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| import gradio as gr | |
| import pandas as pd | |
| import numpy as np | |
| import plotly.express as px | |
| import plotly.graph_objects as go | |
| from plotly.subplots import make_subplots | |
| import io | |
| import json | |
| import warnings | |
| import google.generativeai as genai | |
| import os | |
| from contextlib import redirect_stdout | |
| # --- Configuration --- | |
| warnings.filterwarnings('ignore') | |
| CSS = """ | |
| /* --- Phoenix UI Custom CSS --- */ | |
| /* Stat Card Styling */ | |
| .stat-card { | |
| border-radius: 12px !important; | |
| padding: 20px !important; | |
| background: #f7fafc; /* light gray background */ | |
| border: 1px solid #e2e8f0; | |
| box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1), 0 2px 4px -1px rgba(0, 0, 0, 0.06); | |
| text-align: center; | |
| } | |
| .stat-card-title { font-size: 16px; font-weight: 500; color: #4a5568; margin-bottom: 8px; } | |
| .stat-card-value { font-size: 32px; font-weight: 700; color: #2d3748; } | |
| /* General Layout & Feel */ | |
| .gradio-container { font-family: 'Inter', sans-serif; } | |
| .gr-button { box-shadow: 0 1px 2px 0 rgba(0,0,0,0.05); } | |
| /* Sidebar Styling */ | |
| .sidebar { | |
| background-color: #f9fafb; | |
| padding: 15px; | |
| border-right: 1px solid #e5e7eb; | |
| min-height: 100vh; | |
| } | |
| .sidebar .gr-button { | |
| width: 100%; | |
| text-align: left !important; | |
| background: none !important; | |
| border: none !important; | |
| box-shadow: none !important; | |
| color: #374151 !important; | |
| font-size: 16px !important; | |
| padding: 12px 10px !important; | |
| margin-bottom: 8px !important; | |
| border-radius: 8px !important; | |
| } | |
| .sidebar .gr-button:hover { background-color: #e5e7eb !important; } | |
| .sidebar .gr-button.selected { background-color: #d1d5db !important; font-weight: 600 !important; } | |
| /* AI Co-pilot Styling */ | |
| .code-block { border: 1px solid #e5e7eb; border-radius: 8px; } | |
| .explanation-block { background-color: #f0f9ff; border-left: 4px solid #3b82f6; padding: 12px; } | |
| """ | |
| # --- Helper Functions --- | |
| def safe_exec(code_string: str, local_vars: dict): | |
| """Safely execute a string of Python code and capture its output.""" | |
| output_buffer = io.StringIO() | |
| try: | |
| with redirect_stdout(output_buffer): | |
| exec(code_string, globals(), local_vars) | |
| stdout = output_buffer.getvalue() | |
| fig = local_vars.get('fig') | |
| result_df = local_vars.get('result_df') | |
| return stdout, fig, result_df, None | |
| except Exception as e: | |
| return None, None, None, f"Execution Error: {str(e)}" | |
| # --- Core Data Processing & State Management --- | |
| def load_and_process_file(file_obj, state_dict): | |
| """Loads a CSV, processes it, and updates the entire UI state.""" | |
| if file_obj is None: | |
| return state_dict, "Please upload a file.", *[gr.update(visible=False)] * 3 | |
| 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 = extract_dataset_metadata(df) | |
| state_dict = { | |
| 'df': df, | |
| 'metadata': metadata, | |
| 'filename': os.path.basename(file_obj.name), | |
| 'dashboard_plots': [] | |
| } | |
| status_msg = f"✅ **{state_dict['filename']}** loaded successfully." | |
| # Update UI elements with new data context | |
| cockpit_update = gr.update(visible=True) | |
| deep_dive_update = gr.update(visible=False) | |
| copilot_update = gr.update(visible=False) | |
| welcome_update = gr.update(visible=False) | |
| # Stat cards | |
| rows, cols = metadata['shape'] | |
| quality = metadata['data_quality'] | |
| return (state_dict, status_msg, welcome_update, cockpit_update, deep_dive_update, copilot_update, | |
