app.py

import gradio as gr
import pandas as pd
import numpy as np
import plotly.express as px
import io
import json
import warnings
import google.generativeai as genai
import os
from typing import List, Dict, Any, Tuple, Optional

# --- Configuration & Constants ---
warnings.filterwarnings('ignore')

CSS = """
/* --- Phoenix UI Professional Dark CSS --- */
body { --body-background-fill: #111827; }
.stat-card { border-radius: 12px !important; padding: 20px !important; background: #1f2937 !important; border: 1px solid #374151 !important; text-align: center; transition: all 0.3s ease; }
.stat-card:hover { transform: translateY(-5px); box-shadow: 0 10px 15px -3px rgba(0,0,0,0.1), 0 4px 6px -2px rgba(0,0,0,0.05); }
.stat-card-title { font-size: 16px; font-weight: 500; color: #9ca3af !important; margin-bottom: 8px; }
.stat-card-value { font-size: 32px; font-weight: 700; color: #f9fafb !important; }
.sidebar { background-color: #111827 !important; padding: 15px; border-right: 1px solid #374151 !important; min-height: 100vh; }
.sidebar .gr-button { width: 100%; text-align: left !important; background: none !important; border: none !important; box-shadow: none !important; color: #d1d5db !important; font-size: 16px !important; padding: 12px 10px !important; margin-bottom: 8px !important; border-radius: 8px !important; transition: background-color 0.2s ease; }
.sidebar .gr-button:hover { background-color: #374151 !important; }
.sidebar .gr-button.selected { background-color: #4f46e5 !important; font-weight: 600 !important; color: white !important; }
.explanation-block { background-color: #1e3a8a !important; border-left: 4px solid #3b82f6 !important; padding: 12px; color: #e5e7eb !important; border-radius: 4px; }
"""

class DataExplorerApp:
    """A professional-grade, AI-powered data exploration application."""

    def __init__(self):
        """Initializes the application and builds the UI and event listeners."""
        self.demo = self._build_ui()

    def _build_ui(self) -> gr.Blocks:
        """
        Defines all UI components, arranges them in the layout,
        and registers all event handlers within the same Blocks context.
        """
        with gr.Blocks(theme=gr.themes.Glass(primary_hue="indigo", secondary_hue="blue"), css=CSS, title="Professional AI Data Explorer") as demo:
            # --- State Management ---
            state_var = gr.State({})

            # --- Component Definition ---
            # Sidebar
            cockpit_btn = gr.Button("📊 Data Cockpit", elem_classes="selected", elem_id="cockpit")
            deep_dive_btn = gr.Button("🔍 Deep Dive Builder", elem_id="deep_dive")
            copilot_btn = gr.Button("🤖 Chief Data Scientist", elem_id="co-pilot")
            file_input = gr.File(label="📁 Upload CSV File", file_types=[".csv"])
            status_output = gr.Markdown("Status: Awaiting data...")
            api_key_input = gr.Textbox(label="🔑 Gemini API Key", type="password", placeholder="Enter key to enable AI...")
            suggestion_btn = gr.Button("Get Smart Suggestions", variant="secondary", interactive=False)
            
            # Cockpit
            rows_stat, cols_stat = gr.Textbox("0", interactive=False, elem_classes="stat-card-value"), gr.Textbox("0", interactive=False, elem_classes="stat-card-value")
            quality_stat, time_cols_stat = gr.Textbox("0%", interactive=False, elem_classes="stat-card-value"), gr.Textbox("0", interactive=False, elem_classes="stat-card-value")
            suggestion_buttons = [gr.Button(visible=False) for _ in range(5)]
            
            # Deep Dive
            plot_type_dd = gr.Dropdown(['histogram', 'bar', 'scatter', 'box'], label="Plot Type", value='histogram')
            x_col_dd = gr.Dropdown([], label="X-Axis / Column", interactive=False)
            y_col_dd = gr.Dropdown([], label="Y-Axis (for Scatter/Box)", visible=False, interactive=False)
            add_plot_btn = gr.Button("Add to Dashboard", variant="primary", interactive=False)
            clear_plots_btn = gr.Button("Clear Dashboard")
            dashboard_gallery = gr.Gallery(label="📊 Your Custom Dashboard", height="auto", columns=2, preview=True)

