Update app.py

app.py

@@ -1,62 +1,34 @@
-import os
-import gradio as gr
-from huggingface_hub import hf_hub_download, login
-import torch
+from fastapi import FastAPI, Request
+from fastapi.responses import JSONResponse
+import base64
+import io
+from PIL import Image
 
-try:
-    from safetensors import safe_open
-    SAFETENSORS_AVAILABLE = True
-except ImportError:
-    SAFETENSORS_AVAILABLE = False
-
-# Hugging Face Token（Secrets に登録しておく必要あり）
-HF_TOKEN = os.environ.get("HUGGINGFACE_HUB_TOKEN")
-if not HF_TOKEN:
-    raise RuntimeError("HUGGINGFACE_HUB_TOKEN が未設定です。SpacesのSecretsで設定してください。")
-
-login(token=HF_TOKEN)
-
-def check_model(repo_id, filename):
-    logs = []
-    try:
-        model_path = hf_hub_download(
-            repo_id=repo_id,
-            filename=filename,
-            token=HF_TOKEN
-        )
-        logs.append(f"📥 Downloaded: {model_path}")
-    except Exception as e:
-        return f"❌ Download failed: {str(e)}"
-
-    if SAFETENSORS_AVAILABLE and filename.endswith(".safetensors"):
-        try:
-            with safe_open(model_path, framework="pt", device="cpu") as f:
-                logs.append("✅ safetensors loaded successfully")
-                logs.append("Keys: " + ", ".join(list(f.keys())[:10]))
-                return "\n".join(logs)
-        except Exception as e:
-            logs.append(f"⚠ safetensors error: {e}")
+app = FastAPI()
 
+@app.post("/generate")
+async def generate_image(request: Request):
     try:
-        sd = torch.load(model_path, map_location="cpu")
-        logs.append("✅ torch.load OK")
-        if isinstance(sd, dict):
-            logs.append("Top-level keys: " + ", ".join(list(sd.keys())[:10]))
-        else:
-            logs.append(f"Loaded object type: {type(sd)}")
+        body = await request.json()
+        base64_image = body.get("image")
+        prompt = body.get("prompt", "")
+
+        # Base64文字列 → PIL画像
+        image_data = base64.b64decode(base64_image.split(",")[-1])
+        image = Image.open(io.BytesIO(image_data)).convert("RGB")
+
+        # 再エンコードしてBase64で返す
+        buffered = io.BytesIO()
+        image.save(buffered, format="PNG")
+        encoded_img = base64.b64encode(buffered.getvalue()).decode("utf-8")
+
+        return JSONResponse(content={
+            "success": True,
+            "echo_prompt": prompt,
+            "image_base64": f"data:image/png;base64,{encoded_img}"
+        })
     except Exception as e:
-        logs.append(f"❌ torch.load failed: {e}")
-    return "\n".join(logs)
-
-demo = gr.Interface(
-    fn=check_model,
-    inputs=[
-        gr.Textbox(label="Repo ID", value="revi13/ip-adapter-faceid-private"),
-        gr.Textbox(label="Filename", value="ip-adapter-faceid-plusv2_sd15.bin")
-    ],
-    outputs=gr.Text(label="Result"),
-    title="🧪 Hugging Face Hub モデル重みファイル検証ツール",
-    description="指定した repo_id / filename のモデル重みをダウンロードして torch.load/safetensors で検証します。"
-)
-
-demo.launch()
+        return JSONResponse(status_code=500, content={
+            "success": False,
+            "error": str(e)
+        })