| gr.update(value=f"{rows:,}"), gr.update(value=cols), gr.update(value=f"{quality}%"), | |
| gr.update(value=f"{len(metadata['datetime_cols'])}"), | |
| gr.update(choices=metadata['columns']), gr.update(choices=metadata['columns']), gr.update(choices=metadata['columns'])) | |
| except Exception as e: | |
| return state_dict, f"❌ **Error:** {e}", *[gr.update()] * 10 | |
| def extract_dataset_metadata(df: pd.DataFrame): | |
| rows, cols = df.shape | |
| 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() | |
| data_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': numeric_cols, 'categorical_cols': categorical_cols, | |
| 'datetime_cols': datetime_cols, 'dtypes': df.dtypes.to_string(), | |
| 'head': df.head().to_string(), 'data_quality': data_quality | |
| } | |
| # --- Page Navigation --- | |
| def switch_page(page_name): | |
| """Controls visibility of main content pages.""" | |
| if page_name == "cockpit": | |
| return gr.update(visible=True), gr.update(visible=False), gr.update(visible=False) | |
| elif page_name == "deep_dive": | |
| return gr.update(visible=False), gr.update(visible=True), gr.update(visible=False) | |
| elif page_name == "co-pilot": | |
| return gr.update(visible=False), gr.update(visible=False), gr.update(visible=True) | |
| return gr.update(visible=True), gr.update(visible=False), gr.update(visible=False) | |
| # --- Page 1: Data Cockpit --- | |
| def get_ai_suggestions(state_dict, api_key): | |
| """Generates proactive analytical suggestions from the AI.""" | |
| if not api_key: return "Enter your Gemini API key to get suggestions.", gr.update(visible=False) | |
| if not state_dict: return "Upload data first.", gr.update(visible=False) | |
| metadata = state_dict['metadata'] | |
| prompt = f""" | |
| Based on the following dataset metadata, generate 3 to 5 specific, actionable, and interesting analytical questions a user might want to ask. Frame them as questions. | |
| - **Columns:** {', '.join(metadata['columns'])} | |
| - **Numeric:** {', '.join(metadata['numeric_cols'])} | |
| - **Categorical:** {', '.join(metadata['categorical_cols'])} | |
| - **Datetime:** {', '.join(metadata['datetime_cols'])} | |
| Return ONLY a JSON list of strings. Example: ["What is the trend of sales over time?", "Which category has the highest average price?"] | |
| """ | |
| try: | |
| genai.configure(api_key=api_key) | |
| model = genai.GenerativeModel('gemini-1.5-flash') | |
| response = model.generate_content(prompt) | |
| suggestions = json.loads(response.text) | |
| # Create a button for each suggestion | |
| buttons = [gr.Button(s, variant="secondary") for s in suggestions] | |
| return gr.update(visible=False), gr.Accordion(label="✨ AI Smart Suggestions", open=True, children=buttons) | |
| except Exception as e: | |
| return f"Could not generate suggestions: {e}", gr.update(visible=False) | |
| # --- Page 2: Deep Dive Dashboard --- | |
| def add_plot_to_dashboard(state_dict, x_col, y_col, plot_type): | |
| """Generates a plot and adds it to the state-managed dashboard.""" | |
| if not x_col: return state_dict, gr.update() | |
| df = state_dict['df'] | |
| title = f"{plot_type.capitalize()}: {y_col} by {x_col}" if y_col else f"Distribution of {x_col}" | |
| fig = None | |
| 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") | |
| 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}") | |
| fig.update_xaxes(title=x_col) | |
| if fig: | |
| state_dict['dashboard_plots'].append(fig) | |
| # Rebuild the accordion with all plots | |
| accordion_children = [gr.Plot(fig, visible=True) for fig in state_dict['dashboard_plots']] | |
| return state_dict, gr.Accordion(label="Your Dashboard Plots", children=accordion_children, open=True) | |
| except Exception as e: | |
| gr.Warning(f"Plotting Error: {e}") | |
