import streamlit as st
import numpy as np
import pandas as pd
import plotly.graph_objects as go
import plotly.express as px
import networkx as nx
import random
random.seed(42)
np.random.seed(42)
st.set_page_config(layout="wide")

# Load and process data
df = pd.read_csv('spices_by_cuisine_with_all_flavors.csv', index_col=0)
pivot = df.drop(columns=['Flavor Description']).sort_index()

cuisines = {}
for col in pivot.columns:
    filter = pivot[col] == 1
    cuisines[col] = pivot[filter].index.to_list()

spices = {}
pivot_t = pivot.T.sort_index()
for col in pivot_t.columns:
    filter = pivot_t[col] == 1
    spices[col] = pivot_t[filter].index.to_list()

def similarity(ratings, kind='user', epsilon=1e-9):
        if kind == 'user':
            sim = ratings.dot(ratings.T) + epsilon
        elif kind == 'item':
            sim = ratings.T.dot(ratings) + epsilon
        norms = np.array([np.sqrt(np.diagonal(sim))])
        return (sim / norms / norms.T)
pivot_names = pivot_t.columns
pivot_np = np.array(pivot_t)
cuisine_similarity = pd.DataFrame(similarity(pivot_np, kind='user'))
cuisine_similarity.columns = pivot_t.index.values
cuisine_similarity.index = pivot_t.index.values

st.title('Spices Across Cuisines')

col1, col2, col3 = st.columns(3)

with col1:
    st.subheader('By Cuisine')
    select_cuisine = st.selectbox('Select a cuisine to view the top 10 spices',cuisines.keys())
    st.write(f'The top 10 ingredients in {select_cuisine}  are:', cuisines[select_cuisine])

with col2:
    st.subheader('By Spice')
    select_spice = st.selectbox('Select a spice to view which cuisines it is present in',spices.keys())
    st.write(f'{select_spice} is part of the following cuisines:', spices[select_spice])

with col3:
    st.subheader("Similar Cuisines")
    select_cuisine_sim = st.selectbox('Select a cuisine to view the 10 most similar cuisines by spices',cuisines.keys())
    st.write(f'{select_cuisine_sim} is most similar to:', cuisine_similarity[select_cuisine_sim].sort_values(ascending=False).index[1:11].to_list())

count = pd.DataFrame(pivot.T.sum().sort_values(ascending=False).reset_index().rename(columns={0: "Count"}))
fig_bar = px.bar(count, x="Spice", y="Count", title="Most Frequently Occuring Spices Across Cuisines")
st.plotly_chart(fig_bar, use_container_width=True)

# Create a graph
G = nx.Graph()

# Add nodes for each cuisine and spice, and edges based on the DataFrame
for col in df.columns:
    if col != "Flavor Description":
        G.add_node(col, type='cuisine')
        spices_for_cuisine = df[df[col] == 1].index.tolist()
        for spice in spices_for_cuisine:
            G.add_node(spice, type='spice')
            G.add_edge(col, spice)

# Get node positions using the spring layout
pos = nx.spring_layout(G)

# Create edge trace
edge_trace = go.Scatter(
    x=[],
    y=[],
    line=dict(width=0.5, color='#888'),
    hoverinfo='none',
    mode='lines')

for edge in G.edges():
    x0, y0 = pos[edge[0]]
    x1, y1 = pos[edge[1]]
    edge_trace['x'] += tuple([x0, x1, None])
    edge_trace['y'] += tuple([y0, y1, None])

# Assign a unique color to each cuisine
cuisine_colors = {cuisine: f"hsl({i * (360 // len(df.columns[:-1]))}, 80%, 50%)" 
                  for i, cuisine in enumerate(df.columns) if cuisine != "Flavor Description"}


# Create node trace for cuisines
node_trace_cuisines = go.Scatter(
    x=[],
    y=[],
    text=[],
    hovertext=[],
    mode='markers+text',
    hoverinfo='text',
    marker=dict(
        showscale=False,
        size=20,
        color=[],
        line=dict(width=0)))

# Create node trace for spices
node_trace_spices = go.Scatter(
    x=[],
    y=[],
    text=[],
    hovertext=[],
    mode='markers+text',
    hoverinfo='text',
    marker=dict(
        showscale=False,
        color='grey',
        size=10,
        line=dict(width=0)))

for node in G.nodes():
    x, y = pos[node]
    if G.nodes[node]['type'] == 'cuisine':
        node_trace_cuisines['x'] += tuple([x])
        node_trace_cuisines['y'] += tuple([y])
        node_trace_cuisines['text'] += tuple([node])
        node_trace_cuisines['marker']['color'] += tuple([cuisine_colors[node]])
        
        # Collect all spices associated with this cuisine
        spices_associated = df[df[node] == 1].index.tolist()
        hover_text = f"{node} uses: {', '.join(spices_associated)}"
        node_trace_cuisines['hovertext'] += tuple([hover_text])
        
    else:
        node_trace_spices['x'] += tuple([x])
        node_trace_spices['y'] += tuple([y])
        node_trace_spices['text'] += tuple([node])
        
        # Collect all cuisines that use this spice
        cuisines_using_spice = df.columns[df.loc[node] == 1].tolist()
        hover_text = f"{node} is used in: {', '.join(cuisines_using_spice)}"
        node_trace_spices['hovertext'] += tuple([hover_text])

# Create the network graph figure with updated hover information
fig_graph = go.Figure(data=[edge_trace, node_trace_cuisines, node_trace_spices],
                layout=go.Layout(
                    title="Network Graph of Cuisines and their Spices",
                    titlefont_size=16,
                    showlegend=False,
                    hovermode='closest',
                    margin=dict(b=20, l=5, r=5, t=40),
                    xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
                    yaxis=dict(showgrid=False, zeroline=False, showticklabels=False))
                )

st.plotly_chart(fig_graph, use_container_width=True)