breast-cancer-mlflow

grader verdict: FAIL (exit 0)
 - FileNotFoun...
FAILED ../tests/test_outputs.py::test_test_predictions_f1 - FileNotFoundError...
FAILED ../tests/test_outputs.py::test_test_predictions_precision_recall - Fil...
FAILED ../tests/test_outputs.py::test_val_test_performance_consistency - File...
FAILED ../tests/test_outputs.py::test_class_prediction_distribution - FileNot...
FAILED ../tests/test_outputs.py::test_model_reproducible_from_hyperparameters
======================== 18 failed, 16 passed in 7.59s =========================

You are an expert terminal agent. You MUST use the provided tools to complete tasks. Use the terminal tool to run shell commands, read_file to read files, write_file to write files, search_files to search, and patch to edit files. Do NOT write out solutions as text - execute them using the tools. Always start by exploring the environment with terminal commands.

## Goal

Build a complete machine learning classification pipeline for breast cancer diagnosis with MLflow experiment tracking, model registry, and FastAPI deployment. Train multiple classification models, track experiments with MLflow, register the best model, and serve predictions through a REST API.

## Background

You will work with pre-split Breast Cancer Wisconsin data containing diagnostic information for classifying tumors as malignant (0) or benign (1). The dataset includes 30 numerical features computed from digitized images of fine needle aspirate (FNA) of breast masses. The training data includes the target column, but the test data does NOT include the target column - you must predict it.

Data files are located at:
- /app/data/train.csv - Training data with 30 features + target column (227 samples, 31 columns)
- /app/data/test.csv - Test data with 30 features only, NO target column (342 samples, 30 columns)

You have access to scikit-learn (version 1.7.2), MLflow (version 2.19.0), FastAPI (version 0.115.6), uvicorn (version 0.34.0), pandas (version 2.2.3), and requests (version 2.32.3).

## Steps

### Step 1. Load and Explore the Data

- Load training data from /app/data/train.csv
  - Contains 30 feature columns + "target" column (31 columns total)
  - Target: 0=malignant, 1=benign
- Load test data from /app/data/test.csv
  - Contains 30 feature columns only (NO target column, 30 columns total)
  - You must predict the target values for these samples
- Verify the data structure and check for any missing values
- For training, split train.csv into train/validation sets:
  - test_size=0.2
  - random_state=42
  - stratify=y (maintain class distribution)
- Save the split information to /app/output/split_info.json:
  ```json
  {
    "random_state": 42,
    "test_size": 0.2,
    "train_size": <int>,
    "val_size": <int>,
    "train_indices": [<list of original indices used for training>],
    "val_indices": [<list of original indices used for validation>]
  }
  ```

### Step 2. Connect to MLflow Tracking Server

An MLflow tracking server is already running at http://127.0.0.1:5000.
- Set MLflow tracking URI to http://127.0.0.1:5000 in your training script
- The server uses /app/mlflow directory for storage

### Step 3. Train Multiple Classification Models with MLflow Tracking

Train at least THREE different classification models with MLflow experiment tracking:

**Model 1 - Logistic Regression** (LogisticRegression from sklearn.linear_model)
- Allowed parameters (use only these): C, max_iter, solver, penalty, random_state
- Create an MLflow experiment named "breast-cancer-classification"
- Log parameters: model_type="logistic_regression" and all hyperparameters used
- Log metrics: train_accuracy, val_accuracy, precision, recall, f1_score
- Log the trained model using mlflow.sklearn.log_model()

**Model 2 - Support Vector Machine** (SVC from sklearn.svm)
- Allowed parameters (use only these): kernel, C, gamma, degree, random_state
- Use the same experiment "breast-cancer-classification"
- Log parameters: model_type="svm" and all hyperparameters used
- Log metrics: train_accuracy, val_accuracy, precision, recall, f1_score
- Log the trained model using mlflow.sklearn.log_model()

**Model 3 - Random Forest Classifier** (RandomForestClassifier from sklearn.ensemble)
- Allowed parameters (use only these): n_estimators, max_depth, min_samples_split, min_samples_leaf, random_state
- Use the same experiment "breast-cancer-classification"
- Log parameters: model_type="random_forest" and all hyperparameters used
- Log metrics: train_accuracy, val_accuracy, precision, recall, f1_score
- Log the trained model using mlflow.sklearn.log_model()

Choose appropriate hyperparameters for each model to achieve good classification performance.

