Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,399 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 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 = "[email protected]"
+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)
 
-"""
-# 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])),
-    ))
+# Footer
+st.markdown("---")
+st.caption(f"© {AUTHOR} | License {LICENSE} | Contact: {EMAIL}")