app.py

import gradio as gr
import pandas as pd
import os
from qatch.connectors.sqlite_connector import SqliteConnector
from qatch.generate_dataset.orchestrator_generator import OrchestratorGenerator
from qatch.evaluate_dataset.orchestrator_evaluator import OrchestratorEvaluator
from predictor.orchestrator_predictor import OrchestratorPredictor
import utilities as us
import plotly.express as px
import plotly.graph_objects as go

with open('style.css', 'r') as file:
    css = file.read()

# DataFrame di default
df_default = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Age': [25, 30, 35],
    'City': ['New York', 'Los Angeles', 'Chicago']
})

models_path = "models.csv"

# Variabile globale per tenere traccia dei dati correnti
df_current = df_default.copy()

input_data = {
    'input_method': "", 
    'data_path': "",
    'db_name': "", 
    'data': {
        'data_frames': {},    # dictionary of dataframes
        'db': None             # SQLITE3 database object
    },
    'models': []
}
def load_data(file, path, use_default):
    """Carica i dati da un file, un percorso o usa il DataFrame di default."""
    global df_current
    if use_default:
        input_data["input_method"] = 'default'
        input_data["data_path"] = os.path.join(".", "data", "datainterface", "mytable.sqlite")
        input_data["db_name"] = os.path.splitext(os.path.basename(input_data["data_path"]))[0]
        input_data["data"]['data_frames'] = {'MyTable': df_current}

        #TODO assegna il db a input_data["data"]['db']

        df_current = df_default.copy()  # Ripristina i dati di default
        return input_data["data"]['data_frames']
    
    selected_inputs = sum([file is not None, bool(path), use_default])
    if selected_inputs > 1:
        return 'Errore: Selezionare solo un metodo di input alla volta.'
    
    if file is not None:
        try:
            input_data["input_method"] = 'uploaded_file'
            input_data["db_name"] = os.path.splitext(os.path.basename(file))[0]
            input_data["data_path"] = os.path.join(".", "data", f"data_interface{input_data['db_name']}.sqlite")
            input_data["data"] = us.load_data(input_data["data_path"], input_data["db_name"])
            df_current = input_data["data"]['data_frames'].get('MyTable', df_default)  # Carica il DataFrame
            print(df_current)
            print(input_data["data"])
            if( input_data["data"]['data_frames'] and not input_data["data"]['db']):
                table2primary_key = {}
                print("ok")
                for table_name, df in input_data["data"]['data_frames'].items():
                    # Assign primary keys for each table
                    table2primary_key[table_name] = 'id'
                print("ok2")
                input_data["data"]["db"] = SqliteConnector(
                    relative_db_path=input_data["data_path"],
                    db_name=input_data["db_name"],
                    tables= input_data["data"]['data_frames'],
                    table2primary_key=table2primary_key
                )
                print(input_data["data"]["db"])
            return input_data["data"]['data_frames']
        except Exception as e:
            return f'Errore nel caricamento del file: {e}'
    
    if path:
        if not os.path.exists(path):
            return 'Errore: Il percorso specificato non esiste.'
        try:
            input_data["input_method"] = 'uploaded_file'
            input_data["data_path"] = path
            input_data["db_name"] = os.path.splitext(os.path.basename(path))[0]
            input_data["data"] = us.load_data(input_data["data_path"], input_data["db_name"])
            df_current = input_data["data"]['data_frames'].get('MyTable', df_default)  # Carica il DataFrame
            
            return input_data["data"]['data_frames']
        except Exception as e:
            return f'Errore nel caricamento del file dal percorso: {e}'

    return input_data["data"]['data_frames']

def preview_default(use_default):
    """Mostra il DataFrame di default se il checkbox è selezionato."""
    if use_default:
        return df_default  # Mostra il DataFrame di default
    return df_current  # Mostra il DataFrame corrente, che potrebbe essere stato modificato

def update_df(new_df):
    """Aggiorna il DataFrame corrente."""
    global df_current  # Usa la variabile globale per aggiornarla
    df_current = new_df
    return df_current

def open_accordion(target):
    # Apre uno e chiude l'altro
    if target == "reset":
        return gr.update(open=True), gr.update(open=False, visible=False), gr.update(open=False, visible=False), gr.update(open=False, visible=False), gr.update(open=False, visible=False)
    elif target == "model_selection":
        return gr.update(open=False), gr.update(open=False), gr.update(open=True, visible=True), gr.update(open=False), gr.update(open=False)

