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') # --- Expert-Crafted Dark Theme CSS --- CSS = """ /* --- Phoenix UI Custom Dark CSS --- */ /* Stat Card Styling */ .stat-card { border-radius: 12px !important; padding: 20px !important; background: #1f2937 !important; /* Dark blue-gray background */ border: 1px solid #374151 !important; 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: #9ca3af !important; margin-bottom: 8px; } .stat-card-value { font-size: 32px; font-weight: 700; color: #f9fafb !important; } /* 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: #111827 !important; /* Very dark blue-gray */ 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; /* Light gray text for readability */ font-size: 16px !important; padding: 12px 10px !important; margin-bottom: 8px !important; border-radius: 8px !important; } .sidebar .gr-button:hover { background-color: #374151 !important; } /* Hover state */ .sidebar .gr-button.selected { background-color: #4f46e5 !important; font-weight: 600 !important; color: white !important; } /* Selected state with primary color */ /* AI Co-pilot Styling */ .code-block { border: 1px solid #374151 !important; border-radius: 8px; } .explanation-block { background-color: #1e3a8a !important; /* Dark blue background */ border-left: 4px solid #3b82f6 !important; /* Brighter blue border */ padding: 12px; color: #e5e7eb !important; } """ # --- Helper and Core Functions (Unchanged) --- def safe_exec(code_string: str, local_vars: dict): 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)}" def load_and_process_file(file_obj, state_dict): if file_obj is None: return state_dict, "Please upload a file.", *[gr.update(visible=False)] * 4 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." cockpit_update = gr.update(visible=True) welcome_update = gr.update(visible=False) rows, cols = metadata['shape'] quality = metadata['data_quality'] return (state_dict, status_msg, welcome_update, cockpit_update, gr.update(visible=False), gr.update(visible=False), 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()] * 11 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} def switch_page(page_name): return (gr.update(visible=page_name=="cockpit"), gr.update(visible=page_name=="deep_dive"), gr.update(visible=page_name=="co-pilot")) def get_ai_suggestions(state_dict, api_key): if not api_key: return "Enter your Gemini API key to get suggestions.", *[gr.update(visible=False)]*5 if not state_dict: return "Upload data first.", *[gr.update(visible=False)]*5 metadata = state_dict['metadata'] prompt = f""" Based on the following dataset metadata, generate 3 to 5 specific, actionable, and interesting analytical questions... 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) buttons = [gr.Button(s, variant="secondary", visible=True) for s in suggestions] buttons += [gr.Button(visible=False)] * (5 - len(buttons)) return gr.update(visible=False), *buttons except Exception as e: return f"Could not generate suggestions: {e}", *[gr.update(visible=False)]*5 def handle_suggestion_click(question_text): return (gr.update(visible=False), gr.update(visible=False), gr.update(visible=True), question_text) def add_plot_to_dashboard(state_dict, x_col, y_col, plot_type): if not x_col: gr.Warning("Please select at least an X-axis column.") return state_dict, state_dict.get('dashboard_plots', []) 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: fig.update_layout(template="plotly_dark") state_dict['dashboard_plots'].append(fig) return state_dict, state_dict['dashboard_plots'] except Exception as e: gr.Warning(f"Plotting Error: {e}") return state_dict, state_dict.get('dashboard_plots', []) def clear_dashboard(state_dict): state_dict['dashboard_plots'] = [] return state_dict, [] def respond_to_chat(user_message, history, state_dict, api_key): if not api_key: history.append((user_message, "I need a Gemini API key to function...")) 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... [Prompt remains the same] **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_json = json.loads(response.text.strip().replace("```json", "").replace("```", "")) 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}) history[-1] = (user_message, f"🤔 **Thought:** *{thought}*") output_updates = [gr.update(visible=False, value=None)] * 4 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: new_explanation = (output_updates[0]['value'] if output_updates[0]['visible'] else "") + f"\n\n**Console Output:**\n```\n{stdout}\n```" output_updates[0] = gr.update(visible=True, value=new_explanation) if error: output_updates[0] = gr.update(visible=True, value=f"**Phoenix Co-pilot:** I encountered an error. Here's the details:\n\n`{error}`") return history, *output_updates except Exception as e: history[-1] = (user_message, f"A critical error occurred: {e}.") return history, *[gr.update(visible=False)] * 4 # --- Gradio UI Definition --- def create_gradio_interface(): with