# streamlit run analysis/viz/comment_streamlit2.py
from datetime import datetime as dt
from pathlib import Path
import pandas as pd
import plotly.graph_objects as go
import plotly.express as px
import plotly.subplots as ps
import streamlit as st

workdir = Path("analysis/jobs/f452-1")
df = pd.read_csv(workdir/"review.csv")
df['date_dt'] = pd.to_datetime(df.date)
df["date_day"] = df["date_dt"].dt.date
forum = pd.read_json(workdir/"forum.jsonl", lines=True).iloc[0].to_dict()
prompt = (workdir/"prompt.txt").read_text(encoding="utf-8")

stance_colors = {'oppose':'#ffa15a', 'neutral':'#e377c2','support':'#19d3f3','unknown':'#000000'}
#stance_colors = {'oppose':'orange', 'neutral':'green','support':'blue','unknown':'gray','mixed':'violet'}
stance_order = ["oppose", "neutral", "unknown", "support"]

st.set_page_config(layout="wide")
st.title("Virginia Townhall Explorer")
st.divider()
st.subheader(forum.get('reg_title'))
st.text(forum.get('reg_desc'))
st.caption(f"Link: https://www.townhall.virginia.gov/L/Comments.cfm?GDocForumID={forum.get('forum_id')}")

st.write(f'Comments posted from {dt.strftime(min(df.date_dt),"%D")}—{dt.strftime(max(df.date_dt),"%D")}')
st.write('Data collected on _')

st.subheader("Comment Summary")
# summary
summary_left, summary_right = st.columns([1,2])
with summary_left:
    # summary table
    #summary_stats = df.groupby("stance").size().reindex(stance_order,fill_value=0).reset_index(name="count")
    summary_stats = (
    df.groupby("stance").size()
      .reindex(stance_order, fill_value=0)
      .reset_index(name="count")
      .assign(percent=lambda d: (d["count"] / d["count"].sum()).map("{:.1%}".format))
)

    st.dataframe(summary_stats, hide_index=True, width="stretch")
with summary_right:
# stance div-h
    counts = df["stance"].value_counts()
    stance_divh = go.Figure()
    stance_divh.add_bar(y=["stance"], x=[-counts.get("oppose",0)], name="oppose", orientation="h", marker_color=stance_colors.get('oppose'), text=[counts.get("oppose",0)], textposition="inside")
    stance_divh.add_bar(y=["stance"], x=[counts.get("neutral",0)], name="neutral", orientation="h", marker_color=stance_colors.get('neutral'), text=[counts.get("neutral",0)], textposition="inside")
    stance_divh.add_bar(y=["stance"], x=[counts.get("unknown",0)], name="unknown", orientation="h", marker_color=stance_colors.get('unknown'), text=[counts.get("unknown",0)], textposition="inside")
    stance_divh.add_bar(y=["stance"], x=[counts.get("support",0)], name="support", orientation="h", marker_color=stance_colors.get('support'), text=[counts.get("support",0)], textposition="inside")
    stance_divh.update_yaxes(title_text="",showticklabels=False)
    stance_divh.update_layout(barmode="relative", title="", height=180, margin=dict(l=0,r=0,t=0,b=0),xaxis_title="", yaxis_title="",legend=dict(orientation="v",y=0.12))
                              #legend_orientation="v")
    st.plotly_chart(stance_divh,width='stretch')
    
# stance_time
#stance_order = ["oppose", "neutral","unknown","support"]
#daily = df.groupby(["date_day", "stance"]).size().reset_index(name="count")
#stance_time = px.bar(daily, x="date_day", y="count", color="stance", category_orders={"stance": stance_order},color_discrete_map=stance_colors,title="")
#st.plotly_chart(stance_time, width='stretch')

