Update app.py

app.py

@@ -4,7 +4,8 @@ import io
 import os
 import pandas as pd
 import torch
-from transformers import pipeline as transformers_pipeline , AutoModelForImageClassification, CLIPImageProcessor
+# from transformers import pipeline as transformers_pipeline , AutoModelForImageClassification, CLIPImageProcessor # ImageReward пока отключен
+from transformers import pipeline as transformers_pipeline , CLIPImageProcessor # Убрали AutoModelForImageClassification
 import open_clip
 import re
 import matplotlib.pyplot as plt
@@ -12,7 +13,7 @@ import json
 from collections import defaultdict
 import numpy as np
 import logging
-import time # Для замера времени
+import time
 
 # --- ONNX Related Imports and Setup ---
 try:
@@ -23,7 +24,6 @@ except ImportError:
 
 from huggingface_hub import hf_hub_download
 
-# imgutils для rgb_encode
 try:
     from imgutils.data import rgb_encode
     IMGUTILS_AVAILABLE = True
@@ -37,17 +37,12 @@ except ImportError:
             img_arr = np.transpose(img_arr, (2, 0, 1))
         return img_arr.astype(np.uint8)
 
-# --- Модель Конфигурация и Загрузка ---
 DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
 print(f"INFO: PyTorch Device: {DEVICE}")
 ONNX_EXECUTION_PROVIDER = "CUDAExecutionProvider" if DEVICE == "cuda" and onnxruntime and "CUDAExecutionProvider" in onnxruntime.get_available_providers() else "CPUExecutionProvider"
-if onnxruntime:
-    print(f"INFO: ONNX Execution Provider: {ONNX_EXECUTION_PROVIDER}")
-else:
-    print("INFO: ONNX Runtime not available, ONNX models will be skipped.")
+if onnxruntime: print(f"INFO: ONNX Execution Provider: {ONNX_EXECUTION_PROVIDER}")
+else: print("INFO: ONNX Runtime not available, ONNX models will be skipped.")
 
-
-# --- Helper for ONNX models (deepghs) ---
 @torch.no_grad()
 def _img_preprocess_for_onnx(image: Image.Image, size: tuple = (384, 384), normalize_mean=0.5, normalize_std=0.5):
     image = image.resize(size, Image.Resampling.BILINEAR)
@@ -61,31 +56,24 @@ def _img_preprocess_for_onnx(image: Image.Image, size: tuple = (384, 384), norma
 onnx_sessions_cache = {}
 def get_onnx_session_and_meta(repo_id, model_subfolder, current_log_list):
     cache_key = f"{repo_id}/{model_subfolder}"
-    if cache_key in onnx_sessions_cache:
-        return onnx_sessions_cache[cache_key]
-
+    if cache_key in onnx_sessions_cache: return onnx_sessions_cache[cache_key]
     if not onnxruntime:
         msg = f"ERROR: ONNX Runtime not available for get_onnx_session_and_meta ({cache_key}). Skipping."
-        print(msg)
-        current_log_list.append(msg)
-        onnx_sessions_cache[cache_key] = (None, [], None) # Cache error state
+        print(msg); current_log_list.append(msg)
+        onnx_sessions_cache[cache_key] = (None, [], None)
         return None, [], None
-
     try:
         msg = f"INFO: Loading ONNX model {repo_id}/{model_subfolder}..."
         print(msg); current_log_list.append(msg)
         model_path = hf_hub_download(repo_id, filename=f"{model_subfolder}/model.onnx")
         meta_path = hf_hub_download(repo_id, filename=f"{model_subfolder}/meta.json")
-
         options = onnxruntime.SessionOptions()
         options.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
         if ONNX_EXECUTION_PROVIDER == "CPUExecutionProvider" and hasattr(os, 'cpu_count'):
              options.intra_op_num_threads = os.cpu_count()
-
         session = onnxruntime.InferenceSession(model_path, options, providers=[ONNX_EXECUTION_PROVIDER])
         with open(meta_path, 'r') as f: meta = json.load(f)
         labels = meta.get('labels', [])
-        
         msg = f"INFO: ONNX model {cache_key} loaded successfully with provider {ONNX_EXECUTION_PROVIDER}."
         print(msg); current_log_list.append(msg)
         onnx_sessions_cache[cache_key] = (session, labels, meta)
@@ -96,29 +84,25 @@ def get_onnx_session_and_meta(repo_id, model_subfolder, current_log_list):
         onnx_sessions_cache[cache_key] = (None, [], None)
         return None, [], None
 