| return state_dict, gr.update() | |
| def clear_dashboard(state_dict): | |
| """Clears all plots from the dashboard.""" | |
| state_dict['dashboard_plots'] = [] | |
| return state_dict, gr.Accordion(label="Your Dashboard Plots", children=[]) | |
| # --- Page 3: AI Co-pilot --- | |
| def respond_to_chat(user_message, history, state_dict, api_key): | |
| """Handles the advanced chat interaction with the AI Co-pilot.""" | |
| if not api_key: | |
| history.append((user_message, "I need a Gemini API key to function. Please provide it in the sidebar.")) | |
| return history, *[gr.update(visible=False)] * 4 | |
| if not state_dict: | |
| history.append((user_message, "Please upload a dataset first.")) | |
| return history, *[gr.update(visible=False)] * 4 | |
| history.append((user_message, None)) | |
| metadata = state_dict['metadata'] | |
| prompt = f""" | |
| You are 'Phoenix Co-pilot', an expert AI data analyst. Your goal is to help a user analyze a pandas DataFrame named `df`. | |
| **Instructions:** | |
| 1. Carefully understand the user's question. | |
| 2. Formulate a plan (thought process). | |
| 3. Write Python code to execute that plan. | |
| 4. The code can use pandas (pd), numpy (np), and plotly.express (px). | |
| 5. **For plots, assign the figure to a variable `fig` (e.g., `fig = px.histogram(...)`).** | |
| 6. **For table-like results, assign the final DataFrame to a variable `result_df` (e.g., `result_df = df.describe()`).** | |
| 7. Do not modify the original `df`. Use `df.copy()` if needed. | |
| 8. Provide a brief, user-friendly explanation of the result. | |
| 9. Respond **ONLY** with a single, raw JSON object with keys: "thought", "code", "explanation". | |
| **DataFrame Metadata:** | |
| - Columns and dtypes: {metadata['dtypes']} | |
| - First 5 rows: {metadata['head']} | |
| **User Question:** "{user_message}" | |
| **Your JSON Response:** | |
| """ | |
| try: | |
| genai.configure(api_key=api_key) | |
| model = genai.GenerativeModel('gemini-1.5-flash') | |
| response = model.generate_content(prompt) | |
| response_text = response.text.strip().replace("```json", "").replace("```", "") | |
| response_json = json.loads(response_text) | |
| thought = response_json.get("thought", "Thinking...") | |
| code_to_run = response_json.get("code", "") | |
| explanation = response_json.get("explanation", "Here is the result.") | |
| stdout, fig_result, df_result, error = safe_exec(code_to_run, {'df': state_dict['df'], 'px': px, 'pd': pd, 'np': np}) | |
| bot_message = f"🤔 **Thought:** *{thought}*" | |
| history[-1] = (user_message, bot_message) | |
| # Prepare outputs, making them visible only if they contain content | |
| output_updates = [gr.update(visible=False, value=None)] * 4 # [explanation, code, plot, table] | |
| if explanation: output_updates[0] = gr.update(visible=True, value=f"**Phoenix Co-pilot:** {explanation}") | |
| if code_to_run: output_updates[1] = gr.update(visible=True, value=code_to_run) | |
| if fig_result: output_updates[2] = gr.update(visible=True, value=fig_result) | |
| if df_result is not None: output_updates[3] = gr.update(visible=True, value=df_result) | |
| if stdout: | |
| # Append stdout to explanation if it exists | |
| new_explanation = (output_updates[0]['value'] if output_updates[0]['value'] else "") + f"\n\n**Console Output:**\n```\n{stdout}\n```" | |
| output_updates[0] = gr.update(visible=True, value=new_explanation) | |
| if error: | |
| error_explanation = f"**Phoenix Co-pilot:** I encountered an error. Here's the details:\n\n`{error}`" | |
| output_updates[0] = gr.update(visible=True, value=error_explanation) | |
| return history, *output_updates | |
| except Exception as e: | |
| error_msg = f"A critical error occurred: {e}. The AI may have returned an invalid response. Please try rephrasing your question." | |
