import streamlit as st
import json
import pandas as pd
import plotly.express as px
import seaborn as sns
import matplotlib.pyplot as plt

# Function to load JSONL file into a DataFrame
def load_jsonl(file_path):
    data = []
    with open(file_path, 'r') as f:
        for line in f:
            data.append(json.loads(line))
    return pd.DataFrame(data)

# Function to filter DataFrame by keyword
def filter_by_keyword(df, keyword):
    return df[df.apply(lambda row: row.astype(str).str.contains(keyword).any(), axis=1)]

# Load the data
small_data = load_jsonl("usmle_16.2MB.jsonl")
large_data = load_jsonl("usmle_2.08MB.jsonl")

# Streamlit App
st.title("EDA with Plotly and Seaborn 📊")

# Dropdown for file selection
file_option = st.selectbox("Select file:", ["small_file.jsonl", "large_file.jsonl"])
st.write(f"You selected: {file_option}")

# Show filtered data grid
if file_option == "small_file.jsonl":
    data = small_data
else:
    data = large_data

# Text input for search keyword
search_keyword = st.text_input("Enter a keyword to filter data (e.g., Heart, Lung, Pain, Memory):")

# Button to trigger search
if st.button("Search"):
    filtered_data = filter_by_keyword(data, search_keyword)
    st.write(f"Filtered Dataset by '{search_keyword}'")
    st.dataframe(filtered_data)


# Plotly and Seaborn charts for EDA
if st.button("Generate Charts"):

    st.subheader("Plotly Charts 📈")

    # 1. Scatter Plot
    fig = px.scatter(data, x=data.columns[0], y=data.columns[1])
    st.plotly_chart(fig)

    # 2. Line Plot
    fig = px.line(data, x=data.columns[0], y=data.columns[1])
    st.plotly_chart(fig)

    # 3. Bar Plot
    fig = px.bar(data, x=data.columns[0], y=data.columns[1])
    st.plotly_chart(fig)

    # 4. Histogram
    fig = px.histogram(data, x=data.columns[0])
    st.plotly_chart(fig)

    # 5. Box Plot
    fig = px.box(data, x=data.columns[0], y=data.columns[1])
    st.plotly_chart(fig)

    st.subheader("Seaborn Charts 📊")

    # 6. Violin Plot
    fig, ax = plt.subplots()
    sns.violinplot(x=data.columns[0], y=data.columns[1], data=data)
    st.pyplot(fig)

    # 7. Swarm Plot
    fig, ax = plt.subplots()
    sns.swarmplot(x=data.columns[0], y=data.columns[1], data=data)
    st.pyplot(fig)

    # 8. Pair Plot
    fig = sns.pairplot(data)
    st.pyplot(fig)

    # 9. Heatmap
    fig, ax = plt.subplots()
    sns.heatmap(data.corr(), annot=True)
    st.pyplot(fig)

    # 10. Regplot (Regression Plot)
    fig, ax = plt.subplots()
    sns.regplot(x=data.columns[0], y=data.columns[1], data=data)
    st.pyplot(fig)