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 24 18:57 ..\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}

{"output": "total 132\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": "total 12\ndrwxr-xr-x 3 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 artifacts", "exit_code": 0, "error": null}

<think>Let me break down this task:

1. Load and explore breast cancer data from /app/data/train.csv and /app/data/test.csv
2. Split training data into train/validation sets
3. Train 3 classification models (Logistic Regression, SVM, Random Forest) with MLflow tracking
4. Select best model and register to MLflow Model Registry
5. Build FastAPI application with 4 endpoints
6. Start FastAPI server and verify
7. Test API with test data predictions

Let me start by exploring the environment and understanding the data structure.
</think>

I'll build a complete ML pipeline for breast cancer diagnosis with MLflow tracking and FastAPI deployment. Let me start by exploring the environment and data.

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

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

→ terminal({"command": "ls -la /app/mlflow/ 2>/dev/null || echo \"MLflow directory not found\""})

{"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", "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\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", "exit_code": 0, "error": null}

<think>Let me explore the data files and check the MLflow tracking server.
</think>

→ terminal({"command": "head -5 /app/data/train.csv"})

→ terminal({"command": "head -5 /app/data/test.csv"})

→ terminal({"command": "wc -l /app/data/train.csv /app/data/test.csv"})

→ terminal({"command": "curl -s http://127.0.0.1:5000/ | head -20"})

{"output": "Python 3.13.1\nWARNING: The directory '/root/.cache/pip' or its parent directory is not owned or is not writable by the current user. The cache has been disabled. Check the permissions and owner of that directory. If executing pip with sudo, you should use sudo's -H flag.\nfastapi                            0.115.6\nmlflow                             2.19.0\nmlflow-skinny                      2.19.0\npandas                             2.2.3\nrequests                           2.32.3\nscikit-learn                       1.7.2\nuvicorn                            0.34.0", "exit_code": 0, "error": null}

{"output": "total 12\ndrwxr-xr-x 2 root root 4096 Mar  4 09:20 .\ndrwxr-xr-x 1 root root 4096 Mar  4 09:20 ..", "exit_code": 0, "error": null}

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

<think>Good, I can see:
- train.csv has 228 lines (227 samples + header) with 31 columns (30 features + target)
- test.csv has 343 lines (342 samples + header) with 30 columns (30 features, no target)
- MLflow tracking server should be running at port 5000

Let me check the MLflow server and Python environment.
</think>

→ terminal({"command": "python3 --version && pip list | grep -E \"mlflow|scikit|fastapi|uvicorn|pandas|requests\""})

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

→ terminal({"command": "mkdir -p /app/output /app/data/submission"})

{"output": "200", "exit_code": 0, "error": null}

<think>Good, all the required packages are installed. Let me check if the MLflow tracking server is running and accessible.
</think>

→ terminal({"command": "python3 -c \"import requests; r = requests.get('http://127.0.0.1:5000/'); print(r.status_code)\""})

{"bytes_written": 8422, "dirs_created": true}

<think>The MLflow tracking server is running and accessible. Now let me create the training script that will:
1. Load and explore the data
2. Split the training data
3. Train 3 models with MLflow tracking
4. Select the best model and register it
</think>

The MLflow tracking server is running. Now let me create the training script:

→ write_file({"path": "/app/train.py", "content": "#!/usr/bin/env python3\n\"\"\"\nBreast Cancer Classification Training Pipeline\n- Train multiple classification models with MLflow tracking\n- Register the best model to MLflow Model Registry\n\"\"\"\n\nimport json\nimport numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LogisticRegression…)

{"output": "2026/04/24 19:06:31 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\n2026/04/24 19:06:34 WARNING mlflow.utils.environment: Encountered an unexpected error while inferring pip requirements (model URI: /tmp/tmpd4ud5usr/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. \n2026/04/24 19:06:34 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.\n2026/04/24 19:06:36 WARNING mlflow.utils.environment: Encountered an unexpected error while inferring pip requirements (model URI: /tmp/tmpy3ch15ej/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. \n2026/04/24 19:06:36 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.\n2026/04/24 19:06:39 WARNING mlflow.utils.environment: Encountered an unexpected error while inferring pip requirements (model URI: /tmp/tmpffgc2eqp/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. \n2026/04/24 19:06:39 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'.\n2026/04/24 19:06:39 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'.\nLoading data...\nTraining data shape: (227, 31)\nTest data shape: (342, 30)\nTraining 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 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 dimensi

<think>Now let me run the training script.
</think>

→ terminal({"command": "cd /app && python3 train.py 2>&1", "timeout": 300})

{"success": true, "diff": "--- a//app/train.py\n+++ b//app/train.py\n@@ -237,7 +237,7 @@\n print(f\"Model version: {registered_model.version}\")\n \n # Transition model to Production stage\n-client.transition_model_version(\n+client.transition_model_version_stage(\n     name=MODEL_NAME,\n     version=registered_model.version,\n     stage=\"Production\"\n", "files_modified": ["/app/train.py"], "lint": {"status": "ok", "output": ""}}

