import gradio as gr
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import io

def create_dashboard(file, chart_type, x_column, y_column, color_column):
    """Create dashboard based on uploaded dataset"""
    
    if file is None:
        return None, "Please upload a CSV file"
    
    try:
        # Read the uploaded file
        if file.name.endswith('.csv'):
            df = pd.read_csv(file.name)
        elif file.name.endswith('.xlsx'):
            df = pd.read_excel(file.name)
        else:
            return None, "Please upload a CSV or Excel file"
        
        # Data info
        info = f"""
        Dataset Info:
        - Shape: {df.shape[0]} rows × {df.shape[1]} columns
        - Columns: {', '.join(df.columns.tolist())}
        - Memory usage: {df.memory_usage().sum()} bytes
        """
        
        # Validate columns exist
        if x_column not in df.columns or y_column not in df.columns:
            return None, f"Columns not found. Available: {list(df.columns)}"
        
        # Create the chart based on type
        if chart_type == "Bar Chart":
            fig = px.bar(df, x=x_column, y=y_column, 
                        color=color_column if color_column in df.columns else None,
                        title=f"{chart_type}: {y_column} by {x_column}")
        
        elif chart_type == "Line Chart":
            fig = px.line(df, x=x_column, y=y_column,
                         color=color_column if color_column in df.columns else None,
                         title=f"{chart_type}: {y_column} vs {x_column}")
        
        elif chart_type == "Scatter Plot":
            fig = px.scatter(df, x=x_column, y=y_column,
                           color=color_column if color_column in df.columns else None,
                           title=f"{chart_type}: {y_column} vs {x_column}")
        
        elif chart_type == "Histogram":
            fig = px.histogram(df, x=x_column,
                             color=color_column if color_column in df.columns else None,
                             title=f"{chart_type}: Distribution of {x_column}")
        
        elif chart_type == "Box Plot":
            fig = px.box(df, x=x_column, y=y_column,
                        color=color_column if color_column in df.columns else None,
                        title=f"{chart_type}: {y_column} by {x_column}")
        
        elif chart_type == "Multi-Chart Dashboard":
            # Create a comprehensive dashboard
            fig = make_subplots(
                rows=2, cols=2,
                subplot_titles=(f'{y_column} by {x_column}', 
                              f'Distribution of {x_column}',
                              f'Correlation Plot', 
                              'Summary Statistics'),
                specs=[[{"type": "bar"}, {"type": "histogram"}],
                       [{"type": "scatter"}, {"type": "table"}]]
            )
            
            # Bar chart
            fig.add_trace(
                go.Bar(x=df[x_column], y=df[y_column], name="Bar"),
                row=1, col=1
            )
            
            # Histogram
            fig.add_trace(
                go.Histogram(x=df[x_column], name="Distribution"),
                row=1, col=2
            )
            
            # Scatter plot if we have numeric columns
            numeric_cols = df.select_dtypes(include=['number']).columns
            if len(numeric_cols) >= 2:
                fig.add_trace(
                    go.Scatter(x=df[numeric_cols[0]], y=df[numeric_cols[1]], 
                             mode='markers', name="Correlation"),
                    row=2, col=1
                )
            
            # Summary table
            summary_df = df.describe()
            fig.add_trace(
                go.Table(
                    header=dict(values=['Statistic'] + list(summary_df.columns)),
                    cells=dict(values=[summary_df.index] + 
                                     [summary_df[col] for col in summary_df.columns])
                ),
                row=2, col=2
            )
            
            fig.update_layout(height=800, title="Comprehensive Dashboard")
        
        else:
            return None, "Chart type not supported"
        
        # Update layout
        fig.update_layout(
            template="plotly_white",
            width=800,
            height=600
        )
        
        return fig, info
        
    except Exception as e:
        return None, f"Error processing file: {str(e)}"

def get_columns(file):
    """Extract column names from uploaded file"""
    if file is None:
        return gr.Dropdown(choices=[])
    
    try:
        if file.name.endswith('.csv'):
            df = pd.read_csv(file.name)
        elif file.name.endswith('.xlsx'):
            df = pd.read_excel(file.name)
        else:
            return gr.Dropdown(choices=[])
        
        columns = df.columns.tolist()
        return (gr.Dropdown(choices=columns, value=columns[0] if columns else None),
                gr.Dropdown(choices=columns, value=columns[1] if len(columns) > 1 else columns[0]),
                gr.Dropdown(choices=['None'] + columns, value='None'))
    except:
        return (gr.Dropdown(choices=[]), gr.Dropdown(choices=[]), gr.Dropdown(choices=[]))

# Create Gradio interface
with gr.Blocks(title="Dynamic Dashboard Creator") as demo:
    gr.Markdown("# 📊 Dynamic Dashboard Creator")
    gr.Markdown("Upload any CSV/Excel file and create interactive dashboards!")
    
    with gr.Row():
        with gr.Column(scale=1):
            file_upload = gr.File(
                label="Upload Dataset (CSV or Excel)",
                file_types=[".csv", ".xlsx"]
            )
            
            chart_type = gr.Dropdown(
                choices=["Bar Chart", "Line Chart", "Scatter Plot", 
                        "Histogram", "Box Plot", "Multi-Chart Dashboard"],
                label="Chart Type",
                value="Bar Chart"
            )
            
            x_column = gr.Dropdown(
                label="X-axis Column",
                choices=[]
            )
            
            y_column = gr.Dropdown(
                label="Y-axis Column", 
                choices=[]
            )
            
            color_column = gr.Dropdown(
                label="Color Column (Optional)",
                choices=[]
            )
            
            create_btn = gr.Button("Create Dashboard", variant="primary")
        
        with gr.Column(scale=2):
            plot_output = gr.Plot(label="Dashboard")
            info_output = gr.Textbox(label="Dataset Info", lines=5)
    
    # Update column dropdowns when file is uploaded
    file_upload.change(
        fn=get_columns,
        inputs=[file_upload],
        outputs=[x_column, y_column, color_column]
    )
    
    # Create dashboard when button is clicked
    create_btn.click(
        fn=create_dashboard,
        inputs=[file_upload, chart_type, x_column, y_column, color_column],
        outputs=[plot_output, info_output]
    )

if __name__ == "__main__":
    demo.launch(share=True)