import plotly.express as px
import streamlit as st
import pandas as pd
from ai_assistant import get_ai_response

def get_score_rating(s):
    if   s >= 0.75:
        return "HIGH"
    elif 0.4 <= s < 0.75:
        return "MEDIUM"
    elif s < 0.4:
        return "LOW"

def get_cov_rating(c):
    if   c >= 4:
        return "Sufficient Coverage"
    elif 2 <= c < 4:
        return "Insufficient Coverage"
    elif c < 2:
        return "Significantly Insufficient Coverage"

@st.cache_data
def get_cust_data_dict(cust_name="Wong Ling Yit"):
    
    data = pd.read_csv("data/yoda_data.csv")
    poe_data = pd.read_csv("data/yoda_poe.csv")
    reasons_df = pd.read_csv("data/yoda_reasonings.csv")

    temp = data[data["cust_name"] == cust_name]
    temp_poe = poe_data[poe_data["cust_name"] == cust_name]
    temp_reason = reasons_df[reasons_df["cust_name"] == cust_name]
    
    if len(temp) != 7 or \
       len(temp_poe) != 1 or \
       len(temp_reason) != 5:
        temp = data[data["cust_name"] == "Wong Ling Yit"]
        temp_poe = poe_data[poe_data["cust_name"] == "Wong Ling Yit"]
        temp_reason = reasons_df[reasons_df["cust_name"] == "Wong Ling Yit"]

    temp = temp.rename(columns={
                "prod_cat": "Product Category",
                "cov_level": "Coverage Level",
                "prop_score": "Score",
                "recom_products": "Recommended Product"
            })
    temp["Coverage Rating"] = temp["Coverage Level"].apply(
                                    lambda c: get_cov_rating(c)
    )
    temp["Score Rating"] = temp["Score"].apply(
                                    lambda s: get_score_rating(s)
    )
    cov_rating_map = dict(zip(
                        temp["Product Category"], 
                        temp["Coverage Rating"]
                    ))
    score_rating_map = dict(zip(
                        temp["Product Category"], 
                        temp["Score Rating"]
                    ))

    radar_df = pd.DataFrame({
                    "Product Category": [
                        "Retirement",
                        "Protection",
                        "Savings",
                        "CI",
                        "Investment",
                        "Legacy",
                        "Medical"
                    ]
    })
    radar_df = pd.merge(radar_df, temp, on="Product Category", how="inner")
    temp = temp.sort_values("Score", ascending=False).reset_index(drop=True)
    
    top_products = temp[:3]["Recommended Product"].tolist()
    
    top_score = temp.iloc[0]["Score"]
    score_rating = get_score_rating(top_score)
    top_score_msg = f"{top_score:.2f} - {score_rating}"

    poe_findings = temp_poe.iloc[0]["poe_findings"]
    
    temp_reason = temp_reason.sort_values("r_index", ascending=True).reset_index(drop=True)
    temp_reason_ls = temp_reason["reasonings"].tolist()

    return (radar_df, temp, top_products, top_score_msg, 
            poe_findings, temp_reason_ls, 
            cov_rating_map, score_rating_map)

st.title("Persona: Financial Consultant - Leads follow-up")
st.header("Lead selection", divider="blue")
st.subheader("My customers - Hot Lead🔥")

cust_option = st.selectbox(
    label="Customer options",
    options=(
        "Darek Cieslinski", "Anthony Finch", "Ariel CL Ong", 
        "Deren Meng", "Prabhavathi Bharadwaj", "Tan Li Lin",
        "Wei Shan Chin", "Wong Chen Mey", "Wong Ling Yit"),
    label_visibility="collapsed"
)

## "Wei Shan Chin", "Wong Chen Mey", "Tan Li Lin", "Prabhavathi Bharadwaj", 
## "Deren Meng", "Anthony Finch", "Ariel CL Ong", "Darek Cieslinski"
data_pack        = get_cust_data_dict(cust_name=cust_option)
radar_df         = data_pack[0]
df               = data_pack[1]
top_products     = data_pack[2]
score_msg        = data_pack[3]
poe_findings     = data_pack[4]
model_reasons    = data_pack[5]
cov_rating_map   = data_pack[6]
score_rating_map = data_pack[7]
view_1, view_2   = st.columns(2, gap="medium")