<think>The training worked but the transition method name is different. Let me fix that and re-run.
</think>

I need to fix the method name for transitioning the model version. Let me update the script:

→ patch({"mode": "replace", "path": "/app/train.py", "old_string": "# Transition model to Production stage\nclient.transition_model_version(\n    name=MODEL_NAME,\n    version=registered_model.version,\n    stage=\"Production\"\n)", "new_string": "# Transition model to Production stage\nclient.transition_model_version_stage(\n    name=MODEL_NAME,\n    version=registered_model.version,\n    stage=\"Product…)

{"output": "2026/04/24 19:07:29 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\n2026/04/24 19:07:32 WARNING mlflow.utils.environment: Encountered an unexpected error while inferring pip requirements (model URI: /tmp/tmputm97eej/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. \n2026/04/24 19:07:32 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.\n2026/04/24 19:07:34 WARNING mlflow.utils.environment: Encountered an unexpected error while inferring pip requirements (model URI: /tmp/tmpkgqkrxd9/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. \n2026/04/24 19:07:34 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.\n2026/04/24 19:07:36 WARNING mlflow.utils.environment: Encountered an unexpected error while inferring pip requirements (model URI: /tmp/tmp3gj3cw6j/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. \n2026/04/24 19:07:36 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.\nRegistered model 'breast-cancer-classifier' already exists. Creating a new version of this model...\n2026/04/24 19:07:36 INFO mlflow.store.model_registry.abstract_store: Waiting up to 300 seconds for model version to finish creation. Model name: breast-cancer-classifier, version 2\nCreated version '2' of model 'breast-cancer-classifier'.\nLoading data...\nTraining data shape: (227, 31)\nTest data shape: (342, 30)\nTraining 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 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'

<think>Now let me re-run the training script.
</think>

→ terminal({"command": "cd /app && python3 train.py 2>&1", "timeout": 300})

{"content": "     1|{\n     2|  \"experiment_name\": \"breast-cancer-classification\",\n     3|  \"runs\": [\n     4|    {\n     5|      \"run_id\": \"c5e5679e46214074addb0104e15d8147\",\n     6|      \"model_type\": \"logistic_regression\",\n     7|      \"hyperparameters\": {\n     8|        \"C\": 1.0,\n     9|        \"max_iter\": 1000,\n    10|        \"solver\": \"lbfgs\",\n    11|        \"penalty\": \"l2\",\n    12|        \"random_state\": 42\n    13|      },\n    14|      \"train_accuracy\": 0.9613259668508287,\n    15|      \"val_accuracy\": 0.9347826086956522,\n    16|      \"precision\": 0.90625,\n    17|      \"recall\": 1.0,\n    18|      \"f1_score\": 0.9508196721311475\n    19|    },\n    20|    {\n    21|      \"run_id\": \"b54aba3cfd284a6693426d82f6dafe6d\",\n    22|      \"model_type\": \"svm\",\n    23|      \"hyperparameters\": {\n    24|        \"kernel\": \"rbf\",\n    25|        \"C\": 1.0,\n    26|        \"gamma\": \"scale\",\n    27|        \"degree\": 3,\n    28|        \"random_state\": 42\n    29|      },\n    30|      \"train_accuracy\": 0.9226519337016574,\n    31|      \"val_accuracy\": 0.9130434782608695,\n    32|      \"precision\": 0.8787878787878788,\n    33|      \"recall\": 1.0,\n    34|      \"f1_score\": 0.9354838709677419\n    35|    },\n    36|    {\n    37|      \"run_id\": \"1900abce93ec4dd1bd93a9b9ad0eacd2\",\n    38|      \"model_type\": \"random_forest\",\n    39|      \"hyperparameters\": {\n    40|        \"n_estimators\": 100,\n    41|        \"max_depth\": 10,\n    42|        \"min_samples_split\": 2,\n    43|        \"min_samples_leaf\": 1,\n    44|        \"random_state\": 42\n    45|      },\n    46|      \"train_accuracy\": 1.0,\n    47|      \"val_accuracy\": 0.9130434782608695,\n    48|      \"precision\": 0.9032258064516129,\n    49|      \"recall\": 0.9655172413793104,\n    50|      \"f1_score\": 0.9333333333333333\n    51|    }\n    52|  ],\n    53|  \"best_run_id\": \"c5e5679e46214074addb0104e15d8147\",\n    54|  \"best_model_type\": \"logistic_regression\"\n    55|}", "total_lines": 54, "file_size": 1500, "truncated": false, "is_binary": false, "is_image": false}