            # Co-pilot
            chatbot = gr.Chatbot(height=500, label="Conversation", show_copy_button=True)
            copilot_explanation = gr.Markdown(visible=False, elem_classes="explanation-block")
            copilot_code = gr.Code(language="python", visible=False, label="Executed Code")
            copilot_plot = gr.Plot(visible=False, label="Generated Visualization")
            copilot_table = gr.Dataframe(visible=False, label="Generated Table", wrap=True)
            chat_input = gr.Textbox(label="Your Question", placeholder="e.g., 'What is the relationship between age and salary?'", scale=4)
            chat_submit_btn = gr.Button("Ask AI", variant="primary")
            
            # --- Layout Arrangement ---
            with gr.Row():
                with gr.Column(scale=1, elem_classes="sidebar"):
                    gr.Markdown("## 🚀 AI Explorer Pro"); cockpit_btn; deep_dive_btn; copilot_btn; gr.Markdown("---")
                    file_input; status_output; gr.Markdown("---"); api_key_input; suggestion_btn
                with gr.Column(scale=4):
                    welcome_page = gr.Column(visible=True)
                    with welcome_page:
                        gr.Markdown("# Welcome to the AI Data Explorer Pro\n> Please **upload a CSV file** and **enter your Gemini API key** to begin your analysis.")
                        gr.Image("workflow.png", show_label=False, show_download_button=False, container=False)
                    
                    cockpit_page = gr.Column(visible=False)
                    with cockpit_page:
                        gr.Markdown("## 📊 Data Cockpit: At-a-Glance Overview")
                        with gr.Row():
                            with gr.Column(elem_classes="stat-card"): gr.Markdown("<div class='stat-card-title'>Rows</div>"); rows_stat
                            with gr.Column(elem_classes="stat-card"): gr.Markdown("<div class='stat-card-title'>Columns</div>"); cols_stat
                            with gr.Column(elem_classes="stat-card"): gr.Markdown("<div class='stat-card-title'>Data Quality</div>"); quality_stat
                            with gr.Column(elem_classes="stat-card"): gr.Markdown("<div class='stat-card-title'>Date/Time Cols</div>"); time_cols_stat
                        with gr.Accordion(label="✨ AI Smart Suggestions", open=True): [btn for btn in suggestion_buttons]

                    deep_dive_page = gr.Column(visible=False)
                    with deep_dive_page:
                        gr.Markdown("## 🔍 Deep Dive: Manual Dashboard Builder"); gr.Markdown("Construct your own visualizations to investigate specific relationships.")
                        with gr.Row(): plot_type_dd; x_col_dd; y_col_dd
                        with gr.Row(): add_plot_btn; clear_plots_btn
                        dashboard_gallery
                    
                    copilot_page = gr.Column(visible=False)
                    with copilot_page:
                        gr.Markdown("## 🤖 Chief Data Scientist: Your AI Partner"); chatbot
                        with gr.Accordion("AI's Detailed Response", open=True): copilot_explanation; copilot_code; copilot_plot; copilot_table
                        with gr.Row(): chat_input; chat_submit_btn
            
            # --- Event Handlers Registration (inside the 'with' block) ---
            pages = [cockpit_page, deep_dive_page, copilot_page]
            nav_buttons = [cockpit_btn, deep_dive_btn, copilot_btn]
            
            for i, btn in enumerate(nav_buttons):
                btn.click(
                    lambda id=btn.elem_id: self._switch_page(id), outputs=pages
                ).then(
                    lambda i=i: [gr.update(elem_classes="selected" if j==i else "") for j in range(len(nav_buttons))], outputs=nav_buttons
                )

            file_input.upload(self.load_and_process_file, inputs=[file_input], outputs=[
                state_var, status_output, welcome_page, cockpit_page,
                rows_stat, cols_stat, quality_stat, time_cols_stat,
                x_col_dd, y_col_dd, add_plot_btn
            ]).then(lambda: self._switch_page("cockpit"), outputs=pages) \
            .then(lambda: [gr.update(elem_classes="selected"), gr.update(elem_classes=""), gr.update(elem_classes="")], outputs=nav_buttons)

            api_key_input.change(lambda x: gr.update(interactive=bool(x)), inputs=[api_key_input], outputs=[suggestion_btn])

            plot_type_dd.change(self._update_plot_controls, inputs=[plot_type_dd], outputs=[y_col_dd])
            add_plot_btn.click(self.add_plot_to_dashboard, inputs=[state_var, x_col_dd, y_col_dd, plot_type_dd], outputs=[state_var, dashboard_gallery])
            clear_plots_btn.click(self.clear_dashboard, inputs=[state_var], outputs=[state_var, dashboard_gallery])

