src/streamlit_app.py

import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
import plotly.io as pio
from plotly.subplots import make_subplots
import io

# Metadata
AUTHOR = "Eduardo Nacimiento García"
EMAIL = "enacimie@ull.edu.es"
LICENSE = "Apache 2.0"

# Page config
st.set_page_config(
    page_title="SimpleViz",
    page_icon="🎨",
    layout="wide",
    initial_sidebar_state="expanded",
)

# Title
st.title("🎨 SimpleViz")
st.markdown(f"**Author:** {AUTHOR} | **Email:** {EMAIL} | **License:** {LICENSE}")
st.write("""
Upload a CSV or use the demo dataset to create beautiful, interactive visualizations in seconds.
""")

# === GENERATE DEMO DATASET ===
@st.cache_data
def create_demo_data():
    np.random.seed(42)
    n = 500
    data = {
        "Age": np.random.normal(35, 12, n).astype(int),
        "Income": np.random.normal(45000, 15000, n),
        "Satisfaction": np.random.randint(1, 11, n),
        "City": np.random.choice(["Madrid", "Barcelona", "Valencia", "Seville"], n),
        "Gender": np.random.choice(["M", "F"], n, p=[0.6, 0.4]),
        "Purchase": np.random.choice([0, 1], n, p=[0.7, 0.3]),
        "Date": pd.date_range(start="2023-01-01", periods=n, freq="D")
    }
    df = pd.DataFrame(data)
    # Introduce some nulls for realism
    df.loc[np.random.choice(df.index, 15), "Income"] = np.nan
    return df

# === LOAD DATA ===
if st.button("🧪 Load Demo Dataset"):
    st.session_state['df'] = create_demo_data()
    st.success("✅ Demo dataset loaded!")

uploaded_file = st.file_uploader("📂 Upload your CSV file", type=["csv"])

if uploaded_file:
    df = pd.read_csv(uploaded_file)
    st.session_state['df'] = df
    st.success("✅ File uploaded successfully.")

if 'df' not in st.session_state:
    st.info("👆 Upload a CSV or click 'Load Demo Dataset' to begin.")
    st.stop()

df = st.session_state['df']

# Show data preview
with st.expander("🔍 Data Preview (first 10 rows)"):
    st.dataframe(df.head(10))

# Basic info
st.subheader("📌 Dataset Info")
col1, col2, col3 = st.columns(3)
col1.metric("Rows", df.shape[0])
col2.metric("Columns", df.shape[1])
col3.metric("Missing Values", df.isnull().sum().sum())

# === AUTO-VISUALIZATION RECOMMENDATIONS ===
st.header("✨ Smart Visualization Suggestions")

numeric_cols = df.select_dtypes(include=[np.number]).columns.tolist()
categorical_cols = df.select_dtypes(include=['object', 'category']).columns.tolist()
datetime_cols = df.select_dtypes(include=['datetime', 'datetime64']).columns.tolist()

if datetime_cols:
    date_col = datetime_cols[0]
else:
    date_col = None

# Suggest visualizations based on data types
suggestions = []

if len(numeric_cols) >= 2:
    suggestions.append({
        "name": "Scatter Plot",
        "description": "Visualize relationship between two numeric variables",
        "plot_type": "scatter",
        "x": numeric_cols[0],
        "y": numeric_cols[1] if len(numeric_cols) > 1 else numeric_cols[0]
    })

if len(numeric_cols) >= 1:
    suggestions.append({
        "name": "Histogram",
        "description": "Distribution of a numeric variable",
        "plot_type": "histogram",
        "x": numeric_cols[0]
    })

if len(categorical_cols) >= 1 and len(numeric_cols) >= 1:
    suggestions.append({
        "name": "Bar Plot (Mean)",
        "description": "Compare mean of numeric variable across categories",
        "plot_type": "bar",
        "x": categorical_cols[0],
        "y": numeric_cols[0]
    })

if len(categorical_cols) >= 2:
    suggestions.append({
        "name": "Stacked Bar Plot",
        "description": "Relationship between two categorical variables",
        "plot_type": "stacked_bar",
        "x": categorical_cols[0],
        "color": categorical_cols[1] if len(categorical_cols) > 1 else categorical_cols[0]
    })

if date_col and len(numeric_cols) >= 1:
    suggestions.append({
        "name": "Time Series Line Plot",
        "description": "Trend of numeric variable over time",
        "plot_type": "line",
        "x": date_col,
        "y": numeric_cols[0]
    })

if len(numeric_cols) >= 3:
    suggestions.append({
        "name": "Scatter Plot with Color",
        "description": "Scatter plot with third variable as color",
        "plot_type": "scatter_color",
        "x": numeric_cols[0],
        "y": numeric_cols[1],
        "color": numeric_cols[2]
    })

if len(numeric_cols) >= 2:
    suggestions.append({
        "name": "Box Plot",
        "description": "Distribution and outliers of numeric variable by category",
        "plot_type": "box",
        "x": categorical_cols[0] if categorical_cols else None,
        "y": numeric_cols[0]
    })

if len(numeric_cols) >= 2:
    suggestions.append({
        "name": "Correlation Heatmap",
        "description": "Correlation matrix of numeric variables",
        "plot_type": "heatmap",
        "cols": numeric_cols[:5]  # Limit to 5 columns for readability
    })

