Update app.py

app.py

@@ -12,10 +12,9 @@ HF_DATASET_REPO = "OppaAI/Robot_MCP"
 HF_VLM_MODEL = "Qwen/Qwen2.5-VL-7B-Instruct" 
 
 # --- Helper Functions ---
-def save_and_upload_image(image_b64):
+def save_and_upload_image(image_b64, hf_token):
     """Save image to /tmp and upload to HF dataset."""
     image_bytes = base64.b64decode(image_b64)
-    # Use a unique filename to prevent conflicts in /tmp
     local_tmp_path = f"/tmp/tmp.jpg" 
     with open(local_tmp_path, "wb") as f:
         f.write(image_bytes)
@@ -25,13 +24,14 @@ def save_and_upload_image(image_b64):
         path_or_fileobj=local_tmp_path,
         path_in_repo=path_in_repo,
         repo_id=HF_DATASET_REPO,
-        token=HF_TOKEN,
+        token=hf_token,  # ← use token from payload
         repo_type="dataset"
     )
 
     hf_image_url = f"https://huggingface.co/datasets/{HF_DATASET_REPO}/resolve/main/{path_in_repo}"
     return local_tmp_path, hf_image_url, path_in_repo, len(image_bytes)
 
+
 # --- Main MCP function ---
 def process_and_describe(payload: dict):
     try:
@@ -44,7 +44,7 @@ def process_and_describe(payload: dict):
         image_b64 = payload["image_b64"]
 
         # 2️⃣ Save image temporarily (for tracking)
-        local_tmp_path, hf_url, path_in_repo, size_bytes = save_and_upload_image(image_b64)
+        local_tmp_path, hf_url, path_in_repo, size_bytes = save_and_upload_image(image_b64, hf_token)
 
         # 3️⃣ Initialize HF client per request
         hf_client = InferenceClient(token=hf_token)