Update app.py

app.py

@@ -1,67 +1,38 @@
 import gradio as gr
 import json
 import base64
-from io import BytesIO
-import requests
 import os
 
-HF_TOKEN = os.environ.get("HF_CV_ROBOT_TOKEN")
-MODEL = "Qwen/Qwen2.5-VL-7B-Instruct"
-
 def process(payload: dict):
     try:
         image_b64 = payload["image_b64"]
-        robot_id = payload.get("robot_id", "unknown")
 
-        # Base64 → Bytes
+        # Base64 → bytes
         img_bytes = base64.b64decode(image_b64)
 
-        # multipart file
-        files = {
-            "file": ("image.jpg", BytesIO(img_bytes), "image/jpeg")
-        }
-
-        # Router 要求的 payload 格式（不含 image_data）
-        data = {
-            "model": MODEL,
-            "messages": [
-                {
-                    "role": "user",
-                    "content": [
-                        {"type": "text", "text": "Describe this image in detail."},
-                        {"type": "file", "file": "image.jpg"}
-                    ]
-                }
-            ]
-        }
-
-        resp = requests.post(
-            "https://router.huggingface.co/v1/chat/completions",
-            headers={"Authorization": f"Bearer {HF_TOKEN}"},
-            data={"payload": json.dumps(data)},
-            files=files,
-            timeout=60
-        )
-
-        if resp.status_code != 200:
-            return {"error": f"VLM API error: {resp.status_code}, {resp.text}"}
-
-        out = resp.json()
-        txt = out["choices"][0]["message"]["content"][0]["text"]
-
-        return {
-            "received": True,
-            "robot_id": robot_id,
-            "vllm_analysis": txt
-        }
+        # Save to local file
+        tmp_path = "tmp.jpg"
+        with open(tmp_path, "wb") as f:
+            f.write(img_bytes)
+
+        # Check file exists + return success
+        if os.path.exists(tmp_path):
+            size = os.path.getsize(tmp_path)
+            return {
+                "saved": True,
+                "path": tmp_path,
+                "file_size_bytes": size
+            }
+        else:
+            return {"saved": False, "error": "File not found after save."}
 
     except Exception as e:
-        return {"error": str(e)}
+        return {"saved": False, "error": str(e)}
 
 demo = gr.Interface(
     fn=process,
-    inputs=gr.JSON(label="Input Payload (Dict)"),
-    outputs=gr.JSON(label="Reply to Jetson"),
+    inputs=gr.JSON(label="Input Payload (Dict format)"),
+    outputs=gr.JSON(label="Reply"),
     api_name="predict"
 )