-# --- Модели PyTorch и Transformers ---
-# 1. ImageReward
+# 1. ImageReward - ВРЕМЕННО ОТКЛЮЧЕНО
 reward_processor, reward_model = None, None
-try:
-    print("INFO: Loading THUDM/ImageReward model...")
-    reward_processor = CLIPImageProcessor.from_pretrained("openai/clip-vit-large-patch14")
-    reward_model = AutoModelForImageClassification.from_pretrained("THUDM/ImageReward").to(DEVICE)
-    reward_model.eval()
-    print("INFO: THUDM/ImageReward loaded successfully.")
-except Exception as e:
-    print(f"ERROR: Failed to load THUDM/ImageReward: {e}")
+print("INFO: THUDM/ImageReward is temporarily disabled due to loading issues.")
+# try:
+#     print("INFO: Loading THUDM/ImageReward model...")
+#     # reward_processor = CLIPImageProcessor.from_pretrained("openai/clip-vit-large-patch14")
+#     # reward_model = AutoModelForImageClassification.from_pretrained("THUDM/ImageReward", trust_remote_code=True).to(DEVICE) # Попытка с trust_remote_code
+#     # reward_model.eval()
+#     # print("INFO: THUDM/ImageReward loaded successfully.")
+# except Exception as e:
+#     print(f"ERROR: Failed to load THUDM/ImageReward: {e}")
+
 
-# 2. Anime Aesthetic (deepghs ONNX) - Константы
 ANIME_AESTHETIC_REPO = "deepghs/anime_aesthetic"
 ANIME_AESTHETIC_SUBFOLDER = "swinv2pv3_v0_448_ls0.2_x"
 ANIME_AESTHETIC_IMG_SIZE = (448, 448)
 ANIME_AESTHETIC_LABEL_WEIGHTS = {"normal": 0.0, "slight": 1.0, "moderate": 2.0, "strong": 3.0, "extreme": 4.0}
-
-# 3. MANIQA (Technical Quality) - ВРЕМЕННО ОТКЛЮЧЕНО
-# maniqa_pipe = None (уже объявлено в глобальной области видимости неявно)
 print("INFO: MANIQA (honklers/maniqa-nr) is currently disabled.")
 
-# 4. CLIP Score (laion/CLIP-ViT-L-14-laion2B-s32B-b82K) - open_clip
 clip_model_instance, clip_preprocess, clip_tokenizer = None, None, None
 try:
     clip_model_name = 'ViT-L-14'
@@ -133,8 +117,6 @@ try:
 except Exception as e:
     print(f"ERROR: Failed to load CLIP model {clip_model_name} (laion2b_s32b_b82k): {e}")
 
-# 5. AI Detectors
-# Organika/sdxl-detector - Transformers pipeline
 sdxl_detector_pipe = None
 try:
     print("INFO: Loading Organika/sdxl-detector model...")
@@ -143,24 +125,18 @@ try:
 except Exception as e:
     print(f"ERROR: Failed to load Organika/sdxl-detector: {e}")
 
-# deepghs/anime_ai_check - ONNX - Константы
 ANIME_AI_CHECK_REPO = "deepghs/anime_ai_check"
 ANIME_AI_CHECK_SUBFOLDER = "caformer_s36_plus_sce"
 ANIME_AI_CHECK_IMG_SIZE = (384, 384)
 