For EACH model:
- Start an MLflow run using mlflow.start_run()
- Train on the training data (X_train, y_train from train.csv split)
- Calculate training accuracy
- Calculate validation set metrics: 
… (8213 more chars)

I'll help you build a complete machine learning classification pipeline for breast cancer diagnosis. Let me start by exploring the environment and then proceed step by step.

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

{"output": "total 28\ndrwxr-xr-x 1 root root 4096 Mar  4 09:20 .\ndrwxr-xr-x 1 root root 4096 Apr 23 09:27 ..\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 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}

{"content": "     1|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\n     2|12.99,14.23,84.08,514.3,0.09462,0.09965,0.03738,0.02098,0.1652,0.07238,0.1814,0.6412,0.9219,14.41,0.005231,0.02305,0.03113,0.007315,0.01639,0.005701,13.72,16.91,87.38,576.0,0.1142,0.1975,0.145,0.0585,0.2432,0.1009,1\n     3|16.16,21.54,106.2,809.8,0.1008,0.1284,0.1043,0.05613,0.216,0.05891,0.4332,1.265,2.844,43.68,0.004877,0.01952,0.02219,0.009231,0.01535,0.002373,19.47,31.68,129.7,1175.0,0.1395,0.3055,0.2992,0.1312,0.348,0.07619,0\n     4|9.876,19.4,63.95,298.3,0.1005,0.09697,0.06154,0.03029,0.1945,0.06322,0.1803,1.222,1.528,11.77,0.009058,0.02196,0.03029,0.01112,0.01609,0.00357,10.76,26.83,72.22,361.2,0.1559,0.2302,0.2644,0.09749,0.2622,0.0849,1\n     5|11.62,18.18,76.38,408.8,0.1175,0.1483,0.102,0.05564,0.1957,0.07255,0.4101,1.74,3.027,27.85,0.01459,0.03206,0.04961,0.01841,0.01807,0.005217,13.36,25.4,88.14,528.1,0.178,0.2878,0.3186,0.1416,0.266,0.0927,1\n     6|13.0,25.13,82.61,520.2,0.08369,0.05073,0.01206,0.01762,0.1667,0.05449,0.2621,1.232,1.657,21.19,0.006054,0.008974,0.005681,0.006336,0.01215,0.001514,14.34,31.88,91.06,628.5,0.1218,0.1093,0.04462,0.05921,0.2306,0.06291,1\n     7|13.74,17.91,88.12,585.0,0.07944,0.06376,0.02881,0.01329,0.1473,0.0558,0.25,0.7574,1.573,21.47,0.002838,0.01592,0.0178,0.005828,0.01329,0.001976,15.34,22.46,97.19,725.9,0.09711,0.1824,0.1564,0.06019,0.235,0.07014,1\n     8|7.691,25.44,48.34,170.4,0.08668,0.1199,0.09252,0.01364,0.2037,0.07751,0.2196,1.479,1.445,11.73,0.01547,0.06457,0.09252,0.01364,0.02105,0.007551,8.678,31.89,54.49,223.6,0.1596,0.3064,0.3393,0.05,0.279,0.1066,1\n     9|14.29,16.82,90.3,632.6,0.06429,0.02675,0.00725,0.00625,0.1508,0.05376,0.1302,0.7198,0.8439,10.77,0.003492,0.00371,0.004826,0.003608,0.01536,0.001381,14.91,20.65,94.44,684.6,0.08567,0.05036,0.03866,0.03333,0.2458,0.0612,1\n    10|12.07,13.44,77.83,445.2,0.11,0.09009,0.03781,0.02798,0.1657,0.06608,0.2513,0.504,1.714,18.54,0.007327,0.01153,0.01798,0.007986,0.01962,0.002234,13.45,15.77,86.92,549.9,0.1521,0.1632,0.1622,0.07393,0.2781,0.08052,1\n    11|", "total_lines": 228, "file_size": 48762, "truncated": true, "hint": "Use offset=11 to continue reading (showing 1-10 of 228 lines)", "is_binary": false, "is_image": false}

