import altair as alt
import numpy as np
import pandas as pd
import streamlit as st
import streamlit.components.v1 as components  # For embedding custom HTML
from generate_knowledge_graph import generate_knowledge_graph

# Set up Streamlit page configuration
st.set_page_config(
    page_icon=None, 
    layout="wide",  # Use wide layout for better graph display
    initial_sidebar_state="auto", 
    menu_items=None
)

# Set the title of the app
st.title("Knowledge Graph From Text")

# Sidebar section for user input method
st.sidebar.title("Input document")
input_method = st.sidebar.radio(
    "Choose an input method:",
    ["Upload txt", "Input text"],  # Options for uploading a file or manually inputting text
)

# Case 1: User chooses to upload a .txt file
if input_method == "Upload txt":
    # File uploader widget in the sidebar
    uploaded_file = st.sidebar.file_uploader(label="Upload file", type=["txt"])
    
    if uploaded_file is not None:
        # Read the uploaded file content and decode it as UTF-8 text
        text = uploaded_file.read().decode("utf-8")
 
        # Button to generate the knowledge graph
        if st.sidebar.button("Generate Knowledge Graph"):
            with st.spinner("Generating knowledge graph..."):
                # Call the function to generate the graph from the text
                net = generate_knowledge_graph(text)
                st.success("Knowledge graph generated successfully!")
                print("hello world")
                # Save the graph to an HTML file
                output_file = "knowledge_graph.html"
                net.save_graph(output_file) 

                # Open the HTML file and display it within the Streamlit app
                HtmlFile = open(output_file, 'r', encoding='utf-8')
                components.html(HtmlFile.read(), height=1000)

# Case 2: User chooses to directly input text
else:
    # Text area for manual input
    text = st.sidebar.text_area("Input text", height=300)

    if text:  # Check if the text area is not empty
        if st.sidebar.button("Generate Knowledge Graph"):
            with st.spinner("Generating knowledge graph..."):
                # Call the function to generate the graph from the input text
                net = generate_knowledge_graph(text)
                st.success("Knowledge graph generated successfully!")
                
                # Save the graph to an HTML file
                output_file = "knowledge_graph.html"
                net.save_graph(output_file) 

                # Open the HTML file and display it within the Streamlit app
                HtmlFile = open(output_file, 'r', encoding='utf-8')
                components.html(HtmlFile.read(), height=1000)
"""
# Welcome to Streamlit!

Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
forums](https://discuss.streamlit.io).

In the meantime, below is an example of what you can do with just a few lines of code:
"""

num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
num_turns = st.slider("Number of turns in spiral", 1, 300, 31)

indices = np.linspace(0, 1, num_points)
theta = 2 * np.pi * num_turns * indices
radius = indices

x = radius * np.cos(theta)
y = radius * np.sin(theta)

df = pd.DataFrame({
    "x": x,
    "y": y,
    "idx": indices,
    "rand": np.random.randn(num_points),
})

st.altair_chart(alt.Chart(df, height=700, width=700)
    .mark_point(filled=True)
    .encode(
        x=alt.X("x", axis=None),
        y=alt.Y("y", axis=None),
        color=alt.Color("idx", legend=None, scale=alt.Scale()),
        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
    ))