{"content": "     1|{\n     2|  \"model_name\": \"breast-cancer-classifier\",\n     3|  \"model_version\": \"2\",\n     4|  \"model_stage\": \"Production\",\n     5|  \"run_id\": \"c5e5679e46214074addb0104e15d8147\",\n     6|  \"model_type\": \"logistic_regression\",\n     7|  \"hyperparameters\": {\n     8|    \"C\": 1.0,\n     9|    \"max_iter\": 1000,\n    10|    \"solver\": \"lbfgs\",\n    11|    \"penalty\": \"l2\",\n    12|    \"random_state\": 42\n    13|  },\n    14|  \"val_accuracy\": 0.9347826086956522,\n    15|  \"precision\": 0.90625,\n    16|  \"recall\": 1.0,\n    17|  \"f1_score\": 0.9508196721311475\n    18|}", "total_lines": 17, "file_size": 432, "truncated": false, "is_binary": false, "is_image": false}

{"content": "     1|{\"random_state\": 42, \"test_size\": 0.2, \"train_size\": 181, \"val_size\": 46, \"train_indices\": [60, 172, 141, 90, 180, 80, 150, 133, 169, 154, 98, 81, 136, 166, 35, 184, 29, 41, 48, 45, 140, 177, 76, 196, 126, 144, 75, 44, 74, 130, 92, 115, 215, 21, 43, 167, 181, 139, 121, 120, 189, 137, 102, 207, 124, 62, 182, 34, 104, 200, 63, 105, 78, 165, 221, 148, 99, 176, 53, 173, 153, 185, 23, 86, 70, 127, 201, 143, 108, 26, 51, 163, 211, 170, 112, 195, 32, 206, 52, 174, 224, 114, 204, 125, 69, 91, 4, 220, 107, 191, 17, 188, 24, 155, 25, 73, 113, 156, 123, 10, 95, 217, 116, 138, 216, 57, 30, 213, 19, 72, 175, 203, 135, 22, 66, 103, 122, 97, 190, 85, 146, 158, 82, 218, 101, 100, 36, 65, 6, 39, 40, 61, 210, 56, 164, 33, 219, 27, 0, 223, 187, 64, 55, 202, 8, 226, 118, 12, 212, 161, 15, 18, 142, 222, 179, 128, 1, 178, 89, 109, 50, 83, 38, 84, 42, 159, 152, 111, 77, 2, 28, 183, 96, 3, 129, 157, 160, 49, 214, 194, 14], \"val_indices\": [145, 59, 79, 117, 151, 171, 37, 208, 106, 9, 5, 87, 88, 11, 67, 199, 197, 132, 192, 93, 134, 147, 198, 209, 68, 71, 119, 58, 47, 54, 162, 186, 46, 149, 193, 16, 13, 110, 205, 7, 225, 20, 131, 31, 168, 94]}", "total_lines": 0, "file_size": 1134, "truncated": false, "is_binary": false, "is_image": false}

<think>Training is complete. Now let me verify the output files and create the FastAPI application.
</think>

Training complete! Let me verify the output files and create the FastAPI application:

→ read_file({"path": "/app/output/experiments.json"})

→ read_file({"path": "/app/output/best_model_info.json"})

→ read_file({"path": "/app/output/split_info.json"})

{"bytes_written": 6265, "dirs_created": true}

<think>All the output files are created. Now let me create the FastAPI application.
</think>

All output files are created. Now let me create the FastAPI application:

→ write_file({"path": "/app/api/app.py", "content": "\"\"\"\nFastAPI application for Breast Cancer Classification\n- Loads registered model from MLflow Model Registry\n- Provides prediction endpoints\n\"\"\"\n\nfrom fastapi import FastAPI, HTTPException\nfrom pydantic import BaseModel, Field\nfrom typing import List\nimport mlflow\nfrom mlflow.tracking import MlflowClient\nimport numpy as np\nimport warnings\n…)

{"output": "", "exit_code": -1, "error": "Foreground command uses '&' backgrounding. Use terminal(background=true) for long-lived processes, then run health checks and tests in follow-up terminal calls.", "status": "error"}

<think>Now let me start the FastAPI server in the background.
</think>

→ terminal({"command": "cd /app && uvicorn api.app:app --host 0.0.0.0 --port 8000 2>&1 &\necho \"Server starting...\"\nsleep 5", "timeout": 30})

{"output": "Background process started", "session_id": "proc_9dddc25d6e30", "pid": null, "exit_code": 0, "error": null, "notify_on_complete": true}

<think>I need to use the background parameter to start the server. Let me try that.
</think>