            suggestion_btn.click(self.get_ai_suggestions, inputs=[state_var, api_key_input], outputs=suggestion_buttons)
            for btn in suggestion_buttons:
                btn.click(self.handle_suggestion_click, inputs=[btn], outputs=[cockpit_page, deep_dive_page, copilot_page, chat_input]) \
                .then(lambda: self._switch_page("co-pilot"), outputs=pages) \
                .then(lambda: (gr.update(elem_classes=""), gr.update(elem_classes=""), gr.update(elem_classes="selected")), outputs=nav_buttons)
            
            chat_submit_btn.click(self.respond_to_chat, [state_var, api_key_input, chat_input, chatbot], [chatbot, copilot_explanation, copilot_code, copilot_plot, copilot_table]).then(lambda: "", outputs=[chat_input])
            chat_input.submit(self.respond_to_chat, [state_var, api_key_input, chat_input, chatbot], [chatbot, copilot_explanation, copilot_code, copilot_plot, copilot_table]).then(lambda: "", outputs=[chat_input])
        
        return demo

    def launch(self):
        """Launches the Gradio application."""
        self.demo.launch(debug=True)

    # --- Backend Logic Methods ---
    def _switch_page(self, page_id: str) -> Tuple[gr.update, ...]:
        return gr.update(visible=page_id=="cockpit"), gr.update(visible=page_id=="deep_dive"), gr.update(visible=page_id=="co-pilot")
    
    def _update_plot_controls(self, plot_type: str) -> gr.update:
        return gr.update(visible=plot_type in ['scatter', 'box'])

    def load_and_process_file(self, file_obj: Any) -> Tuple[Any, ...]:
        try:
            df = pd.read_csv(file_obj.name, low_memory=False)
            for col in df.select_dtypes(include=['object']).columns:
                try: df[col] = pd.to_datetime(df[col], errors='raise')
                except (ValueError, TypeError): continue
            
            metadata = self._extract_dataset_metadata(df)
            state = {'df': df, 'metadata': metadata, 'dashboard_plots': []}
            status_msg = f"✅ **{os.path.basename(file_obj.name)}** loaded."
            rows, cols, quality = metadata['shape'][0], metadata['shape'][1], metadata['data_quality']
            
            return (state, status_msg, gr.update(visible=False), gr.update(visible=True),
                    f"{rows:,}", f"{cols}", f"{quality}%", f"{len(metadata['datetime_cols'])}",
                    gr.update(choices=metadata['columns'], interactive=True), gr.update(choices=metadata['columns'], interactive=True), gr.update(interactive=True))
        except Exception as e:
            gr.Error(f"File Load Error: {e}"); return {}, f"❌ Error: {e}", gr.update(visible=True), gr.update(visible=False), "0", "0", "0%", "0", gr.update(choices=[], interactive=False), gr.update(choices=[], interactive=False), gr.update(interactive=False)

    def _extract_dataset_metadata(self, df: pd.DataFrame) -> Dict[str, Any]:
        rows, cols = df.shape
        quality = round((df.notna().sum().sum() / (rows * cols)) * 100, 1) if rows * cols > 0 else 0
        return {'shape': (rows, cols), 'columns': df.columns.tolist(),
                '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=['datetime64', 'datetime64[ns]']).columns.tolist(),
                'dtypes_head': df.head().to_string()}

    def add_plot_to_dashboard(self, state: Dict, x_col: str, y_col: Optional[str], plot_type: str) -> Tuple[Dict, List]:
        if not x_col:
            gr.Warning("Please select at least an X-axis column."); return state, state.get('dashboard_plots', [])
        df = state['df']
        title = f"{plot_type.capitalize()}: {y_col} by {x_col}" if y_col and plot_type in ['box', 'scatter'] else f"Distribution of {x_col}"
        try:
            if plot_type == 'histogram': fig = px.histogram(df, x=x_col, title=title)
            elif plot_type == 'box': fig = px.box(df, x=x_col, y=y_col, title=title)
            elif plot_type == 'scatter': fig = px.scatter(df, x=x_col, y=y_col, title=title, trendline="ols", trendline_color_override="red")
            elif plot_type == 'bar':
                counts = df[x_col].value_counts().nlargest(20)
                fig = px.bar(counts, x=counts.index, y=counts.values, title=f"Top 20 Categories for {x_col}", labels={'index': x_col, 'y': 'Count'})
            if fig:
                fig.update_layout(template="plotly_dark"); state['dashboard_plots'].append(fig); gr.Info(f"Added '{title}' to the dashboard.")
            return state, state['dashboard_plots']
        except Exception as e:
            gr.Error(f"Plotting Error: {e}"); return state, state.get('dashboard_plots', [])

