.

app.py

@@ -1,5 +1,5 @@
 import gradio as gr
-from huggingface_hub import AsyncInferenceClient
+from huggingface_hub import InferenceClient
 from typing import List, Dict, Optional, Union
 import logging
 from dataclasses import dataclass
@@ -100,7 +100,7 @@ class LocalModelManager:
                 logger.error(f"Error preloading model {model_path}: {str(e)}")
                 # 続行するが、エラーをログに記録
 
-    async def load_model(self, model_path: str, task: str = "text-generation"):
+    def load_model(self, model_path: str, task: str = "text-generation"):
         """モデルが既にロードされているか確認し、なければロード"""
         if model_path not in self.pipelines:
             logger.info(f"Loading model on demand: {model_path}")
@@ -142,10 +142,10 @@ class LocalModelManager:
         )
         return outputs[0]["generated_text"]
 
-    async def generate_text(self, model_path: str, text: str) -> str:
-        """テキスト生成の実行（非同期ラッパー）"""
-        if model_path not in self.models:
-            await self.load_model(model_path, "text-generation")
+    def generate_text(self, model_path: str, text: str) -> str:
+        """テキスト生成の実行"""
+        if model_path not in self.pipelines:
+            self.load_model(model_path, "text-generation")
         
         try:
             return self._generate_text_sync(self.pipelines[model_path], text)
@@ -159,10 +159,10 @@ class LocalModelManager:
         result = pipeline(text)
         return str(result)
 
-    async def classify_text(self, model_path: str, text: str) -> str:
-        """テキスト分類の実行（非同期ラッパー）"""
-        if model_path not in self.models:
-            await self.load_model(model_path, "text-classification")
+    def classify_text(self, model_path: str, text: str) -> str:
+        """テキスト分類の実行"""
+        if model_path not in self.pipelines:
+            self.load_model(model_path, "text-classification")
         
         try:
             return self._classify_text_sync(self.pipelines[model_path], text)
@@ -181,7 +181,7 @@ class ModelManager:
         """Inference APIクライアントの初期化"""
         for model in TEXT_GENERATION_MODELS + CLASSIFICATION_MODELS:
             if model.type == ModelType.INFERENCE_API and model.model_id:
-                self.api_clients[model.model_id] = AsyncInferenceClient(
+                self.api_clients[model.model_id] = InferenceClient(
                     model.model_id,
                     token=True  # これによりHFトークンを使用
                 )
@@ -206,7 +206,7 @@ class ModelManager:
         # 事前ロード実行
         self.local_manager.preload_models(models_to_preload, tasks)
 
-    async def run_text_generation(self, text: str, selected_types: List[str]) -> List[str]:
+    def run_text_generation(self, text: str, selected_types: List[str]) -> List[str]:
         """テキスト生成モデルの実行"""
         results = []
         for model in TEXT_GENERATION_MODELS:
@@ -214,20 +214,20 @@ class ModelManager:
                 try:
                     if model.type == ModelType.INFERENCE_API:
                         logger.info(f"Running API text generation: {model.name}")
-                        response = await self.api_clients[model.model_id].text_generation(
+                        response = self.api_clients[model.model_id].text_generation(
                             text, max_new_tokens=100, temperature=0.7
                         )
                         results.append(f"{model.name}: {response}")
                     else:
                         logger.info(f"Running local text generation: {model.name}")
-                        response = await self.local_manager.generate_text(model.model_path, text)
+                        response = self.local_manager.generate_text(model.model_path, text)
                         results.append(f"{model.name}: {response}")
                 except Exception as e:
                     logger.error(f"Error in {model.name}: {str(e)}")
                     results.append(f"{model.name}: Error - {str(e)}")
         return results
 
-    async def run_classification(self, text: str, selected_types: List[str]) -> List[str]:
+    def run_classification(self, text: str, selected_types: List[str]) -> List[str]:
         """分類モデルの実行"""
         results = []
         for model in CLASSIFICATION_MODELS:
@@ -235,11 +235,11 @@ class ModelManager:
                 try:
                     if model.type == ModelType.INFERENCE_API:
                         logger.info(f"Running API classification: {model.name}")
-                        response = await self.api_clients[model.model_id].text_classification(text)
+                        response = self.api_clients[model.model_id].text_classification(text)
                         results.append(f"{model.name}: {response}")
                     else:
                         logger.info(f"Running local classification: {model.name}")
-                        response = await self.local_manager.classify_text(model.model_path, text)
+                        response = self.local_manager.classify_text(model.model_path, text)
                         results.append(f"{model.name}: {response}")
                 except Exception as e:
                     logger.error(f"Error in {model.name}: {str(e)}")
@@ -349,10 +349,10 @@ class ToxicityApp:
                 updates.append(gr.update(visible=visible))
         return updates
 
-    async def handle_invoke(self, text: str, selected_types: List[str]) -> List[str]:
+    def handle_invoke(self, text: str, selected_types: List[str]) -> List[str]:
         """Invokeボタンのハンドラ"""
-        gen_results = await self.model_manager.run_text_generation(text, selected_types)
-        class_results = await self.model_manager.run_classification(text, selected_types)
+        gen_results = self.model_manager.run_text_generation(text, selected_types)
+        class_results = self.model_manager.run_classification(text, selected_types)
         
         # 結果リストの長さを調整
         gen_results.extend([""] * (len(TEXT_GENERATION_MODELS) - len(gen_results)))

requirements.txt

@@ -1,4 +1,3 @@
 transformers>=4.30.0
 torch==2.4.0
 accelerate>=0.26.0
-aiohttp>=3.9.0