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

{"output": "Traceback (most recent call last):\n  File \"/usr/local/lib/python3.13/site-packages/urllib3/connection.py\", line 204, in _new_conn\n    sock = connection.create_connection(\n        (self._dns_host, self.port),\n    ...<2 lines>...\n        socket_options=self.socket_options,\n    )\n  File \"/usr/local/lib/python3.13/site-packages/urllib3/util/connection.py\", line 85, in create_connection\n    raise err\n  File \"/usr/local/lib/python3.13/site-packages/urllib3/util/connection.py\", line 73, in create_connection\n    sock.connect(sa)\n    ~~~~~~~~~~~~^^^^\nConnectionRefusedError: [Errno 111] Connection refused\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n  File \"/usr/local/lib/python3.13/site-packages/urllib3/connectionpool.py\", line 787, in urlopen\n    response = self._make_request(\n        conn,\n    ...<10 lines>...\n        **response_kw,\n    )\n  File \"/usr/local/lib/python3.13/site-packages/urllib3/connectionpool.py\", line 493, in _make_request\n    conn.request(\n    ~~~~~~~~~~~~^\n        method,\n        ^^^^^^^\n    ...<6 lines>...\n        enforce_content_length=enforce_content_length,\n        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n    )\n    ^\n  File \"/usr/local/lib/python3.13/site-packages/urllib3/connection.py\", line 500, in request\n    self.endheaders()\n    ~~~~~~~~~~~~~~~^^\n  File \"/usr/local/lib/python3.13/http/client.py\", line 1331, in endheaders\n    self._send_output(message_body, encode_chunked=encode_chunked)\n    ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.13/http/client.py\", line 1091, in _send_output\n    self.send(msg)\n    ~~~~~~~~~^^^^^\n  File \"/usr/local/lib/python3.13/http/client.py\", line 1035, in send\n    self.connect()\n    ~~~~~~~~~~~~^^\n  File \"/usr/local/lib/python3.13/site-packages/urllib3/connection.py\", line 331, in connect\n    self.sock = self._new_conn()\n                ~~~~~~~~~~~~~~^^\n  File \"/usr/local/lib/python3.13/site-packages/urllib3/connection.py\", line 219, in _new_conn\n    raise NewConnectionError(\n        self, f\"Failed to establish a new connection: {e}\"\n    ) from e\nurllib3.exceptions.NewConnectionError: HTTPConnection(host='localhost', port=8000): Failed to establish a new connection: [Errno 111] Connection refused\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n  File \"/usr/local/lib/python3.13/site-packages/requests/adapters.py\", line 667, in send\n    resp = conn.urlopen(\n        method=request.method,\n    ...<9 lines>...\n        chunked=chunked,\n    )\n  File \"/usr/local/lib/python3.13/site-packages/urllib3/connectionpool.py\", line 841, in urlopen\n    retries = retries.increment(\n        method, url, error=new_e, _pool=self, _stacktrace=sys.exc_info()[2]\n    )\n  File \"/usr/local/lib/python3.13/site-packages/urllib3/util/retry.py\", line 535, in increment\n    raise MaxRetryError(_pool, url, reason) from reason  # type: ignore[arg-type]\n    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nurllib3.exceptions.MaxRetryError: HTTPConnectionPool(host='localhost', port=8000): Max retries exceeded with url: /health (Caused by NewConnectionError(\"HTTPConnection(host='localhost', port=8000): Failed to establish a new connection: [Errno 111] Connection refused\"))\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n  File \"<string>\", line 1, in <module>\n    import requests; r = requests.get('http://localhost:8000/health'); print(r.status_code, r.json())\n                         ~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.13/site-packages/requests/api.py\", line 73, in get\n    return request(\"get\", url, params=params, **kwargs)\n  File \"/usr/local/lib/python3.13/site-packages/requests/api.py\", line 59, in request\n    return s

<think>Good, the server is starting. Let me wait a moment and then check if it's running.
</think>

→ terminal({"command": "sleep 5 && python3 -c \"import requests; r = requests.get('http://localhost:8000/health'); print(r.status_code, r.json())\"", "timeout": 15})

{"session_id": "proc_9dddc25d6e30", "status": "exited", "output": "", "total_lines": 0, "showing": "0 lines"}

<think>The server hasn't started yet. Let me check the process log to see what's happening.
</think>

→ process({"action": "log", "session_id": "proc_9dddc25d6e30"})

{"output": "API module loaded successfully", "exit_code": 0, "error": null}

<think>The process seems to have exited. Let me check what's happening and try running it differently.
</think>

→ terminal({"command": "cd /app && python3 -c \"from api.app import app; print('API module loaded successfully')\"", "timeout": 30})

{"output": "Background process started", "session_id": "proc_27061fbf1681", "pid": null, "exit_code": 0, "error": null, "notify_on_complete": true}

<think>The module loads fine. Let me try running the server directly with a different approach.
</think>

→ terminal({"command": "cd /app && python3 -c \"\nimport uvicorn\nfrom api.app import app\nuvicorn.run(app, host='0.0.0.0', port=8000)\n\"", "background": true, "notify_on_complete": true})