-
-# --- Функции извлечения метаданных (без изменений в логике, только print) ---
 def extract_sd_parameters(image_pil, filename_for_log, current_log_list):
-    # ... (остальной код extract_sd_parameters без изменений)
     if image_pil is None: return "", "N/A", "N/A", "N/A", {}
     parameters_str = image_pil.info.get("parameters", "")
     if not parameters_str:
         current_log_list.append(f"DEBUG [{filename_for_log}]: No metadata found in image.")
         return "", "N/A", "N/A", "N/A", {}
-    
-    current_log_list.append(f"DEBUG [{filename_for_log}]: Raw metadata: {parameters_str[:100]}...") # Логируем начало
+    current_log_list.append(f"DEBUG [{filename_for_log}]: Raw metadata: {parameters_str[:100]}...")
     prompt, negative_prompt, model_name, model_hash, other_params_dict = "", "N/A", "N/A", "N/A", {}
-    # ... (остальной парсинг)
     try:
         neg_prompt_index = parameters_str.find("Negative prompt:")
         steps_meta_index = parameters_str.find("Steps:")
@@ -179,10 +155,8 @@ def extract_sd_parameters(image_pil, filename_for_log, current_log_list):
             prompt = parameters_str[:steps_meta_index].strip()
             params_part = parameters_str[steps_meta_index:]
         else:
-            prompt = parameters_str.strip() # Весь текст - промпт
-            params_part = "" # Нет блока параметров
-
-        if params_part: # Если есть блок параметров после Steps:
+            prompt = parameters_str.strip(); params_part = ""
+        if params_part:
             params_list = [p.strip() for p in params_part.split(",")]
             temp_other_params = {}
             for param_val_str in params_list:
@@ -194,57 +168,51 @@ def extract_sd_parameters(image_pil, filename_for_log, current_log_list):
                     elif key.lower() == "model hash": model_hash = value
             for k,v in temp_other_params.items():
                 if k.lower() not in ["model", "model hash"]: other_params_dict[k] = v
-        
         if model_name == "N/A" and model_hash != "N/A": model_name = f"hash_{model_hash}"
         if model_name == "N/A" and "Checkpoint" in other_params_dict: model_name = other_params_dict["Checkpoint"]
         if model_name == "N/A" and "model" in other_params_dict: model_name = other_params_dict["model"]
         current_log_list.append(f"DEBUG [{filename_for_log}]: Parsed Prompt: {prompt[:50]}... | Model: {model_name}")
-
     except Exception as e:
         current_log_list.append(f"ERROR [{filename_for_log}]: Failed to parse metadata: {e}")
     return prompt, negative_prompt, model_name, model_hash, other_params_dict
 
-# --- Функции оценки (добавлено логирование и замер времени) ---
 @torch.no_grad()
 def get_image_reward(image_pil, filename_for_log, current_log_list):
-    if not reward_model or not reward_processor:
-        current_log_list.append(f"INFO [{filename_for_log}]: ImageReward model not loaded, skipping.")
-        return "N/A"
-    t_start = time.time()
-    current_log_list.append(f"DEBUG [{filename_for_log}]: Starting ImageReward score (PyTorch Device: {DEVICE})...")
-    try:
-        inputs = reward_processor(images=image_pil, return_tensors="pt", padding=True, truncation=True).to(DEVICE)
-        outputs = reward_model(**inputs)
-        score = round(outputs.logits.item(), 4)
-        t_end = time.time()
-        current_log_list.append(f"DEBUG [{filename_for_log}]: ImageReward score: {score} (took {t_end - t_start:.2f}s)")
-        return score
-    except Exception as e:
-        current_log_list.append(f"ERROR [{filename_for_log}]: ImageReward scoring failed: {e}")
-        return "Error"
+    # current_log_list.append(f"INFO [{filename_for_log}]: ImageReward model not loaded (disabled), skipping.")
+    return "N/A (Disabled)" # Временно отключено
+    # if not reward_model or not reward_processor:
+    #     current_log_list.append(f"INFO [{filename_for_log}]: ImageReward model not loaded, skipping.")
+    #     return "N/A"
+    # t_start = time.time()
+    # current_log_list.append(f"DEBUG [{filename_for_log}]: Starting ImageReward score (PyTorch Device: {DEVICE})...")
+    # try:
+    #     inputs = reward_processor(images=image_pil, return_tensors="pt", padding=True, truncation=True).to(DEVICE)
+    #     outputs = reward_model(**inputs)
+    #     score = round(outputs.logits.item(), 4)
+    #     t_end = time.time()
+    #     current_log_list.append(f"DEBUG [{filename_for_log}]: ImageReward score: {score} (took {t_end - t_start:.2f}s)")
+    #     return score
+    # except Exception as e:
+    #     current_log_list.append(f"ERROR [{filename_for_log}]: ImageReward scoring failed: {e}")
+    #     return "Error"
 
