Update app.py

app.py

@@ -5,93 +5,77 @@ from io import BytesIO
 import requests
 import os
 
-# HF token & model
 HF_TOKEN = os.environ.get("HF_CV_ROBOT_TOKEN")
-MODEL = "Qwen/Qwen2.5-VL-7B-Instruct"  # 確認此模型有支援 VLM (目前有)
+MODEL = "Qwen/Qwen2.5-VL-7B-Instruct"
 
 if not HF_TOKEN:
     print("ERROR: HF_CV_ROBOT_TOKEN environment variable not set.")
 
-# -------------------------------
-# 主處理函數
-# -------------------------------
 def process(payload: dict):
     try:
         if not HF_TOKEN:
-            return {"error": "Hugging Face token is missing. Please check Space secrets."}
-        
+            return {"error": "Missing HF token"}
+
         robot_id = payload.get("robot_id", "unknown")
         image_b64 = payload["image_b64"]
 
-        # ------------------------------------------------
-        # ⭐ 1) Base64 → 圖檔並存成 temp.jpg
-        # ------------------------------------------------
+        # Save Base64 → temp file
         img_bytes = base64.b64decode(image_b64)
         temp_path = "temp.jpg"
         with open(temp_path, "wb") as f:
             f.write(img_bytes)
 
-        # ------------------------------------------------
-        # ⭐ 2) JSON 部分（只放文字）
-        # ------------------------------------------------
-        data = {
-            "model": MODEL,
-            "messages": [
-                {
-                    "role": "user",
-                    "content": [
-                        {"type": "text", "text": "Describe this image in detail."}
-                    ]
-                }
-            ]
-        }
+        # HF Router expects:
+        # data = model + messages (string)
+        # files = file
+        messages_json = json.dumps([
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": "Describe this image in detail."}
+                ]
+            }
+        ])
 
-        # ------------------------------------------------
-        # ⭐ 3) 用 multipart/form-data 傳送 image + JSON payload
-        # ------------------------------------------------
         resp = requests.post(
             "https://router.huggingface.co/v1/chat/completions",
             headers={"Authorization": f"Bearer {HF_TOKEN}"},
-            data={"payload": json.dumps(data)},
-            files={"file": ("image.jpg", open(temp_path, "rb"), "image/jpeg")},
+            data={
+                "model": MODEL,               # <- 放在這裡才對！
+                "messages": messages_json     # <- 必須是字串
+            },
+            files={
+                "file": ("image.jpg", open(temp_path, "rb"), "image/jpeg")
+            },
             timeout=60
         )
 
-        # ------------------------------------------------
-        # ⭐ 4) 處理回應
-        # ------------------------------------------------
         if resp.status_code != 200:
             print(f"VLM API error: {resp.status_code}, {resp.text}")
             return {"error": f"VLM API error: {resp.status_code}, {resp.text}"}
 
-        # 正常解析內容
-        try:
-            content = resp.json()["choices"][0]["message"]["content"]
-            # content 是 array，找出 text
-            vlm_text = ""
-            for part in content:
-                if part.get("type") == "text":
-                    vlm_text += part["text"]
-        except Exception as e:
-            return {"error": f"Failed to parse VLM response: {e}, Response text: {resp.text}"}
+        # Parse
+        out = resp.json()
+        parts = out["choices"][0]["message"]["content"]
+
+        text = ""
+        for p in parts:
+            if p["type"] == "text":
+                text += p["text"]
 
         return {
             "received": True,
             "robot_id": robot_id,
-            "vllm_analysis": vlm_text.strip()
+            "vllm_analysis": text
         }
 
     except Exception as e:
-        print(f"Unexpected error: {e}")
         return {"error": str(e)}
 
-# -------------------------------
-# Gradio MCP Server
-# -------------------------------
 demo = gr.Interface(
     fn=process,
-    inputs=gr.JSON(label="Input Payload (Dict format)"),
-    outputs=gr.JSON(label="Reply to Jetson"),
+    inputs=gr.JSON(label="Input Payload"),
+    outputs=gr.JSON(label="Reply"),
     api_name="predict"
 )