# Interfaccia Gradio
interface = gr.Blocks(theme='d8ahazard/rd_blue', css_paths='style.css')

with interface:
    gr.Markdown("# QATCH")
    data_state = gr.State(None)  # Memorizza i dati caricati
    upload_acc = gr.Accordion("Upload your data section", open=True, visible=True)
    select_table_acc = gr.Accordion("Select tables", open=False, visible=False)
    select_model_acc = gr.Accordion("Select models", open=False, visible=False)
    qatch_acc = gr.Accordion("QATCH execution", open=False, visible=False)
    metrics_acc = gr.Accordion("Metrics", open=False, visible=False)



    #################################
    #  PARTE DI INSERIMENTO DEL DB  #
    #################################
    with upload_acc:
        gr.Markdown("## Caricamento dei Dati")

        file_input = gr.File(label="Trascina e rilascia un file", file_types=[".csv", ".xlsx", ".sqlite"])
        path_input = gr.Textbox(label="Oppure inserisci il percorso locale del file")
        with gr.Row():
            default_checkbox = gr.Checkbox(label="Usa DataFrame di default")
        preview_output = gr.DataFrame(interactive=True, visible=True, value=df_default)
        submit_button = gr.Button("Carica Dati", interactive=False)  # Disabilitato di default
        output = gr.JSON(visible=False)  # Output dizionario

        # Funzione per abilitare il bottone se sono presenti dati da caricare
        def enable_submit(file, path, use_default):
            return gr.update(interactive=bool(file or path or use_default))

        # Abilita il bottone quando i campi di input sono valorizzati
        file_input.change(fn=enable_submit, inputs=[file_input, path_input, default_checkbox], outputs=[submit_button])
        path_input.change(fn=enable_submit, inputs=[file_input, path_input, default_checkbox], outputs=[submit_button])
        default_checkbox.change(fn=enable_submit, inputs=[file_input, path_input, default_checkbox], outputs=[submit_button])
        
        # Mostra l'anteprima del DataFrame di default quando il checkbox è selezionato
        default_checkbox.change(fn=preview_default, inputs=[default_checkbox], outputs=[preview_output])
        preview_output.change(fn=update_df, inputs=[preview_output], outputs=[preview_output])

        def handle_output(file, path, use_default):
            """Gestisce l'output quando si preme il bottone 'Carica Dati'."""
            result = load_data(file, path, use_default)
            
            if isinstance(result, dict):  # Se result è un dizionario di DataFrame
                if len(result) == 1:  # Se c'è solo una tabella
                    return (
                        gr.update(visible=False),  # Nasconde l'output JSON
                        result,  # Salva lo stato dei dati
                        gr.update(visible=False),  # Nasconde la selezione tabella
                        result,  # Mantiene lo stato dei dati
                        gr.update(interactive=False),  # Disabilita il pulsante di submit
                        gr.update(visible=True, open=True),  # Passa direttamente a select_model_acc
                        gr.update(visible=True, open=False)
                    )
                else:
                    return (
                        gr.update(visible=False),
                        result,
                        gr.update(open=True, visible=True),
                        result,
                        gr.update(interactive=False),
                        gr.update(visible=False),  # Mantiene il comportamento attuale
                        gr.update(visible=True, open=True)
                    )
            else:
                return (
                    gr.update(visible=False),
                    None,
                    gr.update(open=False, visible=True),
                    None,
                    gr.update(interactive=True),
                    gr.update(visible=False),
                    gr.update(visible=True, open=True)
                )

        submit_button.click(
            fn=handle_output,
            inputs=[file_input, path_input, default_checkbox], 
            outputs=[output, output, select_table_acc, data_state, submit_button, select_model_acc, upload_acc]
        )



    ######################################
    #  PARTE DI SELEZIONE DELLE TABELLE  #
    ######################################
    with select_table_acc:
        table_selector = gr.CheckboxGroup(choices=[], label="Seleziona le tabelle da visualizzare", value=[])
        table_outputs = [gr.DataFrame(label=f"Tabella {i+1}", interactive=True, visible=False) for i in range(5)]
        selected_table_names = gr.Textbox(label="Tabelle selezionate", visible=False, interactive=False)

