import json
from typing import List

import networkx as nx
import nltk
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
import streamlit as st
from annotated_text import annotated_text, parameters
from streamlit_extras import add_vertical_space as avs
from streamlit_extras.badges import badge

from scripts.utils import get_filenames_from_dir

# Set page configuration
st.set_page_config(
    page_title="Resume Matcher",
    page_icon="Assets/img/favicon.ico",
    initial_sidebar_state="auto",
)

# Check if NLTK punkt_tab data is available, if not, download it
try:
    nltk.data.find("tokenizers/punkt_tab")
except LookupError:
    nltk.download("punkt_tab")

parameters.SHOW_LABEL_SEPARATOR = False
parameters.BORDER_RADIUS = 3
parameters.PADDING = "0.5 0.25rem"


def create_star_graph(nodes_and_weights, title):
    # Create an empty graph
    G = nx.Graph()

    # Add the central node
    central_node = "resume"
    G.add_node(central_node)

    # Add nodes and edges with weights to the graph
    for node, weight in nodes_and_weights:
        G.add_node(node)
        G.add_edge(central_node, node, weight=weight * 100)

    # Get position layout for nodes
    pos = nx.spring_layout(G)

    # Create edge trace
    edge_x = []
    edge_y = []
    for edge in G.edges():
        x0, y0 = pos[edge[0]]
        x1, y1 = pos[edge[1]]
        edge_x.extend([x0, x1, None])
        edge_y.extend([y0, y1, None])

    edge_trace = go.Scatter(
        x=edge_x,
        y=edge_y,
        line=dict(width=0.5, color="#888"),
        hoverinfo="none",
        mode="lines",
    )

    # Create node trace
    node_x = []
    node_y = []
    for node in G.nodes():
        x, y = pos[node]
        node_x.append(x)
        node_y.append(y)

    node_trace = go.Scatter(
        x=node_x,
        y=node_y,
        mode="markers",
        hoverinfo="text",
        marker=dict(
            showscale=True,
            colorscale="Rainbow",
            reversescale=True,
            color=[],
            size=10,
            colorbar=dict(
                thickness=15,
                title="Node Connections",
                xanchor="left",
                titleside="right",
            ),
            line_width=2,
        ),
    )

    # Color node points by number of connections
    node_adjacencies = []
    node_text = []
    for node in G.nodes():
        adjacencies = list(G.adj[node])  # changes here
        node_adjacencies.append(len(adjacencies))
        node_text.append(f"{node}<br># of connections: {len(adjacencies)}")

    node_trace.marker.color = node_adjacencies
    node_trace.text = node_text

    # Create the figure
    fig = go.Figure(
        data=[edge_trace, node_trace],
        layout=go.Layout(
            title=title,
            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),
        ),
    )

    # Show the figure
    st.plotly_chart(fig)


def create_annotated_text(
    input_string: str, word_list: List[str], annotation: str, color_code: str
):
    # Tokenize the input string
    tokens = nltk.word_tokenize(input_string)

    # Convert the list to a set for quick lookups
    word_set = set(word_list)

    # Initialize an empty list to hold the annotated text
    annotated_text = []

    for token in tokens:
        # Check if the token is in the set
        if token in word_set:
            # If it is, append a tuple with the token, annotation, and color code
            annotated_text.append((token, annotation, color_code))
        else:
            # If it's not, just append the token as a string
            annotated_text.append(token)

    return annotated_text


def read_json(filename):
    with open(filename) as f:
        data = json.load(f)
    return data


def tokenize_string(input_string):
    tokens = nltk.word_tokenize(input_string)
    return tokens


# Display the main title and subheaders
st.title(":blue[Resume Matcher]")
with st.sidebar:
    st.image("Assets/img/header_image.png")
    st.subheader(
        "Free and Open Source ATS to help your resume pass the screening stage."
    )
    st.markdown(
        "Check the website [www.resumematcher.fyi](https://www.resumematcher.fyi/)"
    )

    st.markdown(
        "Give Resume Matcher a ⭐ on [GitHub](https://github.com/srbhr/resume-matcher)"
    )

    badge(type="github", name="srbhr/Resume-Matcher")
    st.markdown("For updates follow me on Twitter.")
    badge(type="twitter", name="_srbhr_")
    st.markdown(
        "If you like the project and would like to further help in development please consider 👇"
    )
    badge(type="buymeacoffee", name="srbhr")