# Daily Comments Breakdown, 3 Tabs
daily_wide = (
    df.groupby(["date_day", "stance"])
      .size()
      .unstack(fill_value=0)
      .reindex(columns=stance_order, fill_value=0)
      .sort_index()
)

daily_long = (
    daily_wide.reset_index()
      .melt(id_vars="date_day", var_name="stance", value_name="count")
)

cum_wide = daily_wide.cumsum()

cum_long = (
    cum_wide.reset_index()
      .melt(id_vars="date_day", var_name="stance", value_name="cumulative_count")
)

cum_total = cum_wide.sum(axis=1)
cum_share = cum_wide.div(cum_total.where(cum_total > 0), axis=0)

cum_share_long = (
    cum_share.reset_index()
      .melt(id_vars="date_day", var_name="stance", value_name="cumulative_share")
)

tab_daily, tab_area, tab_share = st.tabs([
    "Daily",
    "Cumulative",
    "Cumulative Share",
])

with tab_daily:
    fig = px.bar(
        daily_long,
        x="date_day",
        y="count",
        color="stance",
        category_orders={"stance": stance_order},
        color_discrete_map=stance_colors,
    )
    fig.update_layout(barmode="stack", height=420, legend_orientation="v")
    st.plotly_chart(fig, width="stretch")

with tab_area:
    fig = px.area(
        cum_long,
        x="date_day",
        y="cumulative_count",
        color="stance",
        category_orders={"stance": stance_order},
        color_discrete_map=stance_colors,
    )
    fig.update_layout(height=420, legend_orientation="v")
    st.plotly_chart(fig, width="stretch")

with tab_share:
    fig = px.line(
        cum_share_long,
        x="date_day",
        y="cumulative_share",
        color="stance",
        category_orders={"stance": stance_order},
        color_discrete_map=stance_colors,
    )
    fig.update_yaxes(tickformat=".0%", range=[0, 1])
    fig.update_layout(height=420, legend_orientation="v")
    st.plotly_chart(fig, width="stretch")

st.subheader("Comment Explorer")    

# stance/tone heatmap
# TODO add raw values
# TODO OPT add button to swap between pct/tone <> pct/stance
x_order = ["unknown","oppose","mixed","neutral","support"]  # includes mixed even if absent; harmless zero column
y_order = ["positive","neutral","mixed","negative","unclear"]
tab = pd.crosstab(df["tone"], df["stance"]).reindex(index=y_order, columns=x_order, fill_value=0)
pct = tab.div(tab.sum(axis=1).replace(0, pd.NA), axis=0).fillna(0)
fig = px.imshow(
    pct,
    x=x_order, y=y_order,
    text_auto=".0%",
    aspect="auto",
    color_continuous_scale="Greens",
    title="tone by stance, percent within tone",
)
fig.update_traces(text=tab.astype(str) + " / " + (pct*100).round(0).astype(int).astype(str) + "%")
fig.update_layout(height=420, xaxis_title="stance", yaxis_title="tone")
st.plotly_chart(fig, width='stretch')

# comment explorer
stance = st.multiselect("Filter stance", sorted(df["stance"].dropna().unique()), default=sorted(df["stance"].dropna().unique()))
q = st.text_input("Search comment text")
dff = df[df["stance"].isin(stance)]
if q:
    dff = dff[dff["text"].fillna("").str.contains(q, case=False, regex=False)]

st.dataframe(dff[["comment_id", "title", "text", "stance", "stance_confidence", "tone"]], width="stretch")
st.write("Showing " + str(len(dff))+ " comments")

cid = st.selectbox("comment", dff["comment_id"].astype(str))
row = dff[dff["comment_id"].astype(str) == cid].iloc[0]

st.subheader(row["title"])
st.write(row["text"])
st.write(row["author"] + ", " + row["date"][:10])
st.markdown(f"**stance:** {row['stance']} \t|\t **confidence:** {row['stance_confidence']:.2f} \t|\t **tone:** {row['tone']}")
st.write("**analysis:** "+ row["stance_rationale"])
st.write("**model:** " + str(row["model"]))
with st.expander("Prompt", expanded=False):
    st.code(prompt, language="text")