        # Bottone di selezione modelli (inizialmente disabilitato)
        open_model_selection = gr.Button("Choose your models", interactive=False)

        def update_table_list(data):
            """Aggiorna dinamicamente la lista delle tabelle disponibili."""
            if isinstance(data, dict) and data:
                table_names = list(data.keys())  # Ritorna solo i nomi delle tabelle
                return gr.update(choices=table_names, value=[])  # Reset delle selezioni
            return gr.update(choices=[], value=[])

        def show_selected_tables(data, selected_tables):
            """Mostra solo le tabelle selezionate dall'utente e abilita il bottone."""
            updates = []
            if isinstance(data, dict) and data:
                available_tables = list(data.keys())  # Nomi effettivamente disponibili
                selected_tables = [t for t in selected_tables if t in available_tables]  # Filtra selezioni valide
                
                tables = {name: data[name] for name in selected_tables}  # Filtra i DataFrame
                
                for i, (name, df) in enumerate(tables.items()):
                    updates.append(gr.update(value=df, label=f"Tabella: {name}", visible=True))

                # Se ci sono meno di 5 tabelle, nascondi gli altri DataFrame
                for _ in range(len(tables), 5):
                    updates.append(gr.update(visible=False))
            else:
                updates = [gr.update(value=pd.DataFrame(), visible=False) for _ in range(5)]

            # Abilitare/disabilitare il bottone in base alle selezioni
            button_state = bool(selected_tables)  # True se almeno una tabella è selezionata, False altrimenti
            updates.append(gr.update(interactive=button_state))  # Aggiorna stato bottone
            
            return updates

        def show_selected_table_names(selected_tables):
            """Mostra i nomi delle tabelle selezionate quando si preme il bottone."""
            if selected_tables:
                return gr.update(value=", ".join(selected_tables), visible=False)
            return gr.update(value="", visible=False)

        # Aggiorna automaticamente la lista delle checkbox quando `data_state` cambia
        data_state.change(fn=update_table_list, inputs=[data_state], outputs=[table_selector])

        # Aggiorna le tabelle visibili e lo stato del bottone in base alle selezioni dell'utente
        table_selector.change(fn=show_selected_tables, inputs=[data_state, table_selector], outputs=table_outputs + [open_model_selection])

        # Mostra la lista delle tabelle selezionate quando si preme "Choose your models"
        open_model_selection.click(fn=show_selected_table_names, inputs=[table_selector], outputs=[selected_table_names])
        open_model_selection.click(open_accordion, inputs=gr.State("model_selection"), outputs=[upload_acc, select_table_acc, select_model_acc, qatch_acc, metrics_acc])



    ####################################
    #  PARTE DI SELEZIONE DEL MODELLO  #
    ####################################
    with select_model_acc:
        gr.Markdown("**Model Selection**")

        # Supponiamo che `us.read_models_csv` restituisca anche il percorso dell'immagine
        model_list_dict = us.read_models_csv(models_path)
        model_list = [model["name"] for model in model_list_dict]
        model_images = [model["image_path"] for model in model_list_dict]

        # Creazione dinamica di checkbox con immagini
        model_checkboxes = []
        for model, image_path in zip(model_list, model_images):
            with gr.Row():
                with gr.Column(scale=1):

                    gr.Image(image_path, show_label=False)
                with gr.Column(scale=2):
                    model_checkboxes.append(gr.Checkbox(label=model, value=False))

        selected_models_output = gr.JSON(visible = False)

        # Funzione per ottenere i modelli selezionati
        def get_selected_models(*model_selections):
            selected_models = [model for model, selected in zip(model_list, model_selections) if selected]
            input_data['models'] = selected_models
            button_state = bool(selected_models)  # True se almeno un modello è selezionato, False altrimenti
            return selected_models, gr.update(open=True, visible=True), gr.update(interactive=button_state)

        # Bottone di submit (inizialmente disabilitato)
        submit_models_button = gr.Button("Submit Models", interactive=False)

        # Collegamento dei checkbox agli eventi di selezione
        for checkbox in model_checkboxes:
            checkbox.change(
                fn=get_selected_models, 
                inputs=model_checkboxes, 
                outputs=[selected_models_output, select_model_acc, submit_models_button]
            )

        submit_models_button.click(
            fn=lambda *args: (get_selected_models(*args), gr.update(open=False, visible=True), gr.update(open=True, visible=True)),
            inputs=model_checkboxes,
            outputs=[selected_models_output, select_model_acc, qatch_acc]
        )
        
        reset_data = gr.Button("Open upload data section")
        reset_data.click(open_accordion, inputs=gr.State("reset"), outputs=[upload_acc, select_table_acc, select_model_acc, qatch_acc, metrics_acc])