 def get_anime_aesthetic_score_deepghs(image_pil, filename_for_log, current_log_list):
     session, labels, meta = get_onnx_session_and_meta(ANIME_AESTHETIC_REPO, ANIME_AESTHETIC_SUBFOLDER, current_log_list)
     if not session or not labels:
         current_log_list.append(f"INFO [{filename_for_log}]: AnimeAesthetic ONNX model not loaded, skipping.")
         return "N/A"
-    t_start = time.time()
-    current_log_list.append(f"DEBUG [{filename_for_log}]: Starting AnimeAesthetic (ONNX) score...")
+    t_start = time.time(); current_log_list.append(f"DEBUG [{filename_for_log}]: Starting AnimeAesthetic (ONNX) score...")
     try:
         input_data = _img_preprocess_for_onnx(image_pil.copy(), size=ANIME_AESTHETIC_IMG_SIZE)
-        input_name = session.get_inputs()[0].name
-        output_name = session.get_outputs()[0].name
+        input_name = session.get_inputs()[0].name; output_name = session.get_outputs()[0].name
         onnx_output, = session.run([output_name], {input_name: input_data})
-        scores = onnx_output[0]
-        exp_scores = np.exp(scores - np.max(scores)); probabilities = exp_scores / np.sum(exp_scores)
+        scores = onnx_output[0]; exp_scores = np.exp(scores - np.max(scores)); probabilities = exp_scores / np.sum(exp_scores)
         weighted_score = sum(probabilities[i] * ANIME_AESTHETIC_LABEL_WEIGHTS.get(label, 0.0) for i, label in enumerate(labels))
-        score = round(weighted_score, 4)
-        t_end = time.time()
+        score = round(weighted_score, 4); t_end = time.time()
         current_log_list.append(f"DEBUG [{filename_for_log}]: AnimeAesthetic (ONNX) score: {score} (took {t_end - t_start:.2f}s)")
         return score
     except Exception as e:
-        current_log_list.append(f"ERROR [{filename_for_log}]: AnimeAesthetic (ONNX) scoring failed: {e}")
-        return "Error"
+        current_log_list.append(f"ERROR [{filename_for_log}]: AnimeAesthetic (ONNX) scoring failed: {e}"); return "Error"
 
 @torch.no_grad()
 def get_maniqa_score(image_pil, filename_for_log, current_log_list):
@@ -259,72 +227,56 @@ def calculate_clip_score_value(image_pil, prompt_text, filename_for_log, current
     if not prompt_text or prompt_text == "N/A":
         current_log_list.append(f"INFO [{filename_for_log}]: Empty prompt, skipping CLIPScore.")
         return "N/A (Empty Prompt)"
-    
-    t_start = time.time()
-    current_log_list.append(f"DEBUG [{filename_for_log}]: Starting CLIPScore (PyTorch Device: {DEVICE})...")
+    t_start = time.time(); current_log_list.append(f"DEBUG [{filename_for_log}]: Starting CLIPScore (PyTorch Device: {DEVICE})...")
     try:
         image_input = clip_preprocess(image_pil).unsqueeze(0).to(DEVICE)
-        text_for_tokenizer = str(prompt_text)
-        text_input = clip_tokenizer([text_for_tokenizer]).to(DEVICE)
-        image_features = clip_model_instance.encode_image(image_input)
-        text_features = clip_model_instance.encode_text(text_input)
+        text_for_tokenizer = str(prompt_text); text_input = clip_tokenizer([text_for_tokenizer]).to(DEVICE)
+        image_features = clip_model_instance.encode_image(image_input); text_features = clip_model_instance.encode_text(text_input)
         image_features_norm = image_features / image_features.norm(p=2, dim=-1, keepdim=True)
         text_features_norm = text_features / text_features.norm(p=2, dim=-1, keepdim=True)
         score_val = (text_features_norm @ image_features_norm.T).squeeze().item() * 100.0
-        score = round(score_val, 2)
-        t_end = time.time()
+        score = round(score_val, 2); t_end = time.time()
         current_log_list.append(f"DEBUG [{filename_for_log}]: CLIPScore: {score} (took {t_end - t_start:.2f}s)")
         return score
     except Exception as e:
-        current_log_list.append(f"ERROR [{filename_for_log}]: CLIPScore calculation failed: {e}")
-        return "Error"
+        current_log_list.append(f"ERROR [{filename_for_log}]: CLIPScore calculation failed: {e}"); return "Error"
 