st.divider()
avs.add_vertical_space(1)

resume_names = get_filenames_from_dir("Data/Processed/Resumes")

output = st.selectbox(
    f"There are {len(resume_names)} resumes present. Please select one from the menu below:",
    resume_names,
)

avs.add_vertical_space(5)

selected_file = read_json("Data/Processed/Resumes/" + output)

avs.add_vertical_space(2)
st.markdown("#### Parsed Resume Data")
st.caption(
    "This text is parsed from your resume. This is how it'll look like after getting parsed by an ATS."
)
st.caption("Utilize this to understand how to make your resume ATS friendly.")
avs.add_vertical_space(3)
# st.json(selected_file)
st.write(selected_file["clean_data"])

avs.add_vertical_space(3)
st.write("Now let's take a look at the extracted keywords from the resume.")

annotated_text(
    create_annotated_text(
        selected_file["clean_data"],
        selected_file["extracted_keywords"],
        "KW",
        "#0B666A",
    )
)

avs.add_vertical_space(5)
st.write("Now let's take a look at the extracted entities from the resume.")

# Call the function with your data
create_star_graph(selected_file["keyterms"], "Entities from Resume")

df2 = pd.DataFrame(selected_file["keyterms"], columns=["keyword", "value"])

# Create the dictionary
keyword_dict = {}
for keyword, value in selected_file["keyterms"]:
    keyword_dict[keyword] = value * 100

fig = go.Figure(
    data=[
        go.Table(
            header=dict(
                values=["Keyword", "Value"], font=dict(size=12), fill_color="#070A52"
            ),
            cells=dict(
                values=[list(keyword_dict.keys()), list(keyword_dict.values())],
                line_color="darkslategray",
                fill_color="#6DA9E4",
            ),
        )
    ]
)
st.plotly_chart(fig)

st.divider()

fig = px.treemap(
    df2,
    path=["keyword"],
    values="value",
    color_continuous_scale="Rainbow",
    title="Key Terms/Topics Extracted from your Resume",
)
st.write(fig)

avs.add_vertical_space(5)

job_descriptions = get_filenames_from_dir("Data/Processed/JobDescription")

output = st.selectbox(
    f"There are {len(job_descriptions)} job descriptions present. Please select one from the menu below:",
    job_descriptions,
)

avs.add_vertical_space(5)

selected_jd = read_json("Data/Processed/JobDescription/" + output)

avs.add_vertical_space(2)
st.markdown("#### Job Description")
st.caption(
    "Currently in the pipeline I'm parsing this from PDF but it'll be from txt or copy paste."
)
avs.add_vertical_space(3)
# st.json(selected_file)
st.write(selected_jd["clean_data"])

st.markdown("#### Common Words between Job Description and Resumes Highlighted.")

annotated_text(
    create_annotated_text(
        selected_file["clean_data"], selected_jd["extracted_keywords"], "JD", "#F24C3D"
    )
)

st.write("Now let's take a look at the extracted entities from the job description.")

# Call the function with your data
create_star_graph(selected_jd["keyterms"], "Entities from Job Description")

df2 = pd.DataFrame(selected_jd["keyterms"], columns=["keyword", "value"])

# Create the dictionary
keyword_dict = {}
for keyword, value in selected_jd["keyterms"]:
    keyword_dict[keyword] = value * 100

fig = go.Figure(
    data=[
        go.Table(
            header=dict(
                values=["Keyword", "Value"], font=dict(size=12), fill_color="#070A52"
            ),
            cells=dict(
                values=[list(keyword_dict.keys()), list(keyword_dict.values())],
                line_color="darkslategray",
                fill_color="#6DA9E4",
            ),
        )
    ]
)
st.plotly_chart(fig)

st.divider()

fig = px.treemap(
    df2,
    path=["keyword"],
    values="value",
    color_continuous_scale="Rainbow",
    title="Key Terms/Topics Extracted from the selected Job Description",
)
st.write(fig)

avs.add_vertical_space(5)

st.divider()

st.markdown("## Vector Similarity Scores")
st.caption("Powered by Qdrant Vector Search")
st.info("These are pre-computed queries", icon="ℹ")
st.warning(
    "Running Qdrant or Sentence Transformers without having capacity is not recommended",
    icon="⚠",
)


