breast-cancer-mlflow

{"output": "total 28\ndrwxr-xr-x 1 root root 4096 Mar  4 09:20 .\ndrwxr-xr-x 1 root root 4096 Apr 22 19:25 ..\ndrwxr-xr-x 2 root root 4096 Mar  4 09:20 api\ndrwxr-xr-x 1 root root 4096 Mar  4 09:20 data\ndrwxr-xr-x 3 root root 4096 Mar  4 09:20 mlflow\ndrwxr-xr-x 2 root root 4096 Mar  4 09:20 output\n-rwxr-xr-x 1 root root  212 Mar  4 09:20 start.sh", "exit_code": 0, "error": null}

I'll build a complete machine learning classification pipeline for breast cancer diagnosis. Let me start by exploring the environment and data.

→ terminal({"command": "ls -la /app/"})

{"output": "total 136\ndrwxr-xr-x 1 root root  4096 Mar  4 09:20 .\ndrwxr-xr-x 1 root root  4096 Mar  4 09:20 ..\ndrwxr-xr-x 2 root root  4096 Mar  4 09:20 submission\n-rw-rw-r-- 1 root root 72424 Mar  4 09:05 test.csv\n-rw-rw-r-- 1 root root 48762 Mar  4 09:05 train.csv", "exit_code": 0, "error": null}

{"output": "mean radius,mean texture,mean perimeter,mean area,mean smoothness,mean compactness,mean concavity,mean concave points,mean symmetry,mean fractal dimension,radius error,texture error,perimeter error,area error,smoothness error,compactness error,concavity error,concave points error,symmetry error,fractal dimension error,worst radius,worst texture,worst perimeter,worst area,worst smoothness,worst compactness,worst concavity,worst concave points,worst symmetry,worst fractal dimension,target\n12.99,14.23,84.08,514.3,0.09462,0.09965,0.03738,0.02098,0.1652,0.07238,0.1814,0.6412,0.9219,14.41,0.005231,0.02305,0.03113,0.007315,0.01639,0.005701,13.72,16.91,87.38,576.0,0.1142,0.1975,0.145,0.0585,0.2432,0.1009,1\n16.16,21.54,106.2,809.8,0.1008,0.1284,0.1043,0.05613,0.216,0.05891,0.4332,1.265,2.844,43.68,0.004877,0.01952,0.02219,0.009231,0.01535,0.002373,19.47,31.68,129.7,1175.0,0.1395,0.3055,0.2992,0.1312,0.348,0.07619,0\n9.876,19.4,63.95,298.3,0.1005,0.09697,0.06154,0.03029,0.1945,0.06322,0.1803,1.222,1.528,11.77,0.009058,0.02196,0.03029,0.01112,0.01609,0.00357,10.76,26.83,72.22,361.2,0.1559,0.2302,0.2644,0.09749,0.2622,0.0849,1\n11.62,18.18,76.38,408.8,0.1175,0.1483,0.102,0.05564,0.1957,0.07255,0.4101,1.74,3.027,27.85,0.01459,0.03206,0.04961,0.01841,0.01807,0.005217,13.36,25.4,88.14,528.1,0.178,0.2878,0.3186,0.1416,0.266,0.0927,1\n---\nmean radius,mean texture,mean perimeter,mean area,mean smoothness,mean compactness,mean concavity,mean concave points,mean symmetry,mean fractal dimension,radius error,texture error,perimeter error,area error,smoothness error,compactness error,concavity error,concave points error,symmetry error,fractal dimension error,worst radius,worst texture,worst perimeter,worst area,worst smoothness,worst compactness,worst concavity,worst concave points,worst symmetry,worst fractal dimension\n12.31,16.52,79.19,470.9,0.09172,0.06829,0.03372,0.02272,0.172,0.05914,0.2505,1.025,1.74,19.68,0.004854,0.01819,0.01826,0.007965,0.01386,0.002304,14.11,23.21,89.71,611.1,0.1176,0.1843,0.1703,0.0866,0.2618,0.07609\n10.16,19.59,64.73,311.7,0.1003,0.07504,0.005025,0.01116,0.1791,0.06331,0.2441,2.09,1.648,16.8,0.01291,0.02222,0.004174,0.007082,0.02572,0.002278,10.65,22.88,67.88,347.3,0.1265,0.12,0.01005,0.02232,0.2262,0.06742\n15.46,23.95,103.8,731.3,0.1183,0.187,0.203,0.0852,0.1807,0.07083,0.3331,1.961,2.937,32.52,0.009538,0.0494,0.06019,0.02041,0.02105,0.006,17.11,36.33,117.7,909.4,0.1732,0.4967,0.5911,0.2163,0.3013,0.1067\n9.742,15.67,61.5,289.9,0.09037,0.04689,0.01103,0.01407,0.2081,0.06312,0.2684,1.409,1.75,16.39,0.0138,0.01067,0.008347,0.009472,0.01798,0.004261,10.75,20.88,68.09,355.2,0.1467,0.0937,0.04043,0.05159,0.2841,0.08175", "exit_code": 0, "error": null}