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

terminal({"command": "ps aux | grep mlflow"})

{"output": "bash: line 3: ps: command not found", "exit_code": 1, "error": null, "exit_code_meaning": "No matches found (not an error)"}

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

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

Let me check the mlflow directory and create the necessary directories:

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

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

{"output": "total 240\ndrwxr-xr-x 1 root root   4096 Apr 23 09:27 .\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 23 09:27 mlflow.db", "exit_code": 0, "error": null}

Good! Now let me create the training script step by step. First, let me create the training script:

write_file({"path": "/app/train.py", "content": "#!/usr/bin/env python3\n\nimport os\nimport json\nimport pandas as pd\nimport numpy as np\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.svm import SVC\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.metrics import accuracy_score, precision_score, recall_score, f1_scor…)

{"bytes_written": 6113, "dirs_created": true}

Now let me run the training script to train the models:

terminal({"command": "cd /app && python train.py"})

{"output": "2026/04/23 09:35:06 INFO mlflow.tracking.fluent: Experiment with name 'breast-cancer-classification' does not exist. Creating a new experiment.\n2026/04/23 09:35:06 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\n2026/04/23 09:35:08 WARNING mlflow.utils.environment: Encountered an unexpected error while inferring pip requirements (model URI: /tmp/tmpmbdd8_rc/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. \n2026/04/23 09:35:08 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.\n2026/04/23 09:35:10 WARNING mlflow.utils.environment: Encountered an unexpected error while inferring pip requirements (model URI: /tmp/tmpu0j1xlha/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. \n2026/04/23 09:35:10 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.\n2026/04/23 09:35:12 WARNING mlflow.utils.environment: Encountered an unexpected error while inferring pip requirements (model URI: /tmp/tmpnxq13637/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. \n2026/04/23 09:35:12 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.\nLoading data...\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']\nTarget distribution: target\n1    0.625551\n0    0.374449\nName: proportion, dtype: float64\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']\nMissing values in training data: 0\nMissing v
… (1310 more chars)

Great! The models have been trained and logged to MLflow. Now let me verify the split_info.json file was created and then proceed to select the best model and register it:

