )
            
                # st.title("Probability")
                
                # st.markdown("<h1 style='text-align: center; color: #05A4E9;'>Probability</h1>", unsafe_allow_html=True)
                html_match_str = f"""<div 
                                style = text-align: center;
                                font-size: 25px; 
                                font-weight: bold; 
                                word-wrap: break-word;'>
                                Match percentage with top skills
                                </div>"""
                st.markdown(html_match_str,unsafe_allow_html=True )
                st_echarts(options=option, key="1")

            with col2:
                # st.markdown('-----------')
                df = create_dfs(annotations)
                st.markdown('## Extracted Skill :')
                st.write(", \n".join(df))