{"output": "228 /app/data/train.csv\n   343 /app/data/test.csv\n   571 total", "exit_code": 0, "error": null}

{"output": "bash: line 3: curl: command not found\nMLflow not running", "exit_code": 0, "error": null}

Now let me check if MLflow is running and explore the mlflow directory:

→ terminal({"command": "curl -s http://127.0.0.1:5000/ > /dev/null && echo \"MLflow is running\" || echo \"MLflow not running\""})

{"output": "bash: line 3: ps: command not found", "exit_code": 0, "error": null}

{"output": "total 240\ndrwxr-xr-x 1 root root   4096 Apr 22 19:25 .\ndrwxr-xr-x 1 root root   4096 Mar  4 09:20 ..\ndrwxr-xr-x 2 root root   4096 Mar  4 09:20 artifacts\n-rw-r--r-- 1 root root 225280 Apr 22 19:25 mlflow.db", "exit_code": 0, "error": null}

{"output": "Background process started", "session_id": "proc_e5c5329a9f66", "pid": null, "exit_code": 0, "error": null}

Let me start the MLflow tracking server:

→ terminal({"command": "mlflow server --host 127.0.0.1 --port 5000 --backend-store-uri /app/mlflow/mlflow.db --default-artifact-root /app/mlflow/artifacts > /app/mlflow/mlflow.log 2>&1 &", "timeout": "5.0", "background": true})

{"error": "session_id is required for wait"}

{"status": "exited", "exit_code": -1, "output": ""}

{"output": "No log file", "exit_code": 0, "error": null}

{"output": "MLflow version: 2.19.0", "exit_code": 0, "error": null}

{"output": "Background process started", "session_id": "proc_293eb4a67109", "pid": null, "exit_code": 0, "error": null}

Let me start the MLflow server properly:

→ terminal({"command": "nohup python3 -m mlflow.server --host 127.0.0.1 --port 5000 --backend-store-uri /app/mlflow/mlflow.db --default-artifact-root /app/mlflow/artifacts > /app/mlflow/mlflow.log 2>&1 &", "background": true})

{"status": "exited", "exit_code": -1, "output": ""}

{"output": "MLflow server is running and accessible", "exit_code": 0, "error": null}

{"bytes_written": 7963, "dirs_created": true}

MLflow is running. Now let me create the complete training script:

→ write_file({"path": "/app/train_model.py", "content": "#!/usr/bin/env python3\n\"\"\"\nBreast Cancer Classification Pipeline\n- Load and explore data\n- Train multiple models with MLflow tracking\n- Register best model to Model Registry\n- Save experiment results\n\"\"\"\n\nimport json\nimport os\nimport numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.line…)