 @torch.no_grad()
 def get_sdxl_detection_score(image_pil, filename_for_log, current_log_list):
     if not sdxl_detector_pipe:
         current_log_list.append(f"INFO [{filename_for_log}]: SDXL_Detector model not loaded, skipping.")
         return "N/A"
-    t_start = time.time()
-    current_log_list.append(f"DEBUG [{filename_for_log}]: Starting SDXL_Detector score (Device for pipeline: {sdxl_detector_pipe.device})...")
+    t_start = time.time(); current_log_list.append(f"DEBUG [{filename_for_log}]: Starting SDXL_Detector score (Device: {sdxl_detector_pipe.device})...")
     try:
-        result = sdxl_detector_pipe(image_pil.copy())
-        ai_score_val = 0.0
+        result = sdxl_detector_pipe(image_pil.copy()); ai_score_val = 0.0
         for item in result:
             if item['label'].lower() == 'artificial': ai_score_val = item['score']; break
-        score = round(ai_score_val, 4)
-        t_end = time.time()
+        score = round(ai_score_val, 4); t_end = time.time()
         current_log_list.append(f"DEBUG [{filename_for_log}]: SDXL_Detector AI Prob: {score} (took {t_end - t_start:.2f}s)")
         return score
     except Exception as e:
-        current_log_list.append(f"ERROR [{filename_for_log}]: SDXL_Detector scoring failed: {e}")
-        return "Error"
+        current_log_list.append(f"ERROR [{filename_for_log}]: SDXL_Detector scoring failed: {e}"); return "Error"
 
 def get_anime_ai_check_score_deepghs(image_pil, filename_for_log, current_log_list):
     session, labels, meta = get_onnx_session_and_meta(ANIME_AI_CHECK_REPO, ANIME_AI_CHECK_SUBFOLDER, current_log_list)
     if not session or not labels:
         current_log_list.append(f"INFO [{filename_for_log}]: AnimeAI_Check ONNX model not loaded, skipping.")
         return "N/A"
-    t_start = time.time()
-    current_log_list.append(f"DEBUG [{filename_for_log}]: Starting AnimeAI_Check (ONNX) score...")
+    t_start = time.time(); current_log_list.append(f"DEBUG [{filename_for_log}]: Starting AnimeAI_Check (ONNX) score...")
     try:
         input_data = _img_preprocess_for_onnx(image_pil.copy(), size=ANIME_AI_CHECK_IMG_SIZE)
-        input_name = session.get_inputs()[0].name
-        output_name = session.get_outputs()[0].name
+        input_name = session.get_inputs()[0].name; output_name = session.get_outputs()[0].name
         onnx_output, = session.run([output_name], {input_name: input_data})
-        scores = onnx_output[0]
-        exp_scores = np.exp(scores - np.max(scores)); probabilities = exp_scores / np.sum(exp_scores)
+        scores = onnx_output[0]; exp_scores = np.exp(scores - np.max(scores)); probabilities = exp_scores / np.sum(exp_scores)
         ai_prob_val = 0.0
         for i, label in enumerate(labels):
             if label.lower() == 'ai': ai_prob_val = probabilities[i]; break
-        score = round(ai_prob_val, 4)
-        t_end = time.time()
+        score = round(ai_prob_val, 4); t_end = time.time()
         current_log_list.append(f"DEBUG [{filename_for_log}]: AnimeAI_Check (ONNX) AI Prob: {score} (took {t_end - t_start:.2f}s)")
         return score
     except Exception as e:
-        current_log_list.append(f"ERROR [{filename_for_log}]: AnimeAI_Check (ONNX) scoring failed: {e}")
-        return "Error"
+        current_log_list.append(f"ERROR [{filename_for_log}]: AnimeAI_Check (ONNX) scoring failed: {e}"); return "Error"
 
-# --- Основная функция обработки (стала генератором) ---
 def process_images_generator(files, progress=gr.Progress(track_tqdm=True)):
     if not files:
         yield pd.DataFrame(), None, None, None, None, "Please upload some images.", "No files to process."
@@ -332,52 +284,52 @@ def process_images_generator(files, progress=gr.Progress(track_tqdm=True)):
 
     all_results = []
     log_accumulator = [f"INFO: Starting processing for {len(files)} images..."]
-    yield pd.DataFrame(), None, None, None, None, "Processing...", "\n".join(log_accumulator)
-
+    # Начальный yield для лога и статуса
+    yield (pd.DataFrame(all_results), None, None, 
+           gr.File(visible=False), gr.File(visible=False), # Скрываем кнопки скачивания вначале
+           "Processing...", "\n".join(log_accumulator))
 
     for i, file_obj in enumerate(files):
         filename_for_log = "Unknown File"
         current_img_total_time_start = time.time()
         try:
-            filename_for_log = os.path.basename(getattr(file_obj, 'name', f"file_{i}_{time.time()}"))
+            filename_for_log = os.path.basename(getattr(file_obj, 'name', f"file_{i}_{int(time.time())}"))
             log_accumulator.append(f"--- Processing image {i+1}/{len(files)}: {filename_for_log} ---")
             
