tidy wandb link and drop tab

app.py

@@ -684,11 +684,11 @@ def fetch_runs_for_ui():
             return pd.DataFrame(
                 {
                     "Status": [],
+                    "W&B Link": [],
                     "Project": [],
                     "Task": [],
                     "Model": [],
                     "Created": [],
-                    "W&B Link": [],
                     "Run ID": [],
                 }
             )
@@ -697,23 +697,21 @@ def fetch_runs_for_ui():
         for run in reversed(runs):  # Newest first
             wandb_link = ""
             if run.get("wandb_url"):
-                wandb_link = (
-                    f'<a href="{run["wandb_url"]}" target="_blank">View W&B</a>'
-                )
+                wandb_link = f"[📊 W&B Run]({run['wandb_url']})"
 
             data.append(
                 {
                     "Status": f"{get_status_emoji(run['status'])} {run['status'].title()}",
+                    "W&B Link": wandb_link,
                     "Project": run["project_name"],
                     "Task": run["task"].replace("-", " ").title(),
                     "Model": run["base_model"],
                     "Created": run["created_at"][:16].replace("T", " "),
-                    "W&B Link": wandb_link,
                     "Run ID": run["run_id"][:8] + "...",
                 }
             )
 
-        return pd.DataFrame(data)
+        return pd.DataFrame(data, datatype="markdown")
 