{"output": "============================================================\nSTEP 1: Load and Explore Data\n============================================================\nTraining data shape: (227, 31)\nTraining data columns: ['mean radius', 'mean texture', 'mean perimeter', 'mean area', 'mean smoothness', 'mean compactness', 'mean concavity', 'mean concave points', 'mean symmetry', 'mean fractal dimension', 'radius error', 'texture error', 'perimeter error', 'area error', 'smoothness error', 'compactness error', 'concavity error', 'concave points error', 'symmetry error', 'fractal dimension error', 'worst radius', 'worst texture', 'worst perimeter', 'worst area', 'worst smoothness', 'worst compactness', 'worst concavity', 'worst concave points', 'worst symmetry', 'worst fractal dimension', 'target']\nTest data shape: (342, 30)\nTest data columns: ['mean radius', 'mean texture', 'mean perimeter', 'mean area', 'mean smoothness', 'mean compactness', 'mean concavity', 'mean concave points', 'mean symmetry', 'mean fractal dimension', 'radius error', 'texture error', 'perimeter error', 'area error', 'smoothness error', 'compactness error', 'concavity error', 'concave points error', 'symmetry error', 'fractal dimension error', 'worst radius', 'worst texture', 'worst perimeter', 'worst area', 'worst smoothness', 'worst compactness', 'worst concavity', 'worst concave points', 'worst symmetry', 'worst fractal dimension']\n\nMissing values in training data: 0\nMissing values in test data: 0\nTraceback (most recent call last):\n  File \"/app/train_model.py\", line 52, in <module>\n    X_train, X_val, y_train, y_val, train_indices, val_indices = train_test_split(\n                                                                 ~~~~~~~~~~~~~~~~^\n        X, y,\n        ^^^^^\n    ...<3 lines>...\n        return_index=True\n        ^^^^^^^^^^^^^^^^^\n    )\n    ^\n  File \"/usr/local/lib/python3.13/site-packages/sklearn/utils/_param_validation.py\", line 196, in wrapper\n    params = func_sig.bind(*args, **kwargs)\n  File \"/usr/local/lib/python3.13/inspect.py\", line 3264, in bind\n    return self._bind(args, kwargs)\n           ~~~~~~~~~~^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.13/inspect.py\", line 3253, in _bind\n    raise TypeError(\n        'got an unexpected keyword argument {arg!r}'.format(\n            arg=next(iter(kwargs))))\nTypeError: got an unexpected keyword argument 'return_index'", "exit_code": 1, "error": null}

{"success": true, "diff": "--- a//app/train_model.py\n+++ b//app/train_model.py\n@@ -49,13 +49,20 @@\n y = train_df[\"target\"].values\n \n # Split into train/validation sets\n-X_train, X_val, y_train, y_val, train_indices, val_indices = train_test_split(\n-    X, y, \n-    test_size=0.2, \n-    random_state=42, \n-    stratify=y,\n-    return_index=True\n+# Get indices first\n+all_indices = np.arange(len(X))\n+train_indices, val_indices = train_test_split(\n+    all_indices,\n+    test_size=0.2,\n+    random_state=42,\n+    stratify=y\n )\n+\n+# Use indices to split data\n+X_train = X[train_indices]\n+X_val = X[val_indices]\n+y_train = y[train_indices]\n+y_val = y[val_indices]\n \n print(f\"\\nTrain set size: {len(y_train)}\")\n print(f\"Validation set size: {len(y_val)}\")\n", "files_modified": ["/app/train_model.py"], "lint": {"status": "ok", "output": ""}}

I need to fix the train_test_split call - the `return_index` parameter was removed in newer sklearn versions. Let me fix this:

→ patch({"path": "/app/train_model.py", "old_string": "# Split into train/validation sets\nX_train, X_val, y_train, y_val, train_indices, val_indices = train_test_split(\n    X, y, \n    test_size=0.2, \n    random_state=42, \n    stratify=y,\n    return_index=True\n)", "new_string": "# Split into train/validation sets\n# Get indices first\nall_indices = np.arange(len(X))\ntrain_indices, val_indices = tra…)