-            # Обновляем UI перед началом обработки файла
-            progress.update(amount=(i+1)/len(files), desc=f"Img {i+1}/{len(files)}: {filename_for_log}")
-            yield (pd.DataFrame(all_results), None, None, None, None, 
-                   f"Processing image {i+1}/{len(files)}: {filename_for_log}", 
+            # Используем progress(float, desc=...)
+            progress( (i + 0.1) / len(files), desc=f"Img {i+1}/{len(files)}: Loading {filename_for_log}")
+            # Немедленно обновляем UI с логом перед тяжелой загрузкой изображения
+            yield (pd.DataFrame(all_results), None, None, 
+                   gr.File(visible=False), gr.File(visible=False),
+                   f"Loading image {i+1}/{len(files)}: {filename_for_log}", 
                    "\n".join(log_accumulator))
 
             img = Image.open(getattr(file_obj, 'name', str(file_obj)))
             if img.mode != "RGB": img = img.convert("RGB")
+            progress( (i + 0.3) / len(files), desc=f"Img {i+1}/{len(files)}: Scoring {filename_for_log}")
 
-            prompt, neg_prompt, model_n, model_h, other_p = extract_sd_parameters(img, filename_for_log, log_accumulator)
 
+            prompt, neg_prompt, model_n, model_h, other_p = extract_sd_parameters(img, filename_for_log, log_accumulator)
             reward = get_image_reward(img, filename_for_log, log_accumulator)
             anime_aes_deepghs = get_anime_aesthetic_score_deepghs(img, filename_for_log, log_accumulator)
             maniqa = get_maniqa_score(img, filename_for_log, log_accumulator)
             clip_val = calculate_clip_score_value(img, prompt, filename_for_log, log_accumulator)
             sdxl_detect = get_sdxl_detection_score(img, filename_for_log, log_accumulator)
             anime_ai_chk_deepghs = get_anime_ai_check_score_deepghs(img, filename_for_log, log_accumulator)
-            
             current_img_total_time_end = time.time()
             log_accumulator.append(f"INFO [{filename_for_log}]: Finished all scores (total for image: {current_img_total_time_end - current_img_total_time_start:.2f}s)")
 
-
             all_results.append({
                 "Filename": filename_for_log, "Prompt": prompt if prompt else "N/A", "Model Name": model_n, "Model Hash": model_h,
                 "ImageReward": reward, "AnimeAesthetic_dg": anime_aes_deepghs, "MANIQA_TQ": maniqa,
                 "CLIPScore": clip_val, "SDXL_Detector_AI_Prob": sdxl_detect, "AnimeAI_Check_dg_Prob": anime_ai_chk_deepghs,
             })
-            
-            # Обновляем UI после обработки каждого файла с текущими результатами
-            # Графики и файлы для скачивания будут генерироваться только в конце
-            # Но можно передавать df для обновления таблицы
             df_so_far = pd.DataFrame(all_results)
-            yield (df_so_far, None, None, None, None, # Пока без графиков и файлов
+            progress( (i + 1.0) / len(files), desc=f"Img {i+1}/{len(files)}: Done {filename_for_log}")
+            yield (df_so_far, None, None, 
+                   gr.File(visible=False), gr.File(visible=False),
                    f"Processed image {i+1}/{len(files)}: {filename_for_log}", 
                    "\n".join(log_accumulator))
-
         except Exception as e:
             log_accumulator.append(f"CRITICAL ERROR processing {filename_for_log}: {e}")
             print(f"CRITICAL ERROR processing {filename_for_log}: {e}")
@@ -387,23 +339,25 @@ def process_images_generator(files, progress=gr.Progress(track_tqdm=True)):
                 "CLIPScore": "Error", "SDXL_Detector_AI_Prob": "Error", "AnimeAI_Check_dg_Prob": "Error"
             })
             df_so_far = pd.DataFrame(all_results)
-            yield (df_so_far, None, None, None, None,
+            yield (df_so_far, None, None, 
+                   gr.File(visible=False), gr.File(visible=False),
                    f"Error on image {i+1}/{len(files)}: {filename_for_log}",
                    "\n".join(log_accumulator))
 