| history[-1] = (user_message, error_msg) | |
| return history, *[gr.update(visible=False)] * 4 | |
| # --- Gradio UI Definition --- | |
| def create_gradio_interface(): | |
| with gr.Blocks(theme=gr.themes.Monochrome(primary_hue="indigo", secondary_hue="blue"), css=CSS, title="Phoenix AI Data Explorer") as demo: | |
| global_state = gr.State({}) | |
| with gr.Row(): | |
| # --- Sidebar --- | |
| with gr.Column(scale=1, elem_classes="sidebar"): | |
| gr.Markdown("# 🚀 Phoenix UI") | |
| gr.Markdown("AI Data Explorer") | |
| # Navigation buttons | |
| cockpit_btn = gr.Button("📊 Data Cockpit", elem_classes="selected") | |
| deep_dive_btn = gr.Button("🔍 Deep Dive Builder") | |
| copilot_btn = gr.Button("🤖 AI Co-pilot") | |
| gr.Markdown("---") | |
| file_input = gr.File(label="📁 Upload New CSV", file_types=[".csv"]) | |
| status_output = gr.Markdown("Status: Awaiting data...") | |
| gr.Markdown("---") | |
| api_key_input = gr.Textbox(label="🔑 Gemini API Key", type="password", placeholder="Enter key here...") | |
| suggestion_btn = gr.Button("Get Smart Suggestions", variant="secondary") | |
| # --- Main Content Area --- | |
| with gr.Column(scale=4): | |
| # Welcome Page (Visible initially) | |
| with gr.Column(visible=True) as welcome_page: | |
| gr.Markdown("# Welcome to the AI Data Explorer (Phoenix UI)", elem_id="welcome-header") | |
| gr.Markdown("Please **upload a CSV file** and **enter your Gemini API key** in the sidebar to begin.") | |
| gr.Image(value="https://i.imgur.com/gY5wSjV.png", label="Workflow", show_label=False, show_download_button=False, container=False) # Placeholder image | |
| # Page 1: Data Cockpit (Hidden initially) | |
| with gr.Column(visible=False) as cockpit_page: | |
| gr.Markdown("## 📊 Data Cockpit") | |
| with gr.Row(): | |
| with gr.Column(elem_classes="stat-card"): | |
| gr.Markdown("<div class='stat-card-title'>Rows</div>", elem_classes="stat-card-content") | |
| rows_stat = gr.Textbox("0", show_label=False, elem_classes="stat-card-value") | |
| with gr.Column(elem_classes="stat-card"): | |
| gr.Markdown("<div class='stat-card-title'>Columns</div>", elem_classes="stat-card-content") | |
| cols_stat = gr.Textbox("0", show_label=False, elem_classes="stat-card-value") | |
| with gr.Column(elem_classes="stat-card"): | |
| gr.Markdown("<div class='stat-card-title'>Data Quality</div>", elem_classes="stat-card-content") | |
| quality_stat = gr.Textbox("0%", show_label=False, elem_classes="stat-card-value") | |
| with gr.Column(elem_classes="stat-card"): | |
| gr.Markdown("<div class='stat-card-title'>Date/Time Cols</div>", elem_classes="stat-card-content") | |
| time_cols_stat = gr.Textbox("0", show_label=False, elem_classes="stat-card-value") | |
| suggestion_status = gr.Markdown(visible=True) | |
| suggestion_accordion = gr.Accordion(label="✨ AI Smart Suggestions", open=False, visible=False) | |
| # Page 2: Deep Dive Dashboard Builder (Hidden initially) | |
| with gr.Column(visible=False) as deep_dive_page: | |
| gr.Markdown("## 🔍 Deep Dive Dashboard Builder") | |
| gr.Markdown("Create a custom dashboard by adding multiple plots to investigate your data.") | |
| with gr.Row(): | |
| plot_type_dd = gr.Dropdown(['histogram', 'bar', 'scatter', 'box'], label="Plot Type", value='histogram') | |
| x_col_dd = gr.Dropdown([], label="X-Axis / Column") | |
| y_col_dd = gr.Dropdown([], label="Y-Axis (for Scatter/Box)") | |
| with gr.Row(): | |
| add_plot_btn = gr.Button("Add to Dashboard", variant="primary") | |
| clear_plots_btn = gr.Button("Clear Dashboard") | |
| dashboard_accordion = gr.Accordion(label="Your Dashboard Plots", open=True) | |
| # Page 3: AI Co-pilot (Hidden initially) | |
| with gr.Column(visible=False) as copilot_page: | |