# Display suggestions
for i, suggestion in enumerate(suggestions):
    with st.expander(f"🎨 Suggestion {i+1}: {suggestion['name']}"):
        st.write(suggestion["description"])
        if st.button(f"Create {suggestion['name']}", key=f"sug_{i}"):
            st.session_state['selected_suggestion'] = suggestion

# === CUSTOM VISUALIZATION BUILDER ===
st.header("🛠️ Custom Visualization Builder")

plot_types = [
    "Scatter Plot",
    "Line Plot",
    "Bar Plot",
    "Histogram",
    "Box Plot",
    "Violin Plot",
    "Pie Chart",
    "Heatmap (Correlation)"
]

selected_plot = st.selectbox("Choose plot type:", plot_types)

fig = None

if selected_plot == "Scatter Plot":
    col1, col2 = st.columns(2)
    with col1:
        x_col = st.selectbox("X-axis:", numeric_cols)
    with col2:
        y_col = st.selectbox("Y-axis:", [col for col in numeric_cols if col != x_col] if len(numeric_cols) > 1 else numeric_cols)
    
    color_col = st.selectbox("Color by (optional):", [None] + categorical_cols + numeric_cols, key="scatter_color")
    size_col = st.selectbox("Size by (optional):", [None] + numeric_cols, key="scatter_size")
    
    title = st.text_input("Plot title:", f"{y_col} vs {x_col}")
    
    if st.button("Generate Scatter Plot"):
        fig = px.scatter(df, x=x_col, y=y_col, color=color_col, size=size_col, title=title)

elif selected_plot == "Line Plot":
    if not datetime_cols and not categorical_cols:
        st.warning("No suitable columns for line plot. Need datetime or categorical x-axis.")
    else:
        available_x = datetime_cols + categorical_cols if datetime_cols else categorical_cols
        col1, col2 = st.columns(2)
        with col1:
            x_col = st.selectbox("X-axis:", available_x)
        with col2:
            y_col = st.selectbox("Y-axis:", numeric_cols)
        
        color_col = st.selectbox("Color by (optional):", [None] + categorical_cols, key="line_color")
        title = st.text_input("Plot title:", f"{y_col} over {x_col}")
        
        if st.button("Generate Line Plot"):
            fig = px.line(df, x=x_col, y=y_col, color=color_col, title=title, markers=True)

elif selected_plot == "Bar Plot":
    if not categorical_cols:
        st.warning("No categorical columns available for bar plot.")
    else:
        col1, col2 = st.columns(2)
        with col1:
            x_col = st.selectbox("Category column:", categorical_cols)
        with col2:
            y_col = st.selectbox("Value column:", numeric_cols)
        
        agg_func = st.selectbox("Aggregation:", ["Mean", "Sum", "Count", "Median"])
        color_col = st.selectbox("Color by (optional):", [None] + categorical_cols, key="bar_color")
        title = st.text_input("Plot title:", f"{agg_func} of {y_col} by {x_col}")
        
        if st.button("Generate Bar Plot"):
            if agg_func == "Mean":
                fig = px.bar(df, x=x_col, y=y_col, color=color_col, title=title)
            elif agg_func == "Sum":
                fig_data = df.groupby(x_col)[y_col].sum().reset_index()
                fig = px.bar(fig_data, x=x_col, y=y_col, color=color_col, title=title)
            elif agg_func == "Count":
                fig = px.histogram(df, x=x_col, color=color_col, title=title)
            else:  # Median
                fig_data = df.groupby(x_col)[y_col].median().reset_index()
                fig = px.bar(fig_data, x=x_col, y=y_col, color=color_col, title=title)

elif selected_plot == "Histogram":
    if not numeric_cols:
        st.warning("No numeric columns available for histogram.")
    else:
        col1, col2 = st.columns(2)
        with col1:
            x_col = st.selectbox("Variable:", numeric_cols)
        with col2:
            nbins = st.slider("Number of bins:", min_value=5, max_value=100, value=30)
        
        color_col = st.selectbox("Color by (optional):", [None] + categorical_cols, key="hist_color")
        title = st.text_input("Plot title:", f"Distribution of {x_col}")
        
        if st.button("Generate Histogram"):
            fig = px.histogram(df, x=x_col, nbins=nbins, color=color_col, title=title, marginal="box")

elif selected_plot == "Box Plot":
    if not numeric_cols:
        st.warning("No numeric columns available for box plot.")
    else:
        col1, col2 = st.columns(2)
        with col1:
            y_col = st.selectbox("Numeric variable:", numeric_cols)
        with col2:
            x_col = st.selectbox("Group by (optional):", [None] + categorical_cols)
        
        title = st.text_input("Plot title:", f"Box plot of {y_col}" + (f" by {x_col}" if x_col else ""))
        
        if st.button("Generate Box Plot"):
            fig = px.box(df, x=x_col, y=y_col, title=title)