{"output": "Traceback (most recent call last):\n  File \"/usr/local/lib/python3.13/site-packages/urllib3/connection.py\", line 204, in _new_conn\n    sock = connection.create_connection(\n        (self._dns_host, self.port),\n    ...<2 lines>...\n        socket_options=self.socket_options,\n    )\n  File \"/usr/local/lib/python3.13/site-packages/urllib3/util/connection.py\", line 85, in create_connection\n    raise err\n  File \"/usr/local/lib/python3.13/site-packages/urllib3/util/connection.py\", line 73, in create_connection\n    sock.connect(sa)\n    ~~~~~~~~~~~~^^^^\nConnectionRefusedError: [Errno 111] Connection refused\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n  File \"/usr/local/lib/python3.13/site-packages/urllib3/connectionpool.py\", line 787, in urlopen\n    response = self._make_request(\n        conn,\n    ...<10 lines>...\n        **response_kw,\n    )\n  File \"/usr/local/lib/python3.13/site-packages/urllib3/connectionpool.py\", line 493, in _make_request\n    conn.request(\n    ~~~~~~~~~~~~^\n        method,\n        ^^^^^^^\n    ...<6 lines>...\n        enforce_content_length=enforce_content_length,\n        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n    )\n    ^\n  File \"/usr/local/lib/python3.13/site-packages/urllib3/connection.py\", line 500, in request\n    self.endheaders()\n    ~~~~~~~~~~~~~~~^^\n  File \"/usr/local/lib/python3.13/http/client.py\", line 1331, in endheaders\n    self._send_output(message_body, encode_chunked=encode_chunked)\n    ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.13/http/client.py\", line 1091, in _send_output\n    self.send(msg)\n    ~~~~~~~~~^^^^^\n  File \"/usr/local/lib/python3.13/http/client.py\", line 1035, in send\n    self.connect()\n    ~~~~~~~~~~~~^^\n  File \"/usr/local/lib/python3.13/site-packages/urllib3/connection.py\", line 331, in connect\n    self.sock = self._new_conn()\n                ~~~~~~~~~~~~~~^^\n  File \"/usr/local/lib/python3.13/site-packages/urllib3/connection.py\", line 219, in _new_conn\n    raise NewConnectionError(\n        self, f\"Failed to establish a new connection: {e}\"\n    ) from e\nurllib3.exceptions.NewConnectionError: HTTPConnection(host='localhost', port=8000): Failed to establish a new connection: [Errno 111] Connection refused\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n  File \"/usr/local/lib/python3.13/site-packages/requests/adapters.py\", line 667, in send\n    resp = conn.urlopen(\n        method=request.method,\n    ...<9 lines>...\n        chunked=chunked,\n    )\n  File \"/usr/local/lib/python3.13/site-packages/urllib3/connectionpool.py\", line 841, in urlopen\n    retries = retries.increment(\n        method, url, error=new_e, _pool=self, _stacktrace=sys.exc_info()[2]\n    )\n  File \"/usr/local/lib/python3.13/site-packages/urllib3/util/retry.py\", line 535, in increment\n    raise MaxRetryError(_pool, url, reason) from reason  # type: ignore[arg-type]\n    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nurllib3.exceptions.MaxRetryError: HTTPConnectionPool(host='localhost', port=8000): Max retries exceeded with url: /health (Caused by NewConnectionError(\"HTTPConnection(host='localhost', port=8000): Failed to establish a new connection: [Errno 111] Connection refused\"))\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n  File \"<string>\", line 1, in <module>\n    import requests; r = requests.get('http://localhost:8000/health'); print(r.status_code, r.json())\n                         ~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.13/site-packages/requests/api.py\", line 73, in get\n    return request(\"get\", url, params=params, **kwargs)\n  File \"/usr/local/lib/python3.13/site-packages/requests/api.py\", line 59, in request\n    return s

<think>Let me wait and check if the server is running.
</think>

→ terminal({"command": "sleep 5 && python3 -c \"import requests; r = requests.get('http://localhost:8000/health'); print(r.status_code, r.json())\"", "timeout": 15})

{"session_id": "proc_27061fbf1681", "status": "exited", "output": "", "total_lines": 0, "showing": "0 lines"}

<think>Let me check the process log to see if there are any errors.
</think>

→ process({"action": "log", "session_id": "proc_27061fbf1681"})

{"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"}

<think>The process exited. Let me try a different approach - running the server directly with nohup or checking if there's an issue with the API.
</think>

→ terminal({"command": "cd /app && python3 -m uvicorn api.app:app --host 0.0.0.0 --port 8000 2>&1 | head -50", "timeout": 20})

{"output": "Background process started", "session_id": "proc_894c6b61427e", "pid": null, "exit_code": 0, "error": null, "watch_patterns": ["Application startup complete", "Uvicorn running", "error", "Error", "Traceback"]}

<think>Let me try a different approach - start the server in background mode and check for errors.
</think>

→ terminal({"command": "cd /app && python3 -m uvicorn api.app:app --host 0.0.0.0 --port 8000", "background": true, "watch_patterns": ["Application startup complete", "Uvicorn running", "error", "Error", "Traceback"]})

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

<think>Let me wait and check the status of the server.
</think>

→ terminal({"command": "sleep 8", "timeout": 15})

{"session_id": "proc_894c6b61427e", "command": "cd /app && python3 -m uvicorn api.app:app --host 0.0.0.0 --port 8000", "status": "exited", "pid": null, "uptime_seconds": 75, "output_preview": "", "exit_code": -1}

<think>Let me check the process status and logs.
</think>

→ process({"action": "poll", "session_id": "proc_894c6b61427e"})