    ###############################
    #  PARTE DI ESECUZIONE QATCH  #
    ###############################
    with qatch_acc:
        selected_models_display = gr.JSON(label="Modelli selezionati")
        submit_models_button.click(
            fn=lambda: gr.update(value=input_data), 
            outputs=[selected_models_display]
        )
        
        proceed_to_metrics_button = gr.Button("Proceed to Metrics")
        proceed_to_metrics_button.click(
            fn=lambda: (gr.update(open=False, visible=True), gr.update(open=True, visible=True)), 
            outputs=[qatch_acc, metrics_acc]
        )
        
        reset_data = gr.Button("Open upload data section")
        reset_data.click(open_accordion, inputs=gr.State("reset"), outputs=[upload_acc, select_table_acc, select_model_acc, qatch_acc, metrics_acc])
                
            
    #######################################
    #  PARTE DI VISUALIZZAZIONE METRICHE  #
    #######################################
    with metrics_acc:
        confirmation_text = gr.Markdown("## Metrics successfully loaded")

        data_path = 'metrics_random2.csv'

        def load_data_csv_es():
            return pd.read_csv(data_path)

        def calculate_average_metrics(df, selected_metrics):
            df['avg_metric'] = df[selected_metrics].mean(axis=1)
            return df

        def plot_metric(df, selected_metrics, group_by, selected_models):
            df = df[df['model'].isin(selected_models)]
            df = calculate_average_metrics(df, selected_metrics)
            avg_metrics = df.groupby(group_by)['avg_metric'].mean().reset_index()
            fig = px.bar(
                avg_metrics, x=group_by[0], y='avg_metric', color=group_by[-1], barmode='group',
                title=f'Media metrica per {group_by[0]}',
                labels={group_by[0]: group_by[0].capitalize(), 'avg_metric': 'Media Metrica'},
                template='plotly_dark'
            )
            return fig

        def plot_radar(df, selected_models):
            radar_data = []
            for model in selected_models:
                model_df = df[df['model'] == model]
                valid_efficiency = model_df['valid_efficiency_score'].mean()
                avg_time = model_df['time'].mean()
                avg_tuple_order = model_df['tuple_order'].dropna().mean()
                
                radar_data.append({
                    'model': model,
                    'valid_efficiency_score': valid_efficiency,
                    'time': avg_time,
                    'tuple_order': avg_tuple_order
                })
            
            radar_df = pd.DataFrame(radar_data)
            categories = ['valid_efficiency_score', 'time', 'tuple_order']
            
            # Calcola il range dinamico per il grafico
            min_val = radar_df[categories].min().min()
            max_val = radar_df[categories].max().max()
            radar_df[categories] = (radar_df[categories] - min_val) / (max_val - min_val)
            
            fig = go.Figure()
            for _, row in radar_df.iterrows():
                fig.add_trace(go.Scatterpolar(
                    r=[row[cat] for cat in categories],
                    theta=categories,
                    fill='toself',
                    name=row['model']
                ))
            
            fig.update_layout(
                polar=dict(radialaxis=dict(visible=True, range=[min_val, max_val])),
                title='Radar Plot delle Metriche per Modello',
                template='plotly_dark',
                width=700, height=700  
            )
            
            return fig

        def plot_query_rate(df, selected_models, show_labels):
            df = df[df['model'].isin(selected_models)]
            
            fig = go.Figure()
            
            for model in selected_models:
                model_df = df[df['model'] == model].copy()
                
                model_df['cumulative_time'] = model_df['time'].cumsum()
                model_df['query_rate'] = 1 / model_df['time']
                
                fig.add_trace(go.Scatter(
                    x=model_df['cumulative_time'],
                    y=model_df['query_rate'], 
                    mode='lines+markers',
                    name=model,
                    line=dict(width=2)
                ))
                
                if show_labels:
                    prev_category = None
                    prev_time = -float('inf')
                    y_positions = [1.1, 1.3]
                    y_idx = 0
                    
                    for i, row in model_df.iterrows():
                        current_category = row['test_category']
                        if current_category != prev_category and row['cumulative_time'] - prev_time > 5:
                            fig.add_vline(x=row['cumulative_time'], line_width=1, line_dash="dash", line_color="gray")
                            fig.add_annotation(
                                x=row['cumulative_time'], 
                                y=max(model_df['query_rate']) * y_positions[y_idx % 2],
                                text=current_category, 
                                showarrow=False, 
                                font=dict(size=10, color="white"),
                                textangle=45,
                                yshift=10, 
                                bgcolor="rgba(0,0,0,0.6)"
                            )
                            prev_category = current_category
                            prev_time = row['cumulative_time']
                            y_idx += 1
            