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

{"content": "     1|{\n     2|  \"random_state\": 42,\n     3|  \"test_size\": 0.2,\n     4|  \"train_size\": 181,\n     5|  \"val_size\": 46,\n     6|  \"train_indices\": [\n     7|    60,\n     8|    172,\n     9|    141,\n    10|    90,\n    11|    180,\n    12|    80,\n    13|    150,\n    14|    133,\n    15|    169,\n    16|    154,\n    17|    98,\n    18|    81,\n    19|    136,\n    20|    166,\n    21|    35,\n    22|    184,\n    23|    29,\n    24|    41,\n    25|    48,\n    26|    45,\n    27|    140,\n    28|    177,\n    29|    76,\n    30|    196,\n    31|    126,\n    32|    144,\n    33|    75,\n    34|    44,\n    35|    74,\n    36|    130,\n    37|    92,\n    38|    115,\n    39|    215,\n    40|    21,\n    41|    43,\n    42|    167,\n    43|    181,\n    44|    139,\n    45|    121,\n    46|    120,\n    47|    189,\n    48|    137,\n    49|    102,\n    50|    207,\n    51|    124,\n    52|    62,\n    53|    182,\n    54|    34,\n    55|    104,\n    56|    200,\n    57|    63,\n    58|    105,\n    59|    78,\n    60|    165,\n    61|    221,\n    62|    148,\n    63|    99,\n    64|    176,\n    65|    53,\n    66|    173,\n    67|    153,\n    68|    185,\n    69|    23,\n    70|    86,\n    71|    70,\n    72|    127,\n    73|    201,\n    74|    143,\n    75|    108,\n    76|    26,\n    77|    51,\n    78|    163,\n    79|    211,\n    80|    170,\n    81|    112,\n    82|    195,\n    83|    32,\n    84|    206,\n    85|    52,\n    86|    174,\n    87|    224,\n    88|    114,\n    89|    204,\n    90|    125,\n    91|    69,\n    92|    91,\n    93|    4,\n    94|    220,\n    95|    107,\n    96|    191,\n    97|    17,\n    98|    188,\n    99|    24,\n   100|    155,\n   101|    25,\n   102|    73,\n   103|    113,\n   104|    156,\n   105|    123,\n   106|    10,\n   107|    95,\n   108|    217,\n   109|    116,\n   110|    138,\n   111|    216,\n   112|    57,\n   113|    30,\n   114|    213,\n   115|    19,\n   116|    72,\n   117|    175,\n   118|    203,\n   119|    135,\n   120|    22,\n   121|    66,\n   122|    103,\n   123|    122,\n   124|    97,\n   125|    190,\n   126|    85,\n   127|    146,\n   128|    158,\n   129|    82,\n   130|    218,\n   131|    101,\n   132|    100,\n   133|    36,\n   134|    65,\n   135|    6,\n   136|    39,\n   137|    40,\n   138|    61,\n   139|    210,\n   140|    56,\n   141|    164,\n   142|    33,\n   143|    219,\n   144|    27,\n   145|    0,\n   146|    223,\n   147|    187,\n   148|    64,\n   149|    55,\n   150|    202,\n   151|    8,\n   152|    226,\n   153|    118,\n   154|    12,\n   155|    212,\n   156|    161,\n   157|    15,\n   158|    18,\n   159|    142,\n   160|    222,\n   161|    179,\n   162|    128,\n   163|    1,\n   164|    178,\n   165|    89,\n   166|    109,\n   167|    50,\n   168|    83,\n   169|    38,\n   170|    84,\n   171|    42,\n   172|    159,\n   173|    152,\n   174|    111,\n   175|    77,\n   176|    2,\n   177|    28,\n   178|    183,\n   179|    96,\n   180|    3,\n   181|    129,\n   182|    157,\n   183|    160,\n   184|    49,\n   185|    214,\n   186|    194,\n   187|    14\n   188|  ],\n   189|  \"val_indices\": [\n   190|    145,\n   191|    59,\n   192|    79,\n   193|    117,\n   194|    151,\n   195|    171,\n   196|    37,\n   197|    208,\n   198|    106,\n   199|    9,\n   200|    5,\n   201|    87,\n   202|    88,\n   203|    11,\n   204|    67,\n   205|    199,\n   206|    197,\n   207|    132,\n   208|    192,\n   209|    93,\n   210|    134,\n   211|    147,\n   212|    198,\n   213|    209,\n   214|    68,\n   215|    71,\n   216|    119,\n   217|    58,\n   218|    47,\n   219|    54,\n   220|    162,\n   221|    186,\n   222|    46,\n   223|    149,\n   224|    193,\n   225|    16,\n   226|    13,\n   227|    110,\n   228|    205,\n   229|    7,\n   230|    225,\n   231|    20,\n   232|    131,\n   233|    31,\n   234|    168,\n   235|    94\n   236|  ]\n   237|}", "total_lines"
… (84 more chars)

Perfect! Now let me create a script to select the best model and register it to the MLflow Model Registry:

write_file({"path": "/app/register_model.py", "content": "#!/usr/bin/env python3\n\nimport json\nimport mlflow\nfrom mlflow.tracking import MlflowClient\n\n# Set MLflow tracking URI\nmlflow.set_tracking_uri(\"http://127.0.0.1:5000\")\n\n# Initialize MLflow client\nclient = MlflowClient()\n\n# Get experiment\nexperiment_name = \"breast-cancer-classification\"\nexperiment = mlflow.get_experiment_by_name(experi…)