    def clear_dashboard(self, state: Dict) -> Tuple[Dict, List]:
        state['dashboard_plots'] = []; gr.Info("Dashboard cleared."); return state, []

    def get_ai_suggestions(self, state: Dict, api_key: str) -> List[gr.update]:
        if not api_key: gr.Warning("API Key is required for suggestions."); return [gr.update(visible=False)]*5
        if not state: gr.Warning("Please load data first."); return [gr.update(visible=False)]*5
        metadata = state['metadata']
        prompt = f"""Based on this metadata (columns: {metadata['columns']}), generate 4 impactful analytical questions. Return ONLY a JSON list of strings."""
        try:
            genai.configure(api_key=api_key)
            suggestions = json.loads(genai.GenerativeModel('gemini-1.5-flash').generate_content(prompt).text)
            return [gr.Button(s, visible=True) for s in suggestions] + [gr.Button(visible=False)] * (5 - len(suggestions))
        except Exception as e: gr.Error(f"AI Suggestion Error: {e}"); return [gr.update(visible=False)]*5

    def handle_suggestion_click(self, question: str) -> Tuple[gr.update, ...]:
        return gr.update(visible=False), gr.update(visible=False), gr.update(visible=True), question

    def respond_to_chat(self, state: Dict, api_key: str, user_message: str, history: List) -> Any:
        if not api_key or not state:
            msg = "I need a Gemini API key and a dataset to work."; history.append((user_message, msg)); return history, *[gr.update(visible=False)]*4
        
        history.append((user_message, "Thinking... 🤔")); yield history, *[gr.update(visible=False)]*4
        
        metadata, prompt = state['metadata'], f"""You are 'Chief Data Scientist', an expert AI analyst...
        **Instructions:**
        1. **Analyze:** Understand the user's intent.
        2. **Method:** Choose the best method (table, value, or plot). Infer the best plot type.
        3. **Plan:** Briefly explain your plan.
        4. **Code:** Write Python code. Use `fig` for plots (with `template='plotly_dark'`) and `result_df` for tables.
        5. **Insight:** Provide a one-sentence business insight.
        6. **Respond ONLY with a single JSON object with keys: "plan", "code", "insight".**
        **Metadata:** {metadata['dtypes_head']}
        **User Question:** "{user_message}"
        """
        try:
            genai.configure(api_key=api_key)
            response_json = json.loads(genai.GenerativeModel('gemini-1.5-flash').generate_content(prompt).text.strip().replace("```json", "").replace("```", ""))
            plan, code, insight = response_json.get("plan"), response_json.get("code"), response_json.get("insight")
            stdout, fig, df_result, error = self._safe_exec(code, {'df': state['df'], 'px': px, 'pd': pd})
            
            history[-1] = (user_message, f"**Plan:** {plan}")
            explanation = f"**Insight:** {insight}"
            if stdout: explanation += f"\n\n**Console Output:**\n```\n{stdout}\n```"
            if error: gr.Error(f"AI Code Execution Failed: {error}")
            
            yield (history, gr.update(visible=bool(explanation), value=explanation), gr.update(visible=bool(code), value=code),
                   gr.update(visible=bool(fig), value=fig), gr.update(visible=bool(df_result is not None), value=df_result))
        except Exception as e:
            history[-1] = (user_message, f"I encountered an error. Please rephrase your question. (Error: {e})")
            yield history, *[gr.update(visible=False)]*4
            
    def _safe_exec(self, code_string: str, local_vars: Dict) -> Tuple[Any, ...]:
        output_buffer = io.StringIO()
        try:
            with redirect_stdout(output_buffer): exec(code_string, globals(), local_vars)
            return output_buffer.getvalue(), local_vars.get('fig'), local_vars.get('result_df'), None
        except Exception as e: return None, None, None, str(e)

if __name__ == "__main__":
    app = DataExplorerApp()
    app.launch()