# Your data
data = [
    {
        "text": "{'resume': 'Alfred Pennyworth",
        "query": "Job Description Product Manager",
        "score": 0.62658,
    },
    {
        "text": "{'resume': 'Barry Allen",
        "query": "Job Description Product Manager",
        "score": 0.43777737,
    },
    {
        "text": "{'resume': 'Bruce Wayne ",
        "query": "Job Description Product Manager",
        "score": 0.39835533,
    },
    {
        "text": "{'resume': 'JOHN DOE",
        "query": "Job Description Product Manager",
        "score": 0.3915512,
    },
    {
        "text": "{'resume': 'Harvey Dent",
        "query": "Job Description Product Manager",
        "score": 0.3519544,
    },
    {
        "text": "{'resume': 'Barry Allen",
        "query": "Job Description Senior Full Stack Engineer",
        "score": 0.6541866,
    },
    {
        "text": "{'resume': 'Alfred Pennyworth",
        "query": "Job Description Senior Full Stack Engineer",
        "score": 0.59806436,
    },
    {
        "text": "{'resume': 'JOHN DOE",
        "query": "Job Description Senior Full Stack Engineer",
        "score": 0.5951386,
    },
    {
        "text": "{'resume': 'Bruce Wayne ",
        "query": "Job Description Senior Full Stack Engineer",
        "score": 0.57700855,
    },
    {
        "text": "{'resume': 'Harvey Dent",
        "query": "Job Description Senior Full Stack Engineer",
        "score": 0.38489106,
    },
    {
        "text": "{'resume': 'Barry Allen",
        "query": "Job Description Front End Engineer",
        "score": 0.76813436,
    },
    {
        "text": "{'resume': 'Bruce Wayne'",
        "query": "Job Description Front End Engineer",
        "score": 0.60440844,
    },
    {
        "text": "{'resume': 'JOHN DOE",
        "query": "Job Description Front End Engineer",
        "score": 0.56080043,
    },
    {
        "text": "{'resume': 'Alfred Pennyworth",
        "query": "Job Description Front End Engineer",
        "score": 0.5395049,
    },
    {
        "text": "{'resume': 'Harvey Dent",
        "query": "Job Description Front End Engineer",
        "score": 0.3859515,
    },
    {
        "text": "{'resume': 'JOHN DOE",
        "query": "Job Description Java Developer",
        "score": 0.5449441,
    },
    {
        "text": "{'resume': 'Alfred Pennyworth",
        "query": "Job Description Java Developer",
        "score": 0.53476423,
    },
    {
        "text": "{'resume': 'Barry Allen",
        "query": "Job Description Java Developer",
        "score": 0.5313871,
    },
    {
        "text": "{'resume': 'Bruce Wayne ",
        "query": "Job Description Java Developer",
        "score": 0.44446343,
    },
    {
        "text": "{'resume': 'Harvey Dent",
        "query": "Job Description Java Developer",
        "score": 0.3616274,
    },
]

# Create a DataFrame
df = pd.DataFrame(data)

# Create different DataFrames based on the query and sort by score
df1 = df[df["query"] == "Job Description Product Manager"].sort_values(
    by="score", ascending=False
)
df2 = df[df["query"] == "Job Description Senior Full Stack Engineer"].sort_values(
    by="score", ascending=False
)
df3 = df[df["query"] == "Job Description Front End Engineer"].sort_values(
    by="score", ascending=False
)
df4 = df[df["query"] == "Job Description Java Developer"].sort_values(
    by="score", ascending=False
)


def plot_df(df, title):
    fig = px.bar(df, x="text", y=df["score"] * 100, title=title)
    st.plotly_chart(fig)


st.markdown("### Bar plots of scores based on similarity to Job Description.")

st.subheader(":blue[Legend]")
st.text("Alfred Pennyworth :  Product Manager")
st.text("Barry Allen :  Front End Developer")
st.text("Harvey Dent :  Machine Learning Engineer")
st.text("Bruce Wayne :  Fullstack Developer (MERN)")
st.text("John Doe :  Fullstack Developer (Java)")


plot_df(df1, "Job Description Product Manager 10+ Years of Exper")
plot_df(df2, "Job Description Senior Full Stack Engineer 5+ Year")
plot_df(df3, "Job Description Front End Engineer 2 Years of Expe")
plot_df(df4, "Job Description Java Developer 3 Years of Experien")


avs.add_vertical_space(3)

# Go back to top
st.markdown("[:arrow_up: Back to Top](#resume-matcher)")