gr.Blocks(theme=gr.themes.Glass(primary_hue="indigo", secondary_hue="blue"), css=CSS, title="Phoenix AI Data Explorer") as demo: global_state = gr.State({}) # --- CORRECTED: "Define-Then-Place" Pattern --- # 1. DEFINE all interactive components first. # Sidebar components cockpit_btn = gr.Button("📊 Data Cockpit", elem_classes="selected") deep_dive_btn = gr.Button("🔍 Deep Dive Builder") copilot_btn = gr.Button("🤖 AI Co-pilot") file_input = gr.File(label="📁 Upload New CSV", file_types=[".csv"]) status_output = gr.Markdown("Status: Awaiting data...") api_key_input = gr.Textbox(label="🔑 Gemini API Key", type="password", placeholder="Enter key here...") suggestion_btn = gr.Button("Get Smart Suggestions", variant="secondary") # Cockpit page components rows_stat = gr.Textbox("0", show_label=False, elem_classes="stat-card-value", interactive=False) cols_stat = gr.Textbox("0", show_label=False, elem_classes="stat-card-value", interactive=False) quality_stat = gr.Textbox("0%", show_label=False, elem_classes="stat-card-value", interactive=False) time_cols_stat = gr.Textbox("0", show_label=False, elem_classes="stat-card-value", interactive=False) suggestion_status = gr.Markdown(visible=True) suggestion_buttons = [gr.Button(visible=False) for _ in range(5)] # Deep Dive page components 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)") add_plot_btn = gr.Button("Add to Dashboard", variant="primary") clear_plots_btn = gr.Button("Clear Dashboard") dashboard_gallery = gr.Gallery(label="📊 Your Custom Dashboard", height="auto", columns=2, preview=True) # Co-pilot page components chatbot = gr.Chatbot(height=400, label="Conversation with Co-pilot", show_copy_button=True) copilot_explanation = gr.Markdown(visible=False, elem_classes="explanation-block") copilot_code = gr.Code(language="python", visible=False, label="Executed Python 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 correlation between age and salary?'", scale=4) chat_submit_btn = gr.Button("Submit", variant="primary") # 2. PLACE the defined components into the layout. with gr.Row(): # Sidebar Layout with gr.Column(scale=1, elem_classes="sidebar"): gr.Markdown("## 🚀 Phoenix UI") cockpit_btn deep_dive_btn copilot_btn gr.Markdown("---") file_input status_output gr.Markdown("---") api_key_input suggestion_btn # Main Content Layout with gr.Column(scale=4): with gr.Column(visible=True) as welcome_page: gr.Markdown("# Welcome to the AI Data Explorer (Phoenix UI)") gr.Markdown("Please **upload a CSV file** and **enter your Gemini API key** in the sidebar to begin.") gr.Image(value="workflow.png", show_label=False, show_download_button=False, container=False) with gr.Column(visible=False) as cockpit_page: gr.Markdown("## 📊 Data Cockpit") with gr.Row(): with gr.Column(elem_classes="stat-card"): gr.Markdown("
Rows
"); rows_stat with gr.Column(elem_classes="stat-card"): gr.Markdown("
Columns
"); cols_stat with gr.Column(elem_classes="stat-card"): gr.Markdown("
Data Quality
"); quality_stat with gr.Column(elem_classes="stat-card"): gr.Markdown("
Date/Time Cols
"); time_cols_stat suggestion_status with gr.Accordion(label="✨ AI Smart Suggestions", open=True): for btn in suggestion_buttons: btn 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 the gallery below.") with gr.Row(): plot_type_dd; x_col_dd; y_col_dd with gr.Row(): add_plot_btn; clear_plots_btn dashboard_gallery with gr.Column(visible=False) as copilot_page: gr.Markdown("## 🤖 AI Co-pilot") chatbot with gr.Accordion("Co-pilot's Response Details", open=True): copilot_explanation; copilot_code; copilot_plot; copilot_table with gr.Row(): chat_input; chat_submit_btn # 3. DEFINE all event handlers. Now all component variables are guaranteed to exist. pages = [cockpit_page, deep_dive_page, copilot_page] nav_buttons = [cockpit_btn, deep_dive_btn, copilot_btn] for i, btn in enumerate(nav_buttons): page_name = btn.value.lower().replace(" ", "_").split(" ")[-1] btn.click(lambda name=page_name: switch_page(name), 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(load_and_process_file, [file_input, global_state], [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, plot_type_dd]) \ .then(lambda: switch_page("cockpit"), outputs=pages) \ .then(lambda: [gr.update(elem_classes="selected"), gr.update(elem_classes=""), gr.update(elem_classes="")], outputs=nav_buttons) suggestion_btn.click(get_ai_suggestions, [global_state, api_key_input], [suggestion_status, *suggestion_buttons]) for btn in suggestion_buttons: btn.click(handle_suggestion_click, inputs=[btn], outputs=[cockpit_page, deep_dive_page, copilot_page, chat_input]) \ .then(lambda: (gr.update(elem_classes=""), gr.update(elem_classes=""), gr.update(elem_classes="selected")), outputs=nav_buttons) add_plot_btn.click(add_plot_to_dashboard, [global_state, x_col_dd, y_col_dd, plot_type_dd], [global_state, dashboard_gallery]) clear_plots_btn.click(clear_dashboard, [global_state], [global_state, dashboard_gallery]) 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)