elif selected_plot == "Violin Plot":
    if not numeric_cols:
        st.warning("No numeric columns available for violin plot.")
    else:
        col1, col2 = st.columns(2)
        with col1:
            y_col = st.selectbox("Numeric variable:", numeric_cols)
        with col2:
            x_col = st.selectbox("Group by (optional):", [None] + categorical_cols)
        
        title = st.text_input("Plot title:", f"Violin plot of {y_col}" + (f" by {x_col}" if x_col else ""))
        
        if st.button("Generate Violin Plot"):
            fig = px.violin(df, x=x_col, y=y_col, box=True, points="outliers", title=title)

elif selected_plot == "Pie Chart":
    if not categorical_cols:
        st.warning("No categorical columns available for pie chart.")
    else:
        col_to_plot = st.selectbox("Category column:", categorical_cols)
        title = st.text_input("Plot title:", f"Distribution of {col_to_plot}")
        
        if st.button("Generate Pie Chart"):
            fig = px.pie(df, names=col_to_plot, title=title)

elif selected_plot == "Heatmap (Correlation)":
    if len(numeric_cols) < 2:
        st.warning("Need at least 2 numeric columns for correlation heatmap.")
    else:
        selected_cols = st.multiselect("Select columns for correlation:", numeric_cols, default=numeric_cols[:5] if len(numeric_cols) >= 5 else numeric_cols)
        
        if len(selected_cols) < 2:
            st.warning("Please select at least 2 columns.")
        else:
            title = st.text_input("Plot title:", "Correlation Heatmap")
            
            if st.button("Generate Heatmap"):
                corr_matrix = df[selected_cols].corr()
                fig = px.imshow(corr_matrix, 
                               text_auto=".2f", 
                               aspect="auto", 
                               title=title, 
                               color_continuous_scale='RdBu_r',
                               labels=dict(color="Correlation"))

# Display and download plot
if fig:
    st.plotly_chart(fig, use_container_width=True)
    
    # Download options
    st.subheader("💾 Download Plot")
    col1, col2 = st.columns(2)
    
    with col1:
        png_data = fig.to_image(format="png", width=1200, height=800, scale=2)
        st.download_button(
            label="Download as PNG",
            data=png_data,
            file_name="plot.png",
            mime="image/png"
        )
    
    with col2:
        html_data = fig.to_html(include_plotlyjs="cdn")
        st.download_button(
            label="Download as HTML",
            data=html_data,
            file_name="plot.html",
            mime="text/html"
        )

# === MULTI-PLOT COMPARISON ===
st.header("⚖️ Compare Multiple Plots")

num_plots = st.slider("Number of plots to compare:", min_value=1, max_value=4, value=2)

if num_plots > 1:
    fig_compare = make_subplots(
        rows=1, cols=num_plots,
        subplot_titles=[f"Plot {i+1}" for i in range(num_plots)],
        shared_yaxes=False
    )
    
    plot_success = True
    
    for i in range(num_plots):
        st.markdown(f"### Plot {i+1}")
        plot_type = st.selectbox(f"Plot type:", plot_types, key=f"compare_type_{i}")
        
        try:
            if plot_type == "Scatter Plot" and len(numeric_cols) >= 2:
                x_col = st.selectbox(f"X-axis:", numeric_cols, key=f"compare_x_{i}")
                y_col = st.selectbox(f"Y-axis:", [col for col in numeric_cols if col != x_col], key=f"compare_y_{i}")
                trace = go.Scatter(x=df[x_col], y=df[y_col], mode='markers', name=f"{y_col} vs {x_col}")
                fig_compare.add_trace(trace, row=1, col=i+1)
                
            elif plot_type == "Histogram" and len(numeric_cols) >= 1:
                x_col = st.selectbox(f"Variable:", numeric_cols, key=f"compare_hist_{i}")
                trace = go.Histogram(x=df[x_col], name=f"Distribution of {x_col}")
                fig_compare.add_trace(trace, row=1, col=i+1)
                
            elif plot_type == "Bar Plot" and len(categorical_cols) >= 1 and len(numeric_cols) >= 1:
                x_col = st.selectbox(f"Category:", categorical_cols, key=f"compare_bar_x_{i}")
                y_col = st.selectbox(f"Value:", numeric_cols, key=f"compare_bar_y_{i}")
                trace = go.Bar(x=df[x_col], y=df[y_col], name=f"{y_col} by {x_col}")
                fig_compare.add_trace(trace, row=1, col=i+1)
                
            else:
                st.warning(f"Plot {i+1}: Invalid combination for {plot_type}")
                plot_success = False
                
        except Exception as e:
            st.error(f"Error in Plot {i+1}: {e}")
            plot_success = False
    
    if plot_success and st.button("Generate Comparison Plot"):
        fig_compare.update_layout(height=600, showlegend=True, title_text="Comparison of Multiple Plots")
        st.plotly_chart(fig_compare, use_container_width=True)

# Footer
st.markdown("---")
st.caption(f"© {AUTHOR} | License {LICENSE} | Contact: {EMAIL}")