import streamlit as st
import pandas as pd
import numpy as np
import yfinance as yf
import altair as alt
import plotly.figure_factory as ff
import pydeck as pdk
from vega_datasets import data as vds
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
from streamlit_image_comparison import image_comparison


def on_input_change():
    user_input = st.session_state.user_input
    st.session_state.past.append(user_input)
    st.session_state.generated.append(
        {"data": "The messages from Bot\nWith new line", "type": "normal"}
    )


def on_btn_click():
    del st.session_state.past[:]
    del st.session_state.generated[:]


def main():
    st.title(" All Graphs")
    (
        col1,
        col2,
    ) = st.columns(2)
    with col1:
        st.line_chart(
            pd.DataFrame(
                {
                    "Apple": yf.download("AAPL", start="2023-01-01", end="2023-07-31")[
                        "Adj Close"
                    ],
                    "Google": yf.download(
                        "GOOGL", start="2023-01-01", end="2023-07-31"
                    )["Adj Close"],
                    "Microsoft": yf.download(
                        "MSFT", start="2023-01-01", end="2023-07-31"
                    )["Adj Close"],
                }
            )
        )
    with col2:
        data = pd.DataFrame(
            {"X": [1, 2, 3, 4, 5], "Y1": [10, 16, 8, 14, 12], "Y2": [5, 8, 3, 6, 7]}
        )
        st.area_chart(data)
    st.plotly_chart(
        ff.create_distplot(
            [np.random.randn(200) - 2, np.random.randn(200), np.random.randn(200) + 2],
            ["Negative Shift", "Normal", "Positive Shift"],
            bin_size=[0.1, 0.25, 0.5],
        ),
        use_container_width=True,
    )
    source = vds.cars()
    chart = {
        "mark": "point",
        "encoding": {
            "x": {"field": "Horsepower", "type": "quantitative"},
            "y": {"field": "Miles_per_Gallon", "type": "quantitative"},
            "color": {"field": "Origin", "type": "nominal"},
            "shape": {"field": "Origin", "type": "nominal"},
        },
    }
    tab1, tab2 = st.tabs(["Streamlit theme (default)", "Vega-Lite native theme"])
    with tab1:
        st.vega_lite_chart(source, chart, theme="streamlit", use_container_width=True)
    with tab2:
        st.vega_lite_chart(source, chart, theme=None, use_container_width=True)
    st.altair_chart(
        alt.Chart(
            pd.DataFrame(
                {
                    "x": np.random.rand(50),
                    "y": np.random.rand(50),
                    "size": np.random.randint(10, 100, 50),
                    "color": np.random.rand(50),
                }
            )
        )
        .mark_circle()
        .encode(
            x="x",
            y="y",
            size="size",
            color="color",
            tooltip=["x", "y", "size", "color"],
        )
        .properties(width=600, height=400),
        use_container_width=True,
    )
    st.bar_chart(
        pd.DataFrame(np.random.randn(20, 3), columns=["Apple", "Banana", "Cherry"])
    )
    st.pydeck_chart(
        pdk.Deck(
            map_style=None,
            initial_view_state=pdk.ViewState(
                latitude=37.76, longitude=-122.4, zoom=11, pitch=50
            ),
            layers=[
                pdk.Layer(
                    "HexagonLayer",
                    data=pd.DataFrame(
                        np.random.randn(1000, 2) / [50, 50] + [37.76, -122.4],
                        columns=["lat", "lon"],
                    ),
                    get_position="[lon, lat]",
                    radius=200,
                    elevation_scale=4,
                    elevation_range=[0, 1000],
                    pickable=True,
                    extruded=True,
                ),
                pdk.Layer(
                    "ScatterplotLayer",
                    data=pd.DataFrame(
                        np.random.randn(1000, 2) / [50, 50] + [37.76, -122.4],
                        columns=["lat", "lon"],
                    ),
                    get_position="[lon, lat]",
                    get_color="[200, 30, 0, 160]",
                    get_radius=200,
                ),
            ],
        )
    )
    import datetime

    np.random.seed(1)
    programmers = ["Alex", "Nicole", "Sara", "Etienne", "Chelsea", "Jody", "Marianne"]
    base = datetime.datetime.today()
    dates = base - np.arange(180) * datetime.timedelta(days=1)
    z = np.random.poisson(size=(len(programmers), len(dates)))
    fig = go.Figure(data=go.Heatmap(z=z, x=dates, y=programmers, colorscale="Viridis"))
    fig.update_layout(title="GitHub commits per day", xaxis_nticks=36)
    st.plotly_chart(fig)
    (
        col1,
        col2,
    ) = st.columns(2)
    with col1:
        df = px.data.gapminder().query("year == 2007").query("continent == 'Americas'")
        fig = px.pie(
            df,
            values="pop",
            names="country",
            title="Population of American continent",
            hover_data=["lifeExp"],
            labels={"lifeExp": "life expectancy"},
        )
        fig.update_traces(textposition="inside", textinfo="percent+label")
        st.plotly_chart(fig)
    with col2:
        fig = go.Figure(
            go.Sunburst(
                labels=[
                    "Eve",
                    "Cain",
                    "Seth",
                    "Enos",
                    "Noam",
                    "Abel",
                    "Awan",
                    "Enoch",
                    "Azura",
                ],
                parents=["", "Eve", "Eve", "Seth", "Seth", "Eve", "Eve", "Awan", "Eve"],
                values=[10, 14, 12, 10, 2, 6, 6, 4, 4],
            )
        )
        fig.update_layout(margin=dict(t=0, l=0, r=0, b=0))
        st.plotly_chart(fig)


if __name__ == "__main__":
    main()