Create app.py

app.py

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+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from transformers import pipeline
+import os
+from typing import Optional
+
+# FastAPIアプリの初期化
+app = FastAPI(
+    title="Hugging Face API on Spaces",
+    description="Hugging Face Transformersを使ったAPI",
+    version="1.0.0"
+)
+
+# リクエスト用のPydanticモデル
+class TextRequest(BaseModel):
+    text: str
+    max_length: Optional[int] = 100
+
+class SentimentResponse(BaseModel):
+    text: str
+    sentiment: str
+    confidence: float
+    model_name: str
+
+class GenerateResponse(BaseModel):
+    input_text: str
+    generated_text: str
+    model_name: str
+
+# グローバル変数でモデルを保持
+sentiment_classifier = None
+text_generator = None
+
+@app.on_event("startup")
+async def load_models():
+    """アプリ起動時にモデルをロード"""
+    global sentiment_classifier, text_generator
+    
+    print("モデルをロード中...")
+    
+    try:
+        # 感情分析モデル（軽量版を使用）
+        sentiment_classifier = pipeline(
+            "sentiment-analysis",
+            model="cardiffnlp/twitter-roberta-base-sentiment-latest"
+        )
+        
+        # テキスト生成モデル（軽量版）
+        text_generator = pipeline(
+            "text-generation",
+            model="distilgpt2"  # GPT-2より軽量
+        )
+        
+        print("✅ モデルのロードが完了しました")
+        
+    except Exception as e:
+        print(f"❌ モデルロードエラー: {e}")
+
+@app.get("/")
+async def root():
+    """ヘルスチェック用エンドポイント"""
+    return {
+        "message": "🤗 Hugging Face API is running on Spaces!",
+        "status": "healthy",
+        "endpoints": ["/sentiment", "/generate", "/models"]
+    }
+
+@app.post("/sentiment", response_model=SentimentResponse)
+async def analyze_sentiment(request: TextRequest):
+    """感情分析エンドポイント"""
+    try:
+        if sentiment_classifier is None:
+            raise HTTPException(
+                status_code=503, 
+                detail="Sentiment model not loaded. Please try again later."
+            )
+        
+        result = sentiment_classifier(request.text)
+        
+        return SentimentResponse(
+            text=request.text,
+            sentiment=result[0]["label"],
+            confidence=round(result[0]["score"], 4),
+            model_name="cardiffnlp/twitter-roberta-base-sentiment-latest"
+        )
+    
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Analysis failed: {str(e)}")
+
+@app.post("/generate", response_model=GenerateResponse)
+async def generate_text(request: TextRequest):
+    """テキスト生成エンドポイント"""
+    try:
+        if text_generator is None:
+            raise HTTPException(
+                status_code=503, 
+                detail="Text generation model not loaded. Please try again later."
+            )
+        
+        # Spacesの制限を考慮して短めに設定
+        max_length = min(request.max_length, 150)
+        
+        result = text_generator(
+            request.text,
+            max_length=max_length,
+            num_return_sequences=1,
+            temperature=0.7,
+            do_sample=True,
+            pad_token_id=text_generator.tokenizer.eos_token_id
+        )
+        
+        generated_text = result[0]["generated_text"]
+        
+        return GenerateResponse(
+            input_text=request.text,
+            generated_text=generated_text,
+            model_name="distilgpt2"
+        )
+    
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Generation failed: {str(e)}")
+
+@app.get("/models")
+async def get_models():
+    """利用可能なモデル情報を取得"""
+    return {
+        "sentiment_analysis": {
+            "model": "cardiffnlp/twitter-roberta-base-sentiment-latest",
+            "status": "loaded" if sentiment_classifier else "not loaded"
+        },
+        "text_generation": {
+            "model": "distilgpt2",
+            "status": "loaded" if text_generator else "not loaded"
+        },
+        "platform": "Hugging Face Spaces"
+    }
+
+@app.get("/health")
+async def health_check():
+    """詳細なヘルスチェック"""
+    return {
+        "status": "healthy",
+        "models": {
+            "sentiment": sentiment_classifier is not None,
+            "generation": text_generator is not None
+        },
+        "memory_usage": "optimized for Spaces"
+    }
+
+# Spaces用の追加設定
+if __name__ == "__main__":
+    import uvicorn
+    # Spacesでは通常ポート7860を使用
+    port = int(os.environ.get("PORT", 7860))
+    uvicorn.run(app, host="0.0.0.0", port=port)