     except Exception as e:
         return pd.DataFrame({"Error": [f"Failed to fetch runs: {str(e)}"]})
@@ -808,168 +806,11 @@ with gr.Blocks(
         # Dashboard Tab
         with gr.Tab("📊 Dashboard"):
             with gr.Row():
-                with gr.Column(scale=3):
-                    gr.Markdown("## Training Runs")
-                    refresh_btn = gr.Button("🔄 Refresh", variant="secondary")
-                    runs_table = gr.Dataframe(
-                        value=fetch_runs_for_ui(), interactive=False
-                    )
-
-                with gr.Column(scale=1):
-                    gr.Markdown("## Quick Stats")
-                    stats = gr.Textbox(
-                        value=get_system_status(), interactive=False, lines=15
-                    )
-
-        # Start Training Tab
-        with gr.Tab("🏃 Start Training"):
-            gr.Markdown("## Submit New Training Job")
-
-            gr.Markdown("""
-            💡 **Hub Integration**: Enable "Push to Hub" to automatically upload your trained model to Hugging Face Hub.
-            Requires `HF_USERNAME` and `HF_TOKEN` environment variables.
-            """)
-
-            with gr.Row():
-                with gr.Column():
-                    task_dropdown = gr.Dropdown(
-                        choices=[
-                            "text-classification",
-                            "llm-sft",
-                            "llm-dpo",
-                            "llm-orpo",
-                            "image-classification",
-                        ],
-                        label="Task Type",
-                        value="text-classification",
-                    )
-
-                    project_name = gr.Textbox(
-                        label="Project Name", placeholder="my-training-project"
-                    )
-
-                    base_model = gr.Textbox(
-                        label="Base Model", placeholder="distilbert-base-uncased"
-                    )
-
-                    dataset_path = gr.Textbox(label="Dataset Path", placeholder="imdb")
-
-                with gr.Column():
-                    epochs = gr.Slider(1, 20, value=3, step=1, label="Epochs")
-                    batch_size = gr.Slider(1, 128, value=16, step=1, label="Batch Size")
-                    learning_rate = gr.Number(value=2e-5, label="Learning Rate")
-                    backend = gr.Dropdown(
-                        choices=["local", "spaces-t4-small", "spaces-a10g-large"],
-                        label="Backend",
-                        value="local",
-                    )
-
-            with gr.Row():
-                with gr.Column():
-                    push_to_hub = gr.Checkbox(label="Push to Hub", value=False)
-                    hub_repo_id = gr.Textbox(
-                        label="Hub Repository ID", placeholder="your-repo-id"
-                    )
-
-            submit_btn = gr.Button("🚀 Start Training", variant="primary", size="lg")
-            submit_output = gr.Textbox(label="Status", interactive=False, lines=10)
-
-        # MCP Info Tab
-        with gr.Tab("🔗 MCP Integration"):
-            # Extract nested expressions to avoid f-string nesting in Gradio markdown
-            total_runs = len(load_runs())
-            wandb_auth = (
-                "✅ Configured"
-                if os.environ.get("WANDB_API_KEY")
-                else "❌ Missing WANDB_API_KEY"
-            )
-            hub_auth = (
-                "✅ Configured" if os.environ.get("HF_TOKEN") else "❌ Missing HF_TOKEN"
-            )
-
-            gr.Markdown(f"""
-            ## MCP Server Information
-            
-            This Gradio app automatically serves as an MCP server.
-            
-            **MCP Endpoint:** `http://SPACE_URL/gradio_api/mcp/sse`
-            **MCP Schema:** `http://SPACE_URL/gradio_api/mcp/schema`
-            
-            ### Available MCP Tools:
-            
-            - `start_training_job` - Submit new training jobs (includes Hub push)
-            - `get_training_runs` - List all runs with status
-            - `get_run_details` - Get detailed run information  
-            - `get_task_recommendations` - Get training recommendations
-            - `get_system_status` - Check system status
-            
-            ### 📊 Weights & Biases Integration:
-            
-            For **complete training metrics** (loss, accuracy, etc.), set:
-            
-            ```bash
-            export WANDB_API_KEY="your-wandb-api-key"
-            export WANDB_PROJECT="autotrain-mcp"  # Optional: custom project name
-            ```
-            
-            Get your API key from: https://wandb.ai/authorize
-            
-            **What gets logged by AutoTrain:**
-            - ✅ Training/validation loss
-            - ✅ Learning rate schedule  
-            - ✅ Gradient norms
-            - ✅ Model checkpoints
-            - ✅ System metrics (GPU, CPU, memory)
-            
-            ### 🤗 Hugging Face Hub Integration:
-            
-            To push models to the Hub, set these environment variables:
-            
-            ```bash
-            export HF_USERNAME="your-hf-username"
-            export HF_TOKEN="your-hf-write-token"
-            ```
-            
-            Get your token from: https://huggingface.co/settings/tokens
-            
-            **Usage Examples:**
-            - `push_to_hub="true"` - Push to Hub using project name as repo
-            - `hub_repo_id="my-org/my-model"` - Push to custom repository
-            
-            ### Connection to the MCP Server
-            
-            Connect to it like this:
-    
-            ```javascript
-            {"mcpServers": {"autotrain": {"url": "http://SPACE_URL/gradio_api/mcp/sse",
-                        "headers": {"Authorization": "Bearer <YOUR-HUGGING-FACE-TOKEN>"}
-                    }
-                }
-            }
-            ```
-            
-            Or like this for Claude Desktop:
-            
-            ```javascript
-            {"mcpServers": {"autotrain": {"command": "npx",
-                        "args": [
-                            "mcp-remote",
-                            "http://SPACE_URL/gradio_api/mcp/sse",
-                            "--header",
-                            "Authorization: Bearer <YOUR-HUGGING-FACE-TOKEN>"
-                        ]
-                    }
-                }
-            }
-            ```
-            
-            ### Current Stats:
-            
-            Total Runs: {total_runs}
-            W&B Project: {WANDB_PROJECT}
-            W&B Auth: {wandb_auth}
-            Hub Auth: {hub_auth}
-            """)
+                gr.Markdown("## Training Runs")
+                refresh_btn = gr.Button("🔄 Refresh", variant="secondary")
+                runs_table = gr.Dataframe(
+                    value=fetch_runs_for_ui(), interactive=False, datatype="markdown"
+                )
 
         # MCP Tools Tab
         with gr.Tab("🔧 MCP Tools"):