{"session_id": "proc_894c6b61427e", "status": "exited", "output": "", "total_lines": 0, "showing": "0 lines"}

<think>The process exited with an error. Let me check the full log.
</think>

→ process({"action": "log", "session_id": "proc_894c6b61427e"})

{"output": "Traceback (most recent call last):\n  File \"/usr/local/lib/python3.13/site-packages/starlette/testclient.py\", line 33, in <module>\n    import httpx\nModuleNotFoundError: No module named 'httpx'\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n  File \"<string>\", line 3, in <module>\n    from fastapi.testclient import TestClient\n  File \"/usr/local/lib/python3.13/site-packages/fastapi/testclient.py\", line 1, in <module>\n    from starlette.testclient import TestClient as TestClient  # noqa\n    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.13/site-packages/starlette/testclient.py\", line 35, in <module>\n    raise RuntimeError(\n    ...<3 lines>...\n    )\nRuntimeError: The starlette.testclient module requires the httpx package to be installed.\nYou can install this with:\n    $ pip install httpx", "exit_code": 1, "error": null}

<think>The log is empty. Let me try running it directly to see the error.
</think>

→ terminal({"command": "cd /app && python3 -c \"\nfrom api.app import app\nfrom fastapi.testclient import TestClient\nclient = TestClient(app)\nresponse = client.get('/health')\nprint(response.status_code)\nprint(response.json())\n\"", "timeout": 30})

{"output": "Successfully installed httpcore-1.0.9 httpx-0.28.1\nWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable.It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning.\n\n[notice] A new release of pip is available: 24.3.1 -> 26.0.1\n[notice] To update, run: pip install --upgrade pip", "exit_code": 0, "error": null}

<think>httpx is not installed. Let me install it and also check if there's an issue with the app itself.
</think>

→ terminal({"command": "pip install httpx 2>&1 | tail -5", "timeout": 60})

{"output": "Status: 200\nResponse: {'status': 'healthy', 'model_loaded': False, 'mlflow_tracking_uri': 'http://127.0.0.1:5000'}", "exit_code": 0, "error": null}

<think>httpx is now installed. Let me test the app directly to see if it loads properly.
</think>

→ terminal({"command": "cd /app && python3 -c \"\nfrom api.app import app\nfrom fastapi.testclient import TestClient\nclient = TestClient(app)\nresponse = client.get('/health')\nprint('Status:', response.status_code)\nprint('Response:', response.json())\n\"", "timeout": 60})

{"bytes_written": 6557, "dirs_created": true}

<think>The app loads, but the model is not loaded. This is because the startup event handler isn't being called properly in the test client. Let me fix the API to load the model differently and also run the server properly.
</think>

The app loads but the model isn't loading during test. Let me fix the API to properly load the model:

→ write_file({"path": "/app/api/app.py", "content": "\"\"\"\nFastAPI application for Breast Cancer Classification\n- Loads registered model from MLflow Model Registry\n- Provides prediction endpoints\n\"\"\"\n\nfrom fastapi import FastAPI, HTTPException\nfrom pydantic import BaseModel, Field\nfrom typing import List\nimport mlflow\nfrom mlflow.tracking import MlflowClient\nfrom mlflow.pyfunc import load_model …)

{"output": "Background process started", "session_id": "proc_62498bba4679", "pid": null, "exit_code": 0, "error": null, "watch_patterns": ["Application startup complete", "Uvicorn running", "error", "Error", "Traceback"]}

<think>Now let me start the FastAPI server in the background.
</think>

→ terminal({"command": "cd /app && python3 -m uvicorn api.app:app --host 0.0.0.0 --port 8000", "background": true, "watch_patterns": ["Application startup complete", "Uvicorn running", "error", "Error", "Traceback"]})