            fig.update_layout(
                title="Rate di Generazione delle Query per Modello",
                xaxis_title="Tempo Cumulativo (s)",
                yaxis_title="Query al Secondo",
                template='plotly_dark',
                legend_title="Modelli"
            )
            
            return fig

        def update_plot(selected_metrics, group_by, selected_models):
            df = load_data_csv_es()
            return plot_metric(df, selected_metrics, group_by, selected_models)

        def update_radar(selected_models):
            df = load_data_csv_es()
            return plot_radar(df, selected_models)

        def update_query_rate(selected_models, show_labels):
            df = load_data_csv_es()
            return plot_query_rate(df, selected_models, show_labels)

        def plot_query_time_evolution(df, selected_models):
            # Filtriamo i dati per i modelli selezionati
            df = df[df['model'].isin(selected_models)]
            
            # Ordinare per modello e tempo per tracciare l'evoluzione
            df_sorted = df.sort_values(by=['model', 'time'])
            
            fig = go.Figure()
            
            # Aggiungiamo una traccia per ogni modello
            for model in selected_models:
                model_df = df_sorted[df_sorted['model'] == model]
                fig.add_trace(go.Scatter(
                    x=model_df.index, y=model_df['time'], mode='lines+markers', name=model,
                    line=dict(shape='linear'),
                    text=model_df['model']
                ))

            fig.update_layout(
                title="Evoluzione del Tempo di Generazione per Modello",
                xaxis_title="Indice della Query",
                yaxis_title="Tempo (s)",
                template='plotly_dark'
            )
            
            return fig


        metrics = ["cell_precision", "cell_recall", "execution_accuracy", "tuple_cardinality", "tuple_constraint"]
        group_options = {
            "SQL Category": ["test_category", "model"],
            "Tabella": ["tbl_name", "model"],
            "Modello": ["model"]
        }

        df_initial = load_data_csv_es()
        models = df_initial['model'].unique().tolist()

        #with gr.Blocks(theme=gr.themes.Default(primary_hue='blue')) as demo:
        gr.Markdown("""## Analisi delle prestazioni dei modelli
        Seleziona una o più metriche per calcolare la media e visualizzare gli istogrammi e radar plots.
        """)

        # Sezione di selezione delle opzioni
        with gr.Row():
            metric_multiselect = gr.CheckboxGroup(choices=metrics, label="Seleziona le metriche")
            model_multiselect = gr.CheckboxGroup(choices=models, label="Seleziona i modelli", value=models)
            group_radio = gr.Radio(choices=list(group_options.keys()), label="Seleziona il raggruppamento", value="SQL Category")
            #show_labels_checkbox = gr.Checkbox(label="Mostra etichette test category", value=True)

        with gr.Row():
            output_plot = gr.Plot()
        # Dividi la pagina in due colonne
        with gr.Row():
            with gr.Column(scale=1):  # Imposta la colonna a occupare metà della larghezza
                radar_plot = gr.Plot(value=update_radar(models))
            with gr.Column(scale=2):  # Imposta la seconda colonna a occupare l'altra metà
                show_labels_checkbox = gr.Checkbox(label="Mostra etichette test category", value=True)
                query_rate_plot = gr.Plot(value=update_query_rate(models, True))

        # Funzioni di callback per il cambiamento dei grafici
        def on_change(selected_metrics, selected_group, selected_models):
            return update_plot(selected_metrics, group_options[selected_group], selected_models)

        def on_radar_change(selected_models):
            return update_radar(selected_models)
        
        show_labels_checkbox.change(update_query_rate, inputs=[model_multiselect, show_labels_checkbox], outputs=query_rate_plot)
        metric_multiselect.change(on_change, inputs=[metric_multiselect, group_radio, model_multiselect], outputs=output_plot)
        group_radio.change(on_change, inputs=[metric_multiselect, group_radio, model_multiselect], outputs=output_plot)
        model_multiselect.change(on_change, inputs=[metric_multiselect, group_radio, model_multiselect], outputs=output_plot)
        model_multiselect.change(on_radar_change, inputs=model_multiselect, outputs=radar_plot)
        model_multiselect.change(update_query_rate, inputs=[model_multiselect, show_labels_checkbox], outputs=query_rate_plot)
        
        reset_data = gr.Button("Open upload data section")
        reset_data.click(open_accordion, inputs=gr.State("reset"), outputs=[upload_acc, select_table_acc, select_model_acc, qatch_acc, metrics_acc])
      
interface.launch()