Update app.py

app.py

@@ -2,33 +2,61 @@ import gradio as gr
 import json
 import base64
 import os
+from huggingface_hub import upload_file
 
-def process(payload: dict):
+# --- Configuration for Hugging Face Hub Upload ---
+# The HF_TOKEN secret is automatically loaded into the environment by Hugging Face Spaces
+HF_TOKEN = os.environ.get("HF_TOKEN")
+
+# !! REPLACE THIS with your actual dataset repo ID (e.g., "Wauplin/my-image-data") !!
+HF_DATASET_REPO = "your-username/image_uploads" 
+
+def process_and_upload(payload: dict):
+    if not HF_TOKEN:
+        return {"error": "HF_TOKEN secret not found in Space settings."}
+        
     try:
         image_b64 = payload["image_b64"]
+        image_bytes = base64.b64decode(image_b64)
 
-        # 直接寫到 space sandbox
-        tmp_path = "/tmp/tmp.jpg"
+        # 1. Save temporarily to the local ephemeral storage (/tmp) first
+        # This file will be immediately deleted after the function finishes or the space restarts
+        local_tmp_path = "/tmp/uploaded_image.jpg"
+        with open(local_tmp_path, "wb") as f:
+            f.write(image_bytes)
 
-        with open(tmp_path, "wb") as f:
-            f.write(base64.b64decode(image_b64))
+        # 2. Upload the temporary file to the persistent Hugging Face Dataset Repo
+        # The path in the repo can be dynamic, e.g., using a timestamp
+        path_in_repo = f"images/uploaded_image_{len(image_bytes)}.jpg" 
+        
+        upload_file(
+            path_or_fileobj=local_tmp_path,
+            path_in_repo=path_in_repo,
+            repo_id=HF_DATASET_REPO,
+            token=HF_TOKEN,
+            repo_type="dataset", 
+        )
 
-        # 再讀取確認寫入成功
-        file_size = os.path.getsize(tmp_path)
+        # 3. Clean up the local temporary file
+        os.remove(local_tmp_path)
 
+        # 4. Return success message
         return {
-            "saved": True,
-            "file_path": tmp_path,
-            "file_size_bytes": file_size
+            "saved_to_hf_hub": True,
+            "repo_id": HF_DATASET_REPO,
+            "path_in_repo": path_in_repo,
+            "file_size_bytes": len(image_bytes)
         }
 
     except Exception as e:
-        return {"error": str(e)}
+        # Check the HF Space logs for full traceback if an error occurs
+        print(f"Upload failed: {e}") 
+        return {"error": f"Failed to upload to HF Hub: {str(e)}"}
 
 
 demo = gr.Interface(
-    fn=process,
-    inputs=gr.JSON(label="Input Payload (Dict format)"),
+    fn=process_and_upload,
+    inputs=gr.JSON(label="Input Payload (Dict format with 'image_b64')"),
     outputs=gr.JSON(label="Reply"),
     api_name="predict"
 )