{"bytes_written": 5135, "dirs_created": true}

{"output": "Found experiment: breast-cancer-classification (ID: 1)\n\nFound 3 runs in the experiment:\n\nRun 1:\n  Run ID: e5af933da24f400283243309e150e6b2\n  Model Type: random_forest\n  Train Accuracy: 0.9945\n  Val Accuracy: 0.9130\n  Precision: 0.9032\n  Recall: 0.9655\n  F1 Score: 0.9333\n  Hyperparameters: {'min_samples_split': '5', 'random_state': '42', 'max_depth': '10', 'min_samples_leaf': '2', 'n_estimators': '100', 'kernel': None, 'degree': None, 'C': None, 'gamma': None, 'solver': None, 'penalty': None, 'max_iter': None}\n\nRun 2:\n  Run ID: e5f3e681482a4149ad0ac7aed5fa4d31\n  Model Type: svm\n  Train Accuracy: 0.9227\n  Val Accuracy: 0.9130\n  Precision: 0.8788\n  Recall: 1.0000\n  F1 Score: 0.9355\n  Hyperparameters: {'min_samples_split': None, 'random_state': '42', 'max_depth': None, 'min_samples_leaf': None, 'n_estimators': None, 'kernel': 'rbf', 'degree': '3', 'C': '1.0', 'gamma': 'scale', 'solver': None, 'penalty': None, 'max_iter': None}\n\nRun 3:\n  Run ID: dbd4d90ab9864b368daeb74d3b3ade23\n  Model Type: logistic_regression\n  Train Accuracy: 0.9503\n  Val Accuracy: 0.9130\n  Precision: 0.8788\n  Recall: 1.0000\n  F1 Score: 0.9355\n  Hyperparameters: {'min_samples_split': None, 'random_state': '42', 'max_depth': None, 'min_samples_leaf': None, 'n_estimators': None, 'kernel': None, 'degree': None, 'C': '0.1', 'gamma': None, 'solver': 'liblinear', 'penalty': 'l2', 'max_iter': '1000'}\n\n=== Best Model ===\nBest Run ID: e5af933da24f400283243309e150e6b2\nBest Model Type: random_forest\nBest Val Accuracy: 0.9130\nTraceback (most recent call last):\n  File \"/app/register_model.py\", line 83, in <module>\n    registered_model = client.create_model(\n                       ^^^^^^^^^^^^^^^^^^^\nAttributeError: 'MlflowClient' object has no attribute 'create_model'", "exit_code": 1, "error": null}

I need to fix the model registration code. Let me update it:

patch({"path": "/app/register_model.py", "old_string": "# Register the best model\nmodel_name = \"breast-cancer-classifier\"\nmodel_uri = f\"runs:/{best_run['run_id']}/model\"\n\ntry:\n    # Register the model\n    registered_model = client.create_model(\n        name=model_name,\n        source=model_uri,\n        run_id=best_run[\"run_id\"]\n    )\n    print(f\"Model registered with version {registere…)

{"success": true, "diff": "--- a//app/register_model.py\n+++ b//app/register_model.py\n@@ -80,10 +80,9 @@\n \n try:\n     # Register the model\n-    registered_model = client.create_model(\n-        name=model_name,\n-        source=model_uri,\n-        run_id=best_run[\"run_id\"]\n+    registered_model = mlflow.register_model(\n+        model_uri=model_uri,\n+        name=model_name\n     )\n     print(f\"Model registered with version {registered_model.version}\")\n     \n", "files_modified": ["/app/register_model.py"], "lint": {"status": "ok", "output": ""}}

{"output": "Successfully registered model 'breast-cancer-classifier'.\n2026/04/23 09:42:59 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/register_model.py:90: 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(\nFound experiment: breast-cancer-classification (ID: 1)\n\nFound 3 runs in the experiment:\n\nRun 1:\n  Run ID: e5af933da24f400283243309e150e6b2\n  Model Type: random_forest\n  Train Accuracy: 0.9945\n  Val Accuracy: 0.9130\n  Precision: 0.9032\n  Recall: 0.9655\n  F1 Score: 0.9333\n  Hyperparameters: {'max_depth': '10', 'random_state': '42', 'n_estimators': '100', 'min_samples_split': '5', 'min_samples_leaf': '2', 'kernel': None, 'C': None, 'gamma': None, 'degree': None, 'max_iter': None, 'penalty': None, 'solver': None}\n\nRun 2:\n  Run ID: e5f3e681482a4149ad0ac7aed5fa4d31\n  Model Type: svm\n  Train Accuracy: 0.9227\n  Val Accuracy: 0.9130\n  Precision: 0.8788\n  Recall: 1.0000\n  F1 Score: 0.9355\n  Hyperparameters: {'max_depth': None, 'random_state': '42', 'n_estimators': None, 'min_samples_split': None, 'min_samples_leaf': None, 'kernel': 'rbf', 'C': '1.0', 'gamma': 'scale', 'degree': '3', 'max_iter': None, 'penalty': None, 'solver': None}\n\nRun 3:\n  Run ID: dbd4d90ab9864b368daeb74d3b3ade23\n  Model Type: logistic_regression\n  Train Accuracy: 0.9503\n  Val Accuracy: 0.9130\n  Precision: 0.8788\n  Recall: 1.0000\n  F1 Score: 0.9355\n  Hyperparameters: {'max_depth': None, 'random_state': '42', 'n_estimators': None, 'min_samples_split': None, 'min_samples_leaf': None, 'kernel': None, 'C': '0.1', 'gamma': None, 'degree': None, 'max_iter': '1000', 'penalty': 'l2', 'solver': 'liblinear'}\n\n=== Best Model ===\nBest Run ID: e5af933da24f400283243309e150e6b2\nBest Model Type: random_forest\nBest Val Accuracy: 0.9130\nModel registered with version 1\nModel version 1 moved to Production stage\n\nExperiment results saved to /app/output/experiments.json\nBest model info saved to /app/output/best_model_info.json\n\nModel registration complete!", "exit_code": 0, "error": null}