{"output": "2026/04/22 19:34:07 WARNING mlflow.utils.git_utils: Failed to import Git (the Git executable is probably not on your PATH), so Git SHA is not available. Error: Failed to initialize: Bad git executable.\nThe git executable must be specified in one of the following ways:\n    - be included in your $PATH\n    - be set via $GIT_PYTHON_GIT_EXECUTABLE\n    - explicitly set via git.refresh(<full-path-to-git-executable>)\n\nAll git commands will error until this is rectified.\n\nThis initial message can be silenced or aggravated in the future by setting the\n$GIT_PYTHON_REFRESH environment variable. Use one of the following values:\n    - quiet|q|silence|s|silent|none|n|0: for no message or exception\n    - warn|w|warning|log|l|1: for a warning message (logging level CRITICAL, displayed by default)\n    - error|e|exception|raise|r|2: for a raised exception\n\nExample:\n    export GIT_PYTHON_REFRESH=quiet\n\n2026/04/22 19:34:11 WARNING mlflow.utils.environment: Encountered an unexpected error while inferring pip requirements (model URI: /tmp/tmp5h7b2ygf/model/model.pkl, flavor: sklearn). Fall back to return ['scikit-learn==1.7.2', 'cloudpickle==3.1.2']. Set logging level to DEBUG to see the full traceback. \n2026/04/22 19:34:11 WARNING mlflow.models.model: Model logged without a signature and input example. Please set `input_example` parameter when logging the model to auto infer the model signature.\n2026/04/22 19:34:13 WARNING mlflow.utils.environment: Encountered an unexpected error while inferring pip requirements (model URI: /tmp/tmp8w30zlfm/model/model.pkl, flavor: sklearn). Fall back to return ['scikit-learn==1.7.2', 'cloudpickle==3.1.2']. Set logging level to DEBUG to see the full traceback. \n2026/04/22 19:34:13 WARNING mlflow.models.model: Model logged without a signature and input example. Please set `input_example` parameter when logging the model to auto infer the model signature.\n2026/04/22 19:34:15 WARNING mlflow.utils.environment: Encountered an unexpected error while inferring pip requirements (model URI: /tmp/tmp49gee9_v/model/model.pkl, flavor: sklearn). Fall back to return ['scikit-learn==1.7.2', 'cloudpickle==3.1.2']. Set logging level to DEBUG to see the full traceback. \n2026/04/22 19:34:15 WARNING mlflow.models.model: Model logged without a signature and input example. Please set `input_example` parameter when logging the model to auto infer the model signature.\nSuccessfully registered model 'breast-cancer-classifier'.\n2026/04/22 19:34:16 INFO mlflow.store.model_registry.abstract_store: Waiting up to 300 seconds for model version to finish creation. Model name: breast-cancer-classifier, version 1\nCreated version '1' of model 'breast-cancer-classifier'.\n/app/train_model.py:238: FutureWarning: ``mlflow.tracking.client.MlflowClient.transition_model_version_stage`` is deprecated since 2.9.0. Model registry stages will be removed in a future major release. To learn more about the deprecation of model registry stages, see our migration guide here: https://mlflow.org/docs/latest/model-registry.html#migrating-from-stages\n  client.transition_model_version_stage(\n============================================================\nSTEP 1: Load and Explore Data\n============================================================\nTraining data shape: (227, 31)\nTraining data columns: ['mean radius', 'mean texture', 'mean perimeter', 'mean area', 'mean smoothness', 'mean compactness', 'mean concavity', 'mean concave points', 'mean symmetry', 'mean fractal dimension', 'radius error', 'texture error', 'perimeter error', 'area error', 'smoothness error', 'compactness error', 'concavity error', 'concave points error', 'symmetry error', 'fractal dimension error', 'worst radius', 'worst texture', 'worst perimeter', 'worst area', 'worst smoothness', 'worst compactness', 'worst concavity', 'worst concave points', 'worst symmetry', 'worst fractal dimension', 'target']\nTest data shape: (342, 30)\nTest data columns: ['mean radiu

{"output": "{\n  \"random_state\": 42,\n  \"test_size\": 0.2,\n  \"train_size\": 181,\n  \"val_size\": 46,\n  \"train_indices\": [\n    60,\n    172,\n    141,\n    90,\n    180,\n    80,\n    150,\n    133,\n    169,\n    154,\n    98,\n    81,\n    136,\n    166,\n    35,\n    184,\n    29,\n    41,\n    48,\n    45,\n    140,\n    177,\n    76,\n    196,\n    126,\n    144,\n    75,\n    44,\n    74,\n    130,\n    92,\n    115,\n    215,\n    21,\n    43,\n    167,\n    181,\n    139,\n    121,\n    120,\n    189,\n    137,\n    102,\n    207,\n    124,\n    62,\n    182,\n    34,\n    104,\n    200,\n    63,\n    105,\n    78,\n    165,\n    221,\n    148,\n    99,\n    176,\n    53,\n    173,\n    153,\n    185,\n    23,\n    86,\n    70,\n    127,\n    201,\n    143,\n    108,\n    26,\n    51,\n    163,\n    211,\n    170,\n    112,\n    195,\n    32,\n    206,\n    52,\n    174,\n    224,\n    114,\n    204,\n    125,\n    69,\n    91,\n    4,\n    220,\n    107,\n    191,\n    17,\n    188,\n    24,\n    155,\n    25,\n    73,\n    113,\n    156,\n    123,\n    10,\n    95,\n    217,\n    116,\n    138,\n    216,\n    57,\n    30,\n    213,\n    19,\n    72,\n    175,\n    203,\n    135,\n    22,\n    66,\n    103,\n    122,\n    97,\n    190,\n    85,\n    146,\n    158,\n    82,\n    218,\n    101,\n    100,\n    36,\n    65,\n    6,\n    39,\n    40,\n    61,\n    210,\n    56,\n    164,\n    33,\n    219,\n    27,\n    0,\n    223,\n    187,\n    64,\n    55,\n    202,\n    8,\n    226,\n    118,\n    12,\n    212,\n    161,\n    15,\n    18,\n    142,\n    222,\n    179,\n    128,\n    1,\n    178,\n    89,\n    109,\n    50,\n    83,\n    38,\n    84,\n    42,\n    159,\n    152,\n    111,\n    77,\n    2,\n    28,\n    183,\n    96,\n    3,\n    129,\n    157,\n    160,\n    49,\n    214,\n    194,\n    14\n  ],\n  \"val_indices\": [\n    145,\n    59,\n    79,\n    117,\n    151,\n    171,\n    37,\n    208,\n    106,\n    9,\n    5,\n    87,\n    88,\n    11,\n    67,\n    199,\n    197,\n    132,\n    192,\n    93,\n    134,\n    147,\n    198,\n    209,\n    68,\n    71,\n    119,\n    58,\n    47,\n    54,\n    162,\n    186,\n    46,\n    149,\n    193,\n    16,\n    13,\n    110,\n    205,\n    7,\n    225,\n    20,\n    131,\n    31,\n    168,\n    94\n  ]\n}", "exit_code": 0, "error": null}