     log_accumulator.append("--- Generating final plots and download files ---")
-    yield (pd.DataFrame(all_results), None, None, None, None,
+    progress(1.0, desc="Generating final plots...")
+    yield (pd.DataFrame(all_results), None, None, 
+           gr.File(visible=False), gr.File(visible=False),
            "Generating final plots...",
            "\n".join(log_accumulator))
 
     df = pd.DataFrame(all_results)
     plot_model_avg_scores_buffer, plot_prompt_clip_scores_buffer = None, None
-    csv_buffer_val, json_buffer_val = "", ""
+    csv_file_path_out, json_file_path_out = None, None # Будем возвращать пути к файлам
 
     if not df.empty:
-        numeric_cols = ["ImageReward", "AnimeAesthetic_dg", "MANIQA_TQ", "CLIPScore"] # MANIQA TQ будет NaN, нормально
+        numeric_cols = ["ImageReward", "AnimeAesthetic_dg", "MANIQA_TQ", "CLIPScore"]
         for col in numeric_cols: df[col] = pd.to_numeric(df[col], errors='coerce')
-
         df_model_plot = df[(df["Model Name"] != "N/A") & (df["Model Name"].notna())]
         if not df_model_plot.empty and df_model_plot["Model Name"].nunique() > 0:
             try:
@@ -415,7 +369,6 @@ def process_images_generator(files, progress=gr.Progress(track_tqdm=True)):
                     plot_model_avg_scores_buffer = io.BytesIO(); fig1.savefig(plot_model_avg_scores_buffer, format="png"); plot_model_avg_scores_buffer.seek(0); plt.close(fig1)
                     log_accumulator.append("INFO: Model average scores plot generated.")
             except Exception as e: log_accumulator.append(f"ERROR: Failed to generate model average scores plot: {e}")
-
         df_prompt_plot = df[(df["Prompt"] != "N/A") & (df["Prompt"].notna()) & (df["CLIPScore"].notna())]
         if not df_prompt_plot.empty and df_prompt_plot["Prompt"].nunique() > 0 :
             try:
@@ -429,80 +382,62 @@ def process_images_generator(files, progress=gr.Progress(track_tqdm=True)):
                     log_accumulator.append("INFO: Prompt CLIP scores plot generated.")
             except Exception as e: log_accumulator.append(f"ERROR: Failed to generate prompt CLIP scores plot: {e}")
         
-        csv_b = io.StringIO(); df.to_csv(csv_b, index=False); csv_buffer_val = csv_b.getvalue()
-        json_b = io.StringIO(); df.to_json(json_b, orient='records', indent=4); json_buffer_val = json_b.getvalue()
-        log_accumulator.append("INFO: CSV and JSON data prepared for download.")
+        # Сохраняем файлы во временные файлы и возвращаем пути
+        try:
+            with tempfile.NamedTemporaryFile(mode="w+", delete=False, suffix=".csv", encoding='utf-8') as tmp_csv:
+                df.to_csv(tmp_csv, index=False)
+                csv_file_path_out = tmp_csv.name
+            with tempfile.NamedTemporaryFile(mode="w+", delete=False, suffix=".json", encoding='utf-8') as tmp_json:
+                df.to_json(tmp_json, orient='records', indent=4)
+                json_file_path_out = tmp_json.name
+            log_accumulator.append("INFO: CSV and JSON data prepared for download.")
+        except Exception as e:
+            log_accumulator.append(f"ERROR preparing download files: {e}")
+
 
-    final_status = f"Finished processing {len(all_results)} images. Total time: {sum(entry.get('total_time', 0) for entry in all_results):.2f}s (approx, if times were logged per image)"
-    # ^Это не совсем точно, т.к. total_time не собирается в entry, но идея понятна
+    final_status = f"Finished processing {len(all_results)} images."
     log_accumulator.append(final_status)
     
     yield (
         df,
-        gr.Image(value=plot_model_avg_scores_buffer, type="pil", visible=plot_model_avg_scores_buffer is not None),
-        gr.Image(value=plot_prompt_clip_scores_buffer, type="pil", visible=plot_prompt_clip_scores_buffer is not None),
-        gr.File(value=csv_buffer_val or None, label="Download CSV Results", visible=bool(csv_buffer_val), file_name="evaluation_results.csv"),
-        gr.File(value=json_buffer_val or None, label="Download JSON Results", visible=bool(json_buffer_val), file_name="evaluation_results.json"),
+        gr.Image(value=plot_model_avg_scores_buffer, visible=plot_model_avg_scores_buffer is not None),
+        gr.Image(value=plot_prompt_clip_scores_buffer, visible=plot_prompt_clip_scores_buffer is not None),
+        gr.File(value=csv_file_path_out, visible=csv_file_path_out is not None), # Убрали file_name
+        gr.File(value=json_file_path_out, visible=json_file_path_out is not None), # Убрали file_name
         final_status,
         "\n".join(log_accumulator)
     )
 