with view_1:
    st.subheader("Coverage level")
    fig = px.line_polar(radar_df, r="Coverage Level", 
                        theta="Product Category", line_close=True)
    fig.update_layout(
        margin=dict(l=60, r=40, t=20, b=20),
    )
    fig.update_traces(fill="toself")

    st.plotly_chart(fig, theme="streamlit", use_container_width=True)

with view_2:
    st.subheader("Propensity to buy")
    fig = px.bar(df, x="Product Category", y="Score")
    fig.update_layout(
        margin=dict(l=60, r=40, t=50, b=20),
    )

    st.plotly_chart(fig, theme="streamlit", use_container_width=True)

st.write("")
st.write("***Expand to see more details.***")
with st.expander("Recent engagement.."):
    st.subheader("Financial Needs Analysis (FNA)", divider="blue")
    st.write("")
    st.write("Date: 15/06/2022 - Protection need for family")
    st.write("")
    st.write("Date: 18/02/2019 - Critical Illness coverage gap of S$50,000")
    st.divider()

    st.subheader("Last policies purchased", divider="blue")
    st.write("")
    st.write("Date: 02/12/2017 - PRUActive LinkGuard purchased for self")
    st.write("")
    st.write("Date: 08/11/2013 - PRUWealth Plus (SGD) purchased for daughter")
    st.divider()

st.write("")
st.header("Insights", divider="blue")
st.markdown(
f"""
**Recommended Products:**
- {top_products[0]}
- {top_products[1]}
- {top_products[2]}

**Top LIA Coverage Gap:**
- {poe_findings}

**Propensity to buy score:**
- {score_msg}
"""
)

st.header("Reasonings", divider="blue")
st.write("")
st.markdown(
f"""
**Model Reasonings:**
- {model_reasons[0]}
- {model_reasons[1]}
- {model_reasons[2]}
- {model_reasons[3]}
- {model_reasons[4]}
"""
)
st.write("")

st.header("Sales pitch", divider="blue")
list_of_cust_tabs = st.tabs(tabs=["Summary", "Assistant"])
summary_tab = list_of_cust_tabs[0]
pitch_tab = list_of_cust_tabs[1]

about_this_cust = f"""
Opportunities
===============
In terms of current coverage level,
- Retirement: {cov_rating_map["Retirement"]}
- Protection: {cov_rating_map["Protection"]}
- Savings: {cov_rating_map["Savings"]}
- Critical Illness: {cov_rating_map["CI"]}
- Investment: {cov_rating_map["Investment"]}
- Legacy: {cov_rating_map["Legacy"]}
- Medical: {cov_rating_map["Medical"]}

In terms of likelihood to buy,
- Retirement: {score_rating_map["Retirement"]}
- Protection: {score_rating_map["Protection"]}
- Savings: {score_rating_map["Savings"]}
- Critical Illness: {score_rating_map["CI"]}
- Investment: {score_rating_map["Investment"]}
- Legacy: {score_rating_map["Legacy"]}
- Medical: {score_rating_map["Medical"]}

Recent engagements
===================
Financial Needs Analysis (FNA):
Date: 15/06/2022 - Protection need for family
Date: 18/02/2019 - Critical Illness coverage gap of S$50,000

Last policies purchased:
Date: 02/12/2017 - PRUActive LinkGuard purchased for self
Date: 08/11/2013 - PRUWealth Plus (SGD) purchased for daughter

Insights
=========
Recommended Products:
- {top_products[0]}
- {top_products[1]}
- {top_products[2]}

Top LIA Coverage Gap:
- {poe_findings}

Propensity to buy score: {score_msg}

Predictive model reasonings
===========================
- {model_reasons[0]}
- {model_reasons[1]}
- {model_reasons[2]}
- {model_reasons[3]}
- {model_reasons[4]}
""".strip()

with summary_tab:
    txt = st.text_area(
        "About this customer",
        about_this_cust,
        height=500
    )

with pitch_tab:
    st.write("Suggest sales pitch for this customer")
    generate_button = st.button("Generate")
    if generate_button:
        placeholder = st.empty()
        full_response = ""

        stream = get_ai_response(about_this_cust)
        for chunk in stream:
            token = chunk.choices[0].delta.content
            if token is not None:
                full_response += token
                # full_response += token.replace("\n", "  \n") \
                #                       .replace("$", "\$") \
                #                       .replace("\[", "$$")
                placeholder.markdown(full_response)
        placeholder.markdown(full_response)
        print(full_response)