{"output": "Traceback (most recent call last):\n  File \"/usr/local/lib/python3.13/site-packages/urllib3/connection.py\", line 204, in _new_conn\n    sock = connection.create_connection(\n        (self._dns_host, self.port),\n    ...<2 lines>...\n        socket_options=self.socket_options,\n    )\n  File \"/usr/local/lib/python3.13/site-packages/urllib3/util/connection.py\", line 85, in create_connection\n    raise err\n  File \"/usr/local/lib/python3.13/site-packages/urllib3/util/connection.py\", line 73, in create_connection\n    sock.connect(sa)\n    ~~~~~~~~~~~~^^^^\nConnectionRefusedError: [Errno 111] Connection refused\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n  File \"/usr/local/lib/python3.13/site-packages/urllib3/connectionpool.py\", line 787, in urlopen\n    response = self._make_request(\n        conn,\n    ...<10 lines>...\n        **response_kw,\n    )\n  File \"/usr/local/lib/python3.13/site-packages/urllib3/connectionpool.py\", line 493, in _make_request\n    conn.request(\n    ~~~~~~~~~~~~^\n        method,\n        ^^^^^^^\n    ...<6 lines>...\n        enforce_content_length=enforce_content_length,\n        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n    )\n    ^\n  File \"/usr/local/lib/python3.13/site-packages/urllib3/connection.py\", line 500, in request\n    self.endheaders()\n    ~~~~~~~~~~~~~~~^^\n  File \"/usr/local/lib/python3.13/http/client.py\", line 1331, in endheaders\n    self._send_output(message_body, encode_chunked=encode_chunked)\n    ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.13/http/client.py\", line 1091, in _send_output\n    self.send(msg)\n    ~~~~~~~~~^^^^^\n  File \"/usr/local/lib/python3.13/http/client.py\", line 1035, in send\n    self.connect()\n    ~~~~~~~~~~~~^^\n  File \"/usr/local/lib/python3.13/site-packages/urllib3/connection.py\", line 331, in connect\n    self.sock = self._new_conn()\n                ~~~~~~~~~~~~~~^^\n  File \"/usr/local/lib/python3.13/site-packages/urllib3/connection.py\", line 219, in _new_conn\n    raise NewConnectionError(\n        self, f\"Failed to establish a new connection: {e}\"\n    ) from e\nurllib3.exceptions.NewConnectionError: HTTPConnection(host='localhost', port=8000): Failed to establish a new connection: [Errno 111] Connection refused\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n  File \"/usr/local/lib/python3.13/site-packages/requests/adapters.py\", line 667, in send\n    resp = conn.urlopen(\n        method=request.method,\n    ...<9 lines>...\n        chunked=chunked,\n    )\n  File \"/usr/local/lib/python3.13/site-packages/urllib3/connectionpool.py\", line 841, in urlopen\n    retries = retries.increment(\n        method, url, error=new_e, _pool=self, _stacktrace=sys.exc_info()[2]\n    )\n  File \"/usr/local/lib/python3.13/site-packages/urllib3/util/retry.py\", line 535, in increment\n    raise MaxRetryError(_pool, url, reason) from reason  # type: ignore[arg-type]\n    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nurllib3.exceptions.MaxRetryError: HTTPConnectionPool(host='localhost', port=8000): Max retries exceeded with url: /health (Caused by NewConnectionError(\"HTTPConnection(host='localhost', port=8000): Failed to establish a new connection: [Errno 111] Connection refused\"))\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n  File \"<string>\", line 1, in <module>\n    import requests; r = requests.get('http://localhost:8000/health'); print('Status:', r.status_code); print('Response:', r.json())\n                         ~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.13/site-packages/requests/api.py\", line 73, in get\n    return request(\"get\", url, params=params, **kwargs)\n  File \"/usr/local/lib/python3.13/site-packages/requests/api.py\", li

<think>Let me wait for the server to start and then check the health endpoint.
</think>

→ terminal({"command": "sleep 8 && python3 -c \"import requests; r = requests.get('http://localhost:8000/health'); print('Status:', r.status_code); print('Response:', r.json())\"", "timeout": 30})

{"session_id": "proc_62498bba4679", "status": "exited", "output": "", "total_lines": 0, "showing": "0 lines"}

<think>Let me check the process log to see what's happening with the server.
</think>

→ process({"action": "log", "session_id": "proc_62498bba4679"})

{"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"}

<think>The process is exiting. Let me try running it directly to see the error.
</think>

→ terminal({"command": "cd /app && timeout 10 python3 -m uvicorn api.app:app --host 0.0.0.0 --port 8000 2>&1 || true", "timeout": 30})

{"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"}

<think>Let me try a different approach - run the server directly with subprocess.
</think>

→ terminal({"command": "cd /app && python3 -c \"\nimport subprocess\nimport sys\nimport time\n\n# Start the server\nproc = subprocess.Popen(\n    [sys.executable, '-m', 'uvicorn', 'api.app:app', '--host', '0.0.0.0', '--port', '8000'],\n    stdout=subprocess.PIPE,\n    stderr=subprocess.PIPE,\n    text=True\n)\n\n# Wait for startup\ntime.sleep(8)\n\n# Check if it's running\nimport requests\ntry:\n    r = requ…)

{"output": "Background process started", "session_id": "proc_0f5f4f89e8fa", "pid": null, "exit_code": 0, "error": null}

<think>The system is blocking foreground commands that start long-lived processes. Let me try using background=true properly.
</think>

→ 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": ""}

<think>Let me wait and check the status.
</think>

→ process({"action": "wait", "session_id": "proc_0f5f4f89e8fa", "timeout": 15})

{"session_id": "proc_0f5f4f89e8fa", "status": "exited", "output": "", "total_lines": 0, "showing": "0 lines"}

<think>The process keeps exiting. Let me check the log and also try to understand why it's failing.
</think>

→ process({"action": "log", "session_id": "proc_0f5f4f89e8fa"})