| gr.Markdown("## 🤖 AI Co-pilot") | |
| gr.Markdown("Ask complex questions in natural language. The Co-pilot will write and execute code to find the answer.") | |
| chatbot = gr.Chatbot(height=400, label="Conversation with Co-pilot") | |
| # AI's multi-modal response area | |
| with gr.Accordion("Co-pilot's Response Details", open=True): | |
| copilot_explanation = gr.Markdown(visible=False, elem_classes="explanation-block") | |
| copilot_code = gr.Code(language="python", visible=False, label="Executed Python Code", elem_classes="code-block") | |
| copilot_plot = gr.Plot(visible=False, label="Generated Visualization") | |
| copilot_table = gr.Dataframe(visible=False, label="Generated Table", wrap=True) | |
| with gr.Row(): | |
| chat_input = gr.Textbox(label="Your Question", placeholder="e.g., 'What is the correlation between age and salary?'", scale=4) | |
| chat_submit_btn = gr.Button("Submit", variant="primary") | |
| # --- Event Handlers --- | |
| # Page Navigation | |
| 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 i=i: (gr.update(visible=i==0), gr.update(visible=i==1), gr.update(visible=i==2)), | |
| outputs=pages).then( | |
| lambda: [gr.update(elem_classes="selected" if j==i else "") for j in range(len(nav_buttons))], | |
| outputs=nav_buttons) | |
| # File Upload | |
| file_input.upload( | |
| fn=load_and_process_file, | |
| inputs=[file_input, global_state], | |
| outputs=[global_state, status_output, welcome_page, cockpit_page, deep_dive_page, copilot_page, | |
| rows_stat, cols_stat, quality_stat, time_cols_stat, | |
| x_col_dd, y_col_dd, copilot_input_col_ref_for_dynamic_update] # This last one is a dummy to trigger updates | |
| ).then( | |
| fn=lambda: (gr.update(elem_classes="selected"), gr.update(elem_classes=""), gr.update(elem_classes="")), | |
| outputs=nav_buttons | |
| ) | |
| # Suggestions Button | |
| suggestion_btn.click( | |
| get_ai_suggestions, | |
| [global_state, api_key_input], | |
| [suggestion_status, suggestion_accordion] | |
| ).then( | |
| fn=lambda: [gr.Button.update(visible=True) for _ in range(5)], # Assumes max 5 suggestions for demo | |
| outputs=[b for b in suggestion_accordion.children] if isinstance(suggestion_accordion, gr.Accordion) and suggestion_accordion.children else [] | |
| ) | |
| # Handle suggestion button clicks to populate chat | |
| if isinstance(suggestion_accordion, gr.Accordion): | |
| for button in suggestion_accordion.children: | |
| button.click( | |
| fn=lambda q=button.value: (gr.update(visible=False), gr.update(visible=False), gr.update(visible=True), q), | |
| outputs=[cockpit_page, deep_dive_page, copilot_page, chat_input] | |
| ).then( | |
| fn=lambda: (gr.update(elem_classes=""), gr.update(elem_classes=""), gr.update(elem_classes="selected")), | |
| outputs=nav_buttons | |
| ) | |
| # Dashboard Builder | |
| add_plot_btn.click(add_plot_to_dashboard, [global_state, x_col_dd, y_col_dd, plot_type_dd], [global_state, dashboard_accordion]) | |
| clear_plots_btn.click(clear_dashboard, [global_state], [global_state, dashboard_accordion]) | |
| # AI Co-pilot Chat | |
| copilot_input_col_ref_for_dynamic_update = x_col_dd # Dummy placeholder for dynamic updates | |
| chat_submit_btn.click( | |
| respond_to_chat, | |
| [chat_input, chatbot, global_state, api_key_input], | |
| [chatbot, copilot_explanation, copilot_code, copilot_plot, copilot_table] | |
| ).then(lambda: "", outputs=[chat_input]) | |
| chat_input.submit( | |
| respond_to_chat, | |
| [chat_input, chatbot, global_state, api_key_input], | |
| [chatbot, copilot_explanation, copilot_code, copilot_plot, copilot_table] | |
| ).then(lambda: "", outputs=[chat_input]) | |
| return demo | |
| if __name__ == "__main__": | |
| app = create_gradio_interface() | |
| app.launch(debug=True) |