Great! Now let me verify the JSON files were created correctly:

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

{"content": "     1|{\n     2|  \"experiment_name\": \"breast-cancer-classification\",\n     3|  \"runs\": [\n     4|    {\n     5|      \"run_id\": \"e5af933da24f400283243309e150e6b2\",\n     6|      \"model_type\": \"random_forest\",\n     7|      \"hyperparameters\": {\n     8|        \"max_depth\": \"10\",\n     9|        \"random_state\": \"42\",\n    10|        \"n_estimators\": \"100\",\n    11|        \"min_samples_split\": \"5\",\n    12|        \"min_samples_leaf\": \"2\",\n    13|        \"kernel\": null,\n    14|        \"C\": null,\n    15|        \"gamma\": null,\n    16|        \"degree\": null,\n    17|        \"max_iter\": null,\n    18|        \"penalty\": null,\n    19|        \"solver\": null\n    20|      },\n    21|      \"train_accuracy\": 0.994475138121547,\n    22|      \"val_accuracy\": 0.9130434782608695,\n    23|      \"precision\": 0.9032258064516129,\n    24|      \"recall\": 0.9655172413793104,\n    25|      \"f1_score\": 0.9333333333333333\n    26|    },\n    27|    {\n    28|      \"run_id\": \"e5f3e681482a4149ad0ac7aed5fa4d31\",\n    29|      \"model_type\": \"svm\",\n    30|      \"hyperparameters\": {\n    31|        \"max_depth\": null,\n    32|        \"random_state\": \"42\",\n    33|        \"n_estimators\": null,\n    34|        \"min_samples_split\": null,\n    35|        \"min_samples_leaf\": null,\n    36|        \"kernel\": \"rbf\",\n    37|        \"C\": \"1.0\",\n    38|        \"gamma\": \"scale\",\n    39|        \"degree\": \"3\",\n    40|        \"max_iter\": null,\n    41|        \"penalty\": null,\n    42|        \"solver\": null\n    43|      },\n    44|      \"train_accuracy\": 0.9226519337016574,\n    45|      \"val_accuracy\": 0.9130434782608695,\n    46|      \"precision\": 0.8787878787878788,\n    47|      \"recall\": 1.0,\n    48|      \"f1_score\": 0.9354838709677419\n    49|    },\n    50|    {\n    51|      \"run_id\": \"dbd4d90ab9864b368daeb74d3b3ade23\",\n    52|      \"model_type\": \"logistic_regression\",\n    53|      \"hyperparameters\": {\n    54|        \"max_depth\": null,\n    55|        \"random_state\": \"42\",\n    56|        \"n_estimators\": null,\n    57|        \"min_samples_split\": null,\n    58|        \"min_samples_leaf\": null,\n    59|        \"kernel\": null,\n    60|        \"C\": \"0.1\",\n    61|        \"gamma\": null,\n    62|        \"degree\": null,\n    63|        \"max_iter\": \"1000\",\n    64|        \"penalty\": \"l2\",\n    65|        \"solver\": \"liblinear\"\n    66|      },\n    67|      \"train_accuracy\": 0.9502762430939227,\n    68|      \"val_accuracy\": 0.9130434782608695,\n    69|      \"precision\": 0.8787878787878788,\n    70|      \"recall\": 1.0,\n    71|      \"f1_score\": 0.9354838709677419\n    72|    }\n    73|  ],\n    74|  \"best_run_id\": \"e5af933da24f400283243309e150e6b2\",\n    75|  \"best_model_type\": \"random_forest\"\n    76|}", "total_lines": 75, "file_size": 2108, "truncated": false, "is_binary": false, "is_image": false}