{"output": "Loading model from MLflow Model Registry...\nError loading model: INVALID_PARAMETER_VALUE: Model version must be an integer, got 'Production'\nTraceback (most recent call last):\n  File \"/app/api/app.py\", line 53, in load_model_from_registry\n    model_version_info = client.get_model_version(\n        name=MODEL_NAME,\n        version=STAGE\n    )\n  File \"/usr/local/lib/python3.13/site-packages/mlflow/tracking/client.py\", line 4122, in get_model_version\n    return self._get_registry_client().get_model_version(name, version)\n           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.13/site-packages/mlflow/tracking/_model_registry/client.py\", line 299, in get_model_version\n    return self.store.get_model_version(name, version)\n           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.13/site-packages/mlflow/store/model_registry/rest_store.py\", line 344, in get_model_version\n    response_proto = self._call_endpoint(GetModelVersion, req_body)\n  File \"/usr/local/lib/python3.13/site-packages/mlflow/store/model_registry/base_rest_store.py\", line 44, in _call_endpoint\n    return call_endpoint(\n        self.get_host_creds(), endpoint, method, json_body, response_proto, extra_headers\n    )\n  File \"/usr/local/lib/python3.13/site-packages/mlflow/utils/rest_utils.py\", line 370, in call_endpoint\n    response = verify_rest_response(response, endpoint)\n  File \"/usr/local/lib/python3.13/site-packages/mlflow/utils/rest_utils.py\", line 240, in verify_rest_response\n    raise RestException(json.loads(response.text))\nmlflow.exceptions.RestException: INVALID_PARAMETER_VALUE: Model version must be an integer, got 'Production'\nOK", "exit_code": 0, "error": null}

<think>The process keeps exiting without any output. Let me check if there's an issue with the API module itself by running it directly.
</think>

→ terminal({"command": "cd /app && python3 -c \"from api import app; print('OK')\" 2>&1", "timeout": 30})

{"bytes_written": 6844, "dirs_created": true}

<think>I see the issue. The `get_model_version` method expects an integer version, not a stage name. I need to use a different method to get the model version by stage. Let me fix the API.
</think>

I see the issue - the API is using the wrong method to get model by stage. Let me fix it:

→ write_file({"path": "/app/api/app.py", "content": "\"\"\"\nFastAPI application for Breast Cancer Classification\n- Loads registered model from MLflow Model Registry\n- Provides prediction endpoints\n\"\"\"\n\nfrom fastapi import FastAPI, HTTPException\nfrom pydantic import BaseModel, Field\nfrom typing import List\nimport mlflow\nfrom mlflow.tracking import MlflowClient\nfrom mlflow.pyfunc import load_model …)

{"output": "Loading model from MLflow Model Registry...\nError loading model: INVALID_PARAMETER_VALUE: Invalid Model Version stage: P. Value must be one of None, Staging, Production, Archived.\nTraceback (most recent call last):\n  File \"/app/api/app.py\", line 54, in load_model_from_registry\n    latest_versions = client.get_latest_versions(MODEL_NAME, STAGE)\n  File \"/usr/local/lib/python3.13/site-packages/mlflow/utils/annotations.py\", line 164, in deprecated_func\n    return obj(*args, **kwargs)\n  File \"/usr/local/lib/python3.13/site-packages/mlflow/tracking/client.py\", line 3493, in get_latest_versions\n    return self._get_registry_client().get_latest_versions(name, stages)\n           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.13/site-packages/mlflow/tracking/_model_registry/client.py\", line 153, in get_latest_versions\n    return self.store.get_latest_versions(name, stages)\n           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.13/site-packages/mlflow/store/model_registry/rest_store.py\", line 193, in get_latest_versions\n    response_proto = self._call_endpoint(GetLatestVersions, req_body, call_all_endpoints=True)\n  File \"/usr/local/lib/python3.13/site-packages/mlflow/store/model_registry/base_rest_store.py\", line 39, in _call_endpoint\n    return call_endpoints(\n        self.get_host_creds(), endpoints, json_body, response_proto, extra_headers\n    )\n  File \"/usr/local/lib/python3.13/site-packages/mlflow/utils/rest_utils.py\", line 387, in call_endpoints\n    raise e\n  File \"/usr/local/lib/python3.13/site-packages/mlflow/utils/rest_utils.py\", line 382, in call_endpoints\n    return call_endpoint(\n        host_creds, endpoint, method, json_body, response_proto, extra_headers\n    )\n  File \"/usr/local/lib/python3.13/site-packages/mlflow/utils/rest_utils.py\", line 370, in call_endpoint\n    response = verify_rest_response(response, endpoint)\n  File \"/usr/local/lib/python3.13/site-packages/mlflow/utils/rest_utils.py\", line 240, in verify_rest_response\n    raise RestException(json.loads(response.text))\nmlflow.exceptions.RestException: INVALID_PARAMETER_VALUE: Invalid Model Version stage: P. Value must be one of None, Staging, Production, Archived.\nOK", "exit_code": 0, "error": null}

<think>Let me test if the model loads now.
</think>

→ terminal({"command": "cd /app && python3 -c \"from api import app; print('OK')\" 2>&1", "timeout": 30})