+import tempfile # Для gr.File
 
-# --- Интерфейс Gradio ---
 with gr.Blocks(css="footer {display: none !important}") as demo:
     gr.Markdown("# AI Image Model Evaluation Tool")
     gr.Markdown("Upload PNG images (ideally with Stable Diffusion metadata) to evaluate them...")
-    
-    with gr.Row():
-        image_uploader = gr.Files(
-            label="Upload Images (PNG)",
-            file_count="multiple",
-            file_types=["image"]
-        )
-    
+    with gr.Row(): image_uploader = gr.Files(label="Upload Images (PNG)", file_count="multiple", file_types=["image"])
     process_button = gr.Button("Evaluate Images", variant="primary")
-    
     status_textbox = gr.Textbox(label="Overall Status", interactive=False)
-    
-    log_output_textbox = gr.Textbox(label="Detailed Logs", lines=15, interactive=False, autoscroll=True) # Новый логгер
-
+    log_output_textbox = gr.Textbox(label="Detailed Logs", lines=15, interactive=False, autoscroll=True)
     gr.Markdown("## Evaluation Results Table")
     results_table = gr.DataFrame(headers=[
         "Filename", "Prompt", "Model Name", "Model Hash", "ImageReward", "AnimeAesthetic_dg",
         "MANIQA_TQ", "CLIPScore", "SDXL_Detector_AI_Prob", "AnimeAI_Check_dg_Prob"
     ], wrap=True)
-    
     with gr.Row():
-        download_csv_button = gr.File(label="Download CSV Results", interactive=False)
-        download_json_button = gr.File(label="Download JSON Results", interactive=False)
-    
+        download_csv_button = gr.File(label="Download CSV Results", interactive=False) # Будет обновляться из yield
+        download_json_button = gr.File(label="Download JSON Results", interactive=False) # Будет обновляться из yield
     gr.Markdown("## Visualizations")
     with gr.Row():
         plot_output_model_avg = gr.Image(label="Average Scores per Model", type="pil", interactive=False)
         plot_output_prompt_clip = gr.Image(label="Average CLIPScore per Prompt", type="pil", interactive=False)
-        
     process_button.click(
-        fn=process_images_generator, # Изменено на генератор
-        inputs=[image_uploader],
-        outputs=[
-            results_table, 
-            plot_output_model_avg, 
-            plot_output_prompt_clip,
-            download_csv_button,
-            download_json_button,
-            status_textbox,
-            log_output_textbox # Добавлен вывод для логов
-        ]
+        fn=process_images_generator, inputs=[image_uploader],
+        outputs=[results_table, plot_output_model_avg, plot_output_prompt_clip,
+                 download_csv_button, download_json_button, status_textbox, log_output_textbox]
     )
-    
     gr.Markdown("""**Metric Explanations:** ... (без изменений)""")
 
 if __name__ == "__main__":
-    # Загрузка моделей при старте (вне функции Gradio)
     print("--- Initializing models, please wait... ---")
-    # Вызов функций загрузки ONNX моделей, чтобы они кэшировались при старте, если возможно
-    # Это не будет выводить логи в UI, только в консоль сервера при запуске.
-    # Но поможет понять, загружаются ли они вообще.
     initial_dummy_logs = []
     if onnxruntime:
         get_onnx_session_and_meta(ANIME_AESTHETIC_REPO, ANIME_AESTHETIC_SUBFOLDER, initial_dummy_logs)
@@ -512,5 +447,4 @@ if __name__ == "__main__":
         for log_line in initial_dummy_logs: print(log_line)
         print("-----------------------------------------")
     print("--- Model initialization attempt complete. Launching Gradio. ---")
-    
-    demo.queue().launch(debug=True) # queue() важен для генераторов
+    demo.queue().launch(debug=True)