ui_model_responses.py

import gradio as gr
import pandas as pd


def add_model_responses_tab(block, data_manager, available_models):
    with gr.Tab("Model Cevapları"):
        gr.Markdown("### Model Cevaplarını Göz At")
        gr.Markdown("**6.200 soruyu ve model cevaplarını model ve kategoriye göre filtreleyin.**")
        
        # Prepare category choices from actual data
        categories = ["Tümü"]
        if not data_manager.responses_data.empty:
            # Check what category column actually exists
            category_col = None
            for col in data_manager.responses_data.columns:
                if 'kategori' in col.lower() or 'category' in col.lower() or 'bolum' in col.lower():
                    category_col = col
                    break
            
            if category_col:
                categories += sorted(data_manager.responses_data[category_col].dropna().unique().tolist())
        
        with gr.Row():
            model_filter = gr.Dropdown(
                choices=["Tümü"] + available_models,
                label="Model Filtresi",
                value="Tümü"
            )
            category_filter = gr.Dropdown(
                choices=categories,
                label="Kategori Filtresi",
                value="Tümü"
            )
            filter_btn = gr.Button("Filtrele", variant="primary")
            clear_btn = gr.Button("Temizle")
        
        with gr.Row():
            page_size = gr.Slider(
                minimum=10,
                maximum=100,
                value=20,
                step=10,
                label="Sayfa Başına Gösterim"
            )
            page_info = gr.HTML(value="<div style='text-align: center; padding: 10px;'>Sayfa 1</div>")
        
        responses_table = gr.DataFrame(
            headers=["Soru", "Doğru Cevap", "Model Cevabı", "Kategori"],
            datatype=["str", "str", "str", "str"],
            col_count=(4, "fixed"),
            interactive=False,
            wrap=True
        )
        
        with gr.Row():
            prev_btn = gr.Button("← Önceki", variant="secondary")
            next_btn = gr.Button("Sonraki →", variant="secondary")
        
        # State variables
        current_page = gr.State(1)
        
        def filter_responses(model, category, page_size_val, page):
            try:
                df = data_manager.responses_data.copy()
                if df.empty:
                    return pd.DataFrame(), 1, "<div style='text-align: center; padding: 10px;'>Veri bulunamadı</div>"
                
                # Find the actual column names in the dataset
                question_col = None
                answer_col = None
                category_col = None
                
                # Look for question column
                for col in df.columns:
                    if 'soru' in col.lower() or 'question' in col.lower():
                        question_col = col
                        break
                
                # Look for answer column
                for col in df.columns:
                    if 'cevap' in col.lower() and not col.endswith('_cevap'):
                        answer_col = col
                        break
                
                # Look for category column
                for col in df.columns:
                    if 'kategori' in col.lower() or 'category' in col.lower() or 'bolum' in col.lower():
                        category_col = col
                        break
                
                # If we can't find required columns, return empty
                if not question_col or not answer_col:
                    return pd.DataFrame(), 1, "<div style='text-align: center; padding: 10px;'>Gerekli sütunlar bulunamadı</div>"
                
                # Filter by model
                if model and model != "Tümü":
                    model_col = f"{model}_cevap"
                    if model_col in df.columns:
                        df = df[df[model_col].notna()]
                
                # Filter by category
                if category and category != "Tümü" and category_col:
                    df = df[df[category_col] == category]
                
                # Prepare display data
                display_cols = [question_col, answer_col]
                if category_col:
                    display_cols.append(category_col)
                
                if model and model != "Tümü":
                    model_col = f"{model}_cevap"
                    if model_col in df.columns:
                        display_cols.append(model_col)
                        df_display = df[display_cols].copy()
                        df_display.columns = ['Soru', 'Doğru Cevap', 'Kategori', 'Model Cevabı'] if category_col else ['Soru', 'Doğru Cevap', 'Model Cevabı']
                    else:
                        df_display = df[display_cols].copy()
                        df_display['Model Cevabı'] = 'Cevap yok'
                        df_display.columns = ['Soru', 'Doğru Cevap', 'Kategori', 'Model Cevabı'] if category_col else ['Soru', 'Doğru Cevap', 'Model Cevabı']
                else:
                    # Show first available model response
                    model_cols = [col for col in df.columns if col.endswith('_cevap')]
                    if model_cols:
                        display_cols.append(model_cols[0])
                        df_display = df[display_cols].copy()
                        df_display.columns = ['Soru', 'Doğru Cevap', 'Kategori', 'Model Cevabı'] if category_col else ['Soru', 'Doğru Cevap', 'Model Cevabı']
                    else:
                        df_display = df[display_cols].copy()
                        df_display['Model Cevabı'] = 'Cevap yok'
                        df_display.columns = ['Soru', 'Doğru Cevap', 'Kategori', 'Model Cevabı'] if category_col else ['Soru', 'Doğru Cevap', 'Model Cevabı']
                
                # Pagination
                total_rows = len(df_display)
                total_pages = (total_rows + page_size_val - 1) // page_size_val
                page = max(1, min(page, total_pages))
                start_idx = (page - 1) * page_size_val
                end_idx = start_idx + page_size_val
                page_data = df_display.iloc[start_idx:end_idx]
                page_info_text = f"<div style='text-align: center; padding: 10px;'>Sayfa {page}/{total_pages} ({total_rows} sonuç)</div>"
                return page_data, page, page_info_text
                
            except Exception as e:
                print(f"Filter error: {e}")
                return pd.DataFrame(), 1, "<div style='text-align: center; padding: 10px;'>Filtreleme hatası</div>"
        
        def next_page(current_page_val):
            return current_page_val + 1
        
        def prev_page(current_page_val):
            return max(1, current_page_val - 1)
        
        def clear_filters():
            return "Tümü", "Tümü", 1
        
        # Event handlers
        filter_btn.click(
            filter_responses,
            inputs=[model_filter, category_filter, page_size, current_page],
            outputs=[responses_table, current_page, page_info]
        )
        
        next_btn.click(
            next_page,
            inputs=[current_page],
            outputs=[current_page]
        ).then(
            filter_responses,
            inputs=[model_filter, category_filter, page_size, current_page],
            outputs=[responses_table, current_page, page_info]
        )
        
        prev_btn.click(
            prev_page,
            inputs=[current_page],
            outputs=[current_page]
        ).then(
            filter_responses,
            inputs=[model_filter, category_filter, page_size, current_page],
            outputs=[responses_table, current_page, page_info]
        )
        
        clear_btn.click(
            clear_filters,
            outputs=[model_filter, category_filter, current_page]
        ).then(
            filter_responses,
            inputs=[model_filter, category_filter, page_size, current_page],
            outputs=[responses_table, current_page, page_info]
        )