Update app.py

app.py

@@ -6,9 +6,6 @@ import io
 import gradio as gr
 from gradio_client import Client
 from dotenv import load_dotenv
-from rich.console import Console
-from rich.table import Table
-from rich import box
 
 # Load environment variables
 load_dotenv()
@@ -18,47 +15,15 @@ HF_TOKEN = os.environ.get("HF_CV_ROBOT_TOKEN")
 HF_SPACE = "OppaAI/Robot_MCP_Server"
 API_NAME = "/predict"
 
-console = Console()
-
-
-def pretty_print_response(resp: dict):
-    """Rich table output with row lines, no URL."""
-    table = Table(
-        title="😎 Robot Vision Result",
-        title_style="bold cyan",
-        title_justify="left",
-        box=box.ROUNDED,
-        show_lines=True,
-        show_header=False,
-        style="bold cyan"
-    )
-
-    objects_list = resp.get("objects", [])
-    objects_str = ", ".join(objects_list) if isinstance(objects_list, list) else str(objects_list)
-
-    table.add_column("Field", style="bold magenta")
-    table.add_column("Value", style="white")
-
-    table.add_row("🤖 Robot ID", str(resp.get("robot_id", "N/A")))
-    table.add_row("🏞️  Image Size", str(resp.get("file_size_bytes", "N/A")))
-    table.add_row("📝 Description", str(resp.get("description", "N/A")))
-    table.add_row("👥 Human", str(resp.get("human", "N/A")))
-    table.add_row("📦 Objects", objects_str)
-    table.add_row("🏛️  Environment", str(resp.get("environment", "N/A")))
-
-    console.print(table)
-    return resp.get("description", ""), resp.get("human", ""), objects_str, resp.get("environment", "")
-
 
 def process_webcam_stream(image):
     """Send webcam image to HF MCP Server and get result"""
     if image is None:
-        return None, None, None, None
+        return "", "", "", ""
 
     # Convert PIL Image to base64
-    import io
     buffered = io.BytesIO()
-    image.save(buffered, format="JPEG")  # <--- directly save PIL Image
+    image.save(buffered, format="JPEG")
     b64_img = base64.b64encode(buffered.getvalue()).decode("utf-8")
 
     # Prepare payload
@@ -73,19 +38,18 @@ def process_webcam_stream(image):
     client = Client(HF_SPACE)
     try:
         resp = client.predict(payload, api_name=API_NAME)
-        # Print table in console
-        pretty_print_response(resp)
+        objects_list = resp.get("objects", [])
+        objects_str = ", ".join(objects_list) if isinstance(objects_list, list) else str(objects_list)
 
-        # Return selected fields for Gradio display
         return (
             resp.get("description", ""),
             resp.get("human", ""),
-            ", ".join(resp.get("objects", [])),
+            objects_str,
             resp.get("environment", "")
         )
     except Exception as e:
-        console.print(f"[bold red]Error sending to HF:[/bold red] {e}")
-        return None, None, None, None
+        return f"Error: {e}", "", "", ""
+
 
 with gr.Blocks() as demo:
     gr.Markdown("## 🎥 Robot Vision Webcam Stream")
@@ -93,7 +57,7 @@ with gr.Blocks() as demo:
     with gr.Row():
         webcam_input = gr.Image(
             label="Captured from Web-Cam",
-            sources=['upload', 'webcam'],
+            sources=["upload", "webcam"],
             type="pil"
         )
         description_out = gr.Textbox(label="Description")
@@ -108,8 +72,5 @@ with gr.Blocks() as demo:
         stream_every=0.5
     )
 
-demo.launch()
-
-
 if __name__ == "__main__":
     demo.launch()