The training is complete. Now let me verify the outputs and create the FastAPI application:

→ terminal({"command": "cat /app/output/split_info.json"})

{"output": "{\n  \"experiment_name\": \"breast-cancer-classification\",\n  \"runs\": [\n    {\n      \"run_id\": \"41d17416bd344b0eb6f7d6a32665295f\",\n      \"model_type\": \"logistic_regression\",\n      \"hyperparameters\": {\n        \"C\": 0.1,\n        \"max_iter\": 1000,\n        \"solver\": \"lbfgs\",\n        \"penalty\": \"l2\",\n        \"random_state\": 42\n      },\n      \"train_accuracy\": 0.9668508287292817,\n      \"val_accuracy\": 0.9347826086956522,\n      \"precision\": 0.90625,\n      \"recall\": 1.0,\n      \"f1_score\": 0.9508196721311475\n    },\n    {\n      \"run_id\": \"28db6d13cf904815bfd7a933b97abe2b\",\n      \"model_type\": \"svm\",\n      \"hyperparameters\": {\n        \"kernel\": \"rbf\",\n        \"C\": 1.0,\n        \"gamma\": \"scale\",\n        \"degree\": 3,\n        \"random_state\": 42\n      },\n      \"train_accuracy\": 0.9226519337016574,\n      \"val_accuracy\": 0.9130434782608695,\n      \"precision\": 0.8787878787878788,\n      \"recall\": 1.0,\n      \"f1_score\": 0.9354838709677419\n    },\n    {\n      \"run_id\": \"2a8f7697710046128b4e8c7528c17f13\",\n      \"model_type\": \"random_forest\",\n      \"hyperparameters\": {\n        \"n_estimators\": 100,\n        \"max_depth\": 10,\n        \"min_samples_split\": 5,\n        \"min_samples_leaf\": 2,\n        \"random_state\": 42\n      },\n      \"train_accuracy\": 0.994475138121547,\n      \"val_accuracy\": 0.9130434782608695,\n      \"precision\": 0.9032258064516129,\n      \"recall\": 0.9655172413793104,\n      \"f1_score\": 0.9333333333333333\n    }\n  ],\n  \"best_run_id\": \"41d17416bd344b0eb6f7d6a32665295f\",\n  \"best_model_type\": \"logistic_regression\"\n}", "exit_code": 0, "error": null}

{"output": "{\n  \"model_name\": \"breast-cancer-classifier\",\n  \"model_version\": \"1\",\n  \"model_stage\": \"Production\",\n  \"run_id\": \"41d17416bd344b0eb6f7d6a32665295f\",\n  \"model_type\": \"logistic_regression\",\n  \"hyperparameters\": {\n    \"C\": 0.1,\n    \"max_iter\": 1000,\n    \"solver\": \"lbfgs\",\n    \"penalty\": \"l2\",\n    \"random_state\": 42\n  },\n  \"val_accuracy\": 0.9347826086956522,\n  \"precision\": 0.90625,\n  \"recall\": 1.0,\n  \"f1_score\": 0.9508196721311475\n}", "exit_code": 0, "error": null}

{"bytes_written": 4945, "dirs_created": true}

Now let me create the FastAPI application:

→ write_file({"path": "/app/api/app.py", "content": "#!/usr/bin/env python3\n\"\"\"\nFastAPI Application for Breast Cancer Classification\n- Loads model from MLflow Model Registry at startup\n- Provides endpoints for single and batch predictions\n\"\"\"\n\nimport json\nimport os\nfrom fastapi import FastAPI, HTTPException\nfrom pydantic import BaseModel\nfrom typing import List, Optional\nimport mlflow\nimport…)

{"output": "Background process started", "session_id": "proc_3214a3a65521", "pid": null, "exit_code": 0, "error": null}

Now let me start the FastAPI server:

→ terminal({"command": "cd /app && nohup uvicorn api.app:app --host 0.0.0.0 --port 8000 > /app/api/api.log 2>&1 &", "background": true})

{"status": "exited", "exit_code": -1, "output": ""}

{"output": "cat: /app/api/api.log: No such file or directory", "exit_code": 1, "error": null}

{"output": "Background process started", "session_id": "proc_272e62043f6f", "pid": null, "exit_code": 0, "error": null}

{"status": "exited", "exit_code": -1, "output": ""}

{"output": "cat: /tmp/api.log: No such file or directory", "exit_code": 1, "error": null}

{"processes": [{"session_id": "proc_e5c5329a9f66", "command": "mlflow server --host 127.0.0.1 --port 5000 --backend-store-uri /app/mlflow/mlflow.db --default-artifact-root /app/mlflow/artifacts > /app/mlflow/mlflow.log 2>&1 &", "cwd": "/app", "pid": null, "started_at": "2026-04-22T14:26:22", "uptime_seconds": 848, "status": "exited", "output_preview": "", "exit_code": -1}, {"session_id": "proc_293eb4a67109", "command": "nohup python3 -m mlflow.server --host 127.0.0.1 --port 5000 --backend-store-uri /app/mlflow/mlflow.db --default-artifact-root /app/mlflow/artifacts > /app/mlflow/mlflow.log 2>&1 &", "cwd": "/app", "pid": null, "started_at": "2026-04-22T14:27:11", "uptime_seconds": 799, "status": "exited", "output_preview": "", "exit_code": -1}, {"session_id": "proc_3214a3a65521", "command": "cd /app && nohup uvicorn api.app:app --host 0.0.0.0 --port 8000 > /app/api/api.log 2>&1 &", "cwd": "/app", "pid": null, "started_at": "2026-04-22T14:38:47", "uptime_seconds": 103, "status": "exited", "output_preview": "", "exit_code": -1}, {"session_id": "proc_272e62043f6f", "command": "cd /app && nohup uvicorn api.app:app --host 0.0.0.0 --port 8000 > /tmp/api.log 2>&1 &", "cwd": "/app", "pid": null, "started_at": "2026-04-22T14:39:44", "uptime_seconds": 46, "status": "exited", "output_preview": "", "exit_code": -1}]}

{"output": "Background process started", "session_id": "proc_13190e2b6ed3", "pid": null, "exit_code": 0, "error": null}

Let me try running the uvicorn server directly:

→ terminal({"command": "cd /app && python3 -m uvicorn api.app:app --host 0.0.0.0 --port 8000 &", "background": true})

{"status": "exited", "exit_code": -1, "output": ""}

{"output": "", "exit_code": -1, "error": "This foreground command appears to start a long-lived server/watch process. Run it with background=true, verify readiness (health endpoint/log signal), then execute tests in a separate command.", "status": "error"}

{"output": "Background process started", "session_id": "proc_21ff33e77785", "pid": null, "exit_code": 0, "error": null}

{"status": "exited", "exit_code": -1, "output": ""}

{"session_id": "proc_21ff33e77785", "status": "exited", "output": "", "total_lines": 0, "showing": "0 lines"}

{"output": "Background process started", "session_id": "proc_a6ad07effa35", "pid": null, "exit_code": 0, "error": null}

Let me try a different approach - run the server in a PTY:

→ terminal({"command": "cd /app && python3 -m uvicorn api.app:app --host 0.0.0.0 --port 8000 &", "background": true, "pty": true})

{"status": "exited", "exit_code": -1, "output": ""}

{"output": "Import successful", "exit_code": 0, "error": null}

Let me check if there's an issue with the app.py file:

→ terminal({"command": "python3 -c \"import sys; sys.path.insert(0, '/app'); from api.app import app; print('Import successful')\""})