{"content": "     1|{\n     2|  \"model_name\": \"breast-cancer-classifier\",\n     3|  \"model_version\": \"1\",\n     4|  \"model_stage\": \"Production\",\n     5|  \"run_id\": \"e5af933da24f400283243309e150e6b2\",\n     6|  \"model_type\": \"random_forest\",\n     7|  \"hyperparameters\": {\n     8|    \"max_depth\": \"10\",\n     9|    \"random_state\": \"42\",\n    10|    \"n_estimators\": \"100\",\n    11|    \"min_samples_split\": \"5\",\n    12|    \"min_samples_leaf\": \"2\",\n    13|    \"kernel\": null,\n    14|    \"C\": null,\n    15|    \"gamma\": null,\n    16|    \"degree\": null,\n    17|    \"max_iter\": null,\n    18|    \"penalty\": null,\n    19|    \"solver\": null\n    20|  },\n    21|  \"val_accuracy\": 0.9130434782608695,\n    22|  \"precision\": 0.9032258064516129,\n    23|  \"recall\": 0.9655172413793104,\n    24|  \"f1_score\": 0.9333333333333333\n    25|}", "total_lines": 24, "file_size": 620, "truncated": false, "is_binary": false, "is_image": false}

Perfect! Now let me create the FastAPI application:

write_file({"path": "/app/api/app.py", "content": "#!/usr/bin/env python3\n\nfrom fastapi import FastAPI, HTTPException\nfrom pydantic import BaseModel\nimport mlflow\nimport mlflow.sklearn\nimport pandas as pd\nimport numpy as np\nfrom typing import List\n\n# Set MLflow tracking URI\nmlflow.set_tracking_uri(\"http://127.0.0.1:5000\")\n\napp = FastAPI(title=\"Breast Cancer Classifier API\", version=\"1.0.0\"…)

{"bytes_written": 6357, "dirs_created": true}

Now let me start the FastAPI server in the background:

terminal({"command": "cd /app && uvicorn api.app:app --host 0.0.0.0 --port 8000 --reload", "background": true, "notify_on_complete": true, "timeout": 60})

{"output": "Background process started", "session_id": "proc_d6493c085b8a", "pid": null, "exit_code": 0, "error": null, "notify_on_complete": true}

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

Let me check what happened and try a different approach:

process({"action": "log", "session_id": "proc_d6493c085b8a"})

{"session_id": "proc_d6493c085b8a", "status": "exited", "output": "", "total_lines": 0, "showing": "0 lines"}

Let me try starting the server without the reload option:

terminal({"command": "cd /app && nohup uvicorn api.app:app --host 0.0.0.0 --port 8000 &", "background": true, "notify_on_complete": true, "timeout": 10})

{"output": "Background process started", "session_id": "proc_243d69a5bedc", "pid": null, "exit_code": 0, "error": null, "notify_on_complete": true}

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

Let me check if there are any issues with the FastAPI app by testing the model loading:

write_file({"path": "/app/test_model_loading.py", "content": "#!/usr/bin/env python3\n\nimport mlflow\nimport mlflow.sklearn\nfrom mlflow.tracking import MlflowClient\n\n# Set MLflow tracking URI\nmlflow.set_tracking_uri(\"http://127.0.0.1:5000\")\n\nprint(\"Testing model loading...\")\n\ntry:\n    # Load model from registry\n    model = mlflow.sklearn.load_model(\n        model_uri=\"models:/breast-cancer-c…)

