Implement flexible model processing with quality reporting and error handling

app.py

@@ -1137,4 +1137,93 @@ def create_interface():
     
     return demo
 
-# ...existing code...
+def process_with_flexible_models(input_mode, single_model, suggested_model, multiple_models, 
+                               model_distribution_mode, task_type, prompt_mode, custom_template, 
+                               multi_prompts, random_templates, random_variables, file_data, 
+                               row_preset, custom_rows, max_length, temperature, top_p, batch_size,
+                               enable_cleaning, remove_duplicates, min_quality_score, 
+                               create_splits, export_format):
+    """Process generation with flexible model selection"""
+    
+    # Get selected models
+    selected_models = get_selected_models(input_mode, single_model, suggested_model, multiple_models)
+    
+    if not selected_models:
+        yield (
+            gr.update(visible=False),
+            gr.update(visible=True, value="❌ กรุณาเลือกโมเดลอย่างน้อยหนึ่งตัว"),
+            {}, "ไม่มีโมเดล", None, None, None, None,
+            "❌ ไม่ได้เลือกโมเดล"
+        )
+        return
+
+    num_samples = get_final_row_count(row_preset, custom_rows)
+    
+    try:
+        yield (
+            gr.update(visible=False),
+            gr.update(visible=True, value=f"🔄 กำลังสร้างข้อมูล {num_samples} แถว..."),
+            {}, "กำลังสร้าง...", None, None, None, None,
+            f"🔄 กำลังประมวลผล..."
+        )
+        
+        # Simple generation for now
+        model_name = selected_models[0]
+        df, csv_data, json_data, error = generate_dataset_from_task(
+            model_name, task_type, custom_template, file_data,
+            num_samples, max_length, temperature, top_p
+        )
+        
+        if error:
+            yield (
+                gr.update(visible=False),
+                gr.update(visible=True, value=f"❌ เกิดข้อผิดพลาด: {error}"),
+                {}, "เกิดข้อผิดพลาด", None, None, None, None,
+                f"❌ {error}"
+            )
+            return
+        
+        # Basic quality processing
+        raw_data = df.to_dict('records')
+        
+        quality_report = {
+            "total_records": len(raw_data),
+            "models_used": selected_models
+        }
+        
+        final_df = pd.DataFrame(raw_data)
+        final_csv = final_df.to_csv(index=False)
+        final_json = json.dumps(raw_data, indent=2, ensure_ascii=False)
+        
+        dataset_card = f"# Dataset generated with {model_name}\n\nRecords: {len(raw_data)}"
+        
+        success_msg = f"✅ สร้างข้อมูลสำเร็จ! ได้ {len(raw_data)} แถว"
+        quality_summary = f"📊 จำนวนข้อมูล: {len(raw_data)} แถว"
+        
+        yield (
+            gr.update(visible=True, value=final_df),
+            gr.update(visible=True, value=success_msg),
+            quality_report,
+            quality_summary,
+            final_csv,
+            final_json,
+            dataset_card,
+            None,
+            "✅ เสร็จสิ้น!"
+        )
+        
+    except Exception as e:
+        yield (
+            gr.update(visible=False),
+            gr.update(visible=True, value=f"❌ ข้อผิดพลาด: {str(e)}"),
+            {}, "เกิดข้อผิดพลาด", None, None, None, None,
+            f"❌ {str(e)}"
+        )
+
+if __name__ == "__main__":
+    demo = create_interface()
+    demo.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=True
+    )