Update app.py

app.py

@@ -69,14 +69,10 @@ CSS = """
 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')
+        with redirect_stdout(output_buffer): exec(code_string, globals(), local_vars)
+        stdout, fig, result_df = output_buffer.getvalue(), local_vars.get('fig'), local_vars.get('result_df')
         return stdout, fig, result_df, None
-    except Exception as e:
-        return None, None, None, f"Execution Error: {str(e)}"
+    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
@@ -85,70 +81,52 @@ def load_and_process_file(file_obj, state_dict):
         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),
+        status_msg, welcome_update = f"✅ **{state_dict['filename']}** loaded.", gr.update(visible=False)
+        rows, cols, quality = metadata['shape'][0], metadata['shape'][1], metadata['data_quality']
+        return (state_dict, status_msg, welcome_update, gr.update(visible=True), 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
+    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}
+    numeric_cols, cat_cols, dt_cols = df.select_dtypes(include=np.number).columns.tolist(), df.select_dtypes(include=['object', 'category']).columns.tolist(), df.select_dtypes(include=['datetime64', 'datetime64[ns]']).columns.tolist()
+    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': cat_cols,
+            'datetime_cols': dt_cols, 'dtypes': df.dtypes.to_string(), 'head': df.head().to_string(), 'data_quality': 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"))
+    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 api_key: return "Enter your Gemini API key...", *[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?"]
-    """
+    metadata, prompt = state_dict['metadata'], f"Based on metadata... generate 3-5 questions... Return ONLY JSON list of strings."
     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))
+        suggestions = json.loads(model.generate_content(prompt).text)
+        buttons = [gr.Button(s, variant="secondary", visible=True) for s in suggestions] + [gr.Button(visible=False)] * (5 - len(suggestions))
         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)
+    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
+    df, title = state_dict['df'], f"{plot_type.capitalize()}: {y_col} by {x_col}" if y_col 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")
-        elif plot_type == 'bar': 
+        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)
+            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_dict['dashboard_plots'].append(fig)
@@ -162,31 +140,17 @@ def clear_dashboard(state_dict):
     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:**
-    """
+    if not api_key or not state_dict:
+        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, None)); metadata = state_dict['metadata']
+    prompt = f"You are 'Phoenix Co-pilot'... IMPORTANT: add `template='plotly_dark'` to all figures... User Question: '{user_message}'"
     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.")
+        response_json = json.loads(genai.GenerativeModel('gemini-1.5-flash').generate_content(prompt).text.strip().replace("```json", "").replace("```", ""))
+        thought, code_to_run, explanation = response_json.get("thought", "Thinking..."), response_json.get("code", ""), 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)
@@ -195,123 +159,82 @@ def respond_to_chat(user_message, history, state_dict, api_key):
         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}`")
+        if error: output_updates[0] = gr.update(visible=True, value=f"**Phoenix Co-pilot:** I encountered an error:\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
+        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.
+        # "Define-Then-Place" Pattern
+        # 1. DEFINE all components
+        # Sidebar
+        # CORRECTED: Added elem_id for robust navigation
+        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("🤖 AI Co-pilot", elem_id="co-pilot")
+        file_input, status_output = gr.File(label="📁 Upload New CSV", file_types=[".csv"]), gr.Markdown("Status: Awaiting data...")
+        api_key_input, suggestion_btn = gr.Textbox(label="🔑 Gemini API Key", type="password", placeholder="Enter key..."), gr.Button("Get Smart Suggestions", variant="secondary")
         
-        # 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
+        rows_stat, cols_stat = gr.Textbox("0", show_label=False, interactive=False, elem_classes="stat-card-value"), gr.Textbox("0", show_label=False, interactive=False, elem_classes="stat-card-value")
+        quality_stat, time_cols_stat = gr.Textbox("0%", show_label=False, interactive=False, elem_classes="stat-card-value"), gr.Textbox("0", show_label=False, interactive=False, elem_classes="stat-card-value")
+        suggestion_status, suggestion_buttons = gr.Markdown(visible=True), [gr.Button(visible=False) for _ in range(5)]
         
-        # 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")
+        # Deep Dive
+        plot_type_dd, x_col_dd, y_col_dd = gr.Dropdown(['histogram', 'bar', 'scatter', 'box'], label="Plot Type", value='histogram'), gr.Dropdown([], label="X-Axis"), gr.Dropdown([], label="Y-Axis")
+        add_plot_btn, clear_plots_btn = gr.Button("Add to Dashboard", variant="primary"), gr.Button("Clear Dashboard")
         dashboard_gallery = gr.Gallery(label="📊 Your Custom Dashboard", height="auto", columns=2, preview=True)
 
-        # Co-pilot page components
+        # Co-pilot
         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")
+        copilot_explanation, copilot_code = gr.Markdown(visible=False, elem_classes="explanation-block"), gr.Code(language="python", visible=False, label="Executed Code")
+        copilot_plot, copilot_table = gr.Plot(visible=False, label="Generated Visualization"), gr.Dataframe(visible=False, label="Generated Table", wrap=True)
+        chat_input, chat_submit_btn = gr.Textbox(label="Your Question", placeholder="e.g., 'What is the correlation between age and salary?'", scale=4), gr.Button("Submit", variant="primary")
         
-        # 2. PLACE the defined components into the layout.
+        # 2. PLACE components into 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
+                gr.Markdown("## 🚀 Phoenix UI"); 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):
                 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.Markdown("# Welcome to the AI Data Explorer (Phoenix UI)\n Please **upload a CSV file** and **enter your Gemini API key** 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("<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
-                    suggestion_status
-                    with gr.Accordion(label="✨ AI Smart Suggestions", open=True):
-                        for btn in suggestion_buttons:
-                            btn
-
+                        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
+                    suggestion_status; 
+                    with gr.Accordion(label="✨ AI Smart Suggestions", open=True): [btn for btn in suggestion_buttons]
                 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
+                    gr.Markdown("## 🔍 Deep Dive Dashboard Builder"); gr.Markdown("Create a custom dashboard by adding plots to the gallery.")
+                    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
+                    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.
+        # 3. DEFINE event handlers
         pages = [cockpit_page, deep_dive_page, copilot_page]
         nav_buttons = [cockpit_btn, deep_dive_btn, copilot_btn]
         
+        # CORRECTED: Navigation logic is now robust and reliable
         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) \
+            btn.click(lambda id=btn.elem_id: 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(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]) \
+                           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)
         
@@ -325,11 +248,9 @@ def create_gradio_interface():
         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])
+                              [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])
+                          [chatbot, copilot_explanation, copilot_code, copilot_plot, copilot_table]).then(lambda: "", outputs=[chat_input])
 
     return demo