{"bytes_written": 1801, "dirs_created": true}

{"output": "/usr/local/lib/python3.13/site-packages/mlflow/store/artifact/utils/models.py:31: FutureWarning: ``mlflow.tracking.client.MlflowClient.get_latest_versions`` 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  latest = client.get_latest_versions(name, None if stage is None else [stage])\n/app/test_model_loading.py:22: FutureWarning: ``mlflow.tracking.client.MlflowClient.get_latest_versions`` 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  model_version = client.get_latest_versions(\"breast-cancer-classifier\", stages=[\"Production\"])[0]\nTesting model loading...\nModel loaded successfully: <class 'sklearn.ensemble._forest.RandomForestClassifier'>\nModel version: 1\nModel stage: Production\nTest prediction: 1\nModel loading test passed!", "exit_code": 0, "error": null}

Great! The model loading works. Let me try a simpler approach to start the FastAPI server:

terminal({"command": "cd /app && python -c \"import uvicorn; uvicorn.run('api.app:app', host='0.0.0.0', port=8000)\" &", "background": true, "notify_on_complete": true, "timeout": 10})

{"output": "Background process started", "session_id": "proc_ffd818307f01", "pid": null, "exit_code": 0, "error": null, "notify_on_complete": true}

{"status": "not_found", "error": "No process with ID proc_ffd818307f08"}

{"processes": [{"session_id": "proc_d6493c085b8a", "command": "cd /app && uvicorn api.app:app --host 0.0.0.0 --port 8000 --reload", "cwd": "/app", "pid": null, "started_at": "2026-04-23T04:48:34", "uptime_seconds": 179, "status": "exited", "output_preview": "", "exit_code": -1}, {"session_id": "proc_243d69a5bedc", "command": "cd /app && nohup uvicorn api.app:app --host 0.0.0.0 --port 8000 &", "cwd": "/app", "pid": null, "started_at": "2026-04-23T04:49:03", "uptime_seconds": 150, "status": "exited", "output_preview": "", "exit_code": -1}, {"session_id": "proc_ffd818307f01", "command": "cd /app && python -c \"import uvicorn; uvicorn.run('api.app:app', host='0.0.0.0', port=8000)\" &", "cwd": "/app", "pid": null, "started_at": "2026-04-23T04:51:24", "uptime_seconds": 9, "status": "exited", "output_preview": "", "exit_code": -1}]}

Let me try a different approach. Let me create a simple script to start the server:

write_file({"path": "/app/start_server.sh", "content": "#!/bin/bash\n\ncd /app\n\n# Start uvicorn in the background\nuvicorn api.app:app --host 0.0.0.0 --port 8000 &\n\n# Wait for the server to start\nsleep 5\n\n# Check if the server is running\necho \"Server started on port 8000\""})

{"bytes_written": 213, "dirs_created": true}

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

{"output": "Background process started", "session_id": "proc_f752b49fc461", "pid": null, "exit_code": 0, "error": null, "notify_on_complete": true}

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

Let me check what's happening by running the server directly and capturing output:

terminal({"command": "cd /app && python -c \"import sys; sys.path.insert(0, '.'); from api.app import app; import uvicorn; uvicorn.run(app, host='0.0.0.0', port=8000)\" > /tmp/server.log 2>&1 & echo $!"})

{"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"}

{"output": "Background process started", "session_id": "proc_a588bf2bb298", "pid": null, "exit_code": 0, "error": null}

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

Let me try a different approach - let me create a simple server startup script:

write_file({"path": "/app/start_api.py", "content": "#!/usr/bin/env python3\n\nimport sys\nimport uvicorn\nfrom api.app import app\n\nif __name__ == \"__main__\":\n    uvicorn.run(app, host=\"0.0.0.0\", port=8000)"})

{"bytes_written": 149, "dirs_created": true}

{"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"}

{"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"}

Let me try a different approach. Let me check if there are any issues with the FastAPI app by creating a simple test:

write_file({"path": "/app/test_api.py", "content": "#!/usr/bin/env python3\n\nimport sys\nimport os\nsys.path.insert(0, '/app')\n\nfrom api.app import app\nfrom fastapi.testclient import TestClient\n\n# Create test client\nclient = TestClient(app)\n\nprint(\"Testing API endpoints...\")\n\n# Test health endpoint\nresponse = client.get(\"/health\")\nprint(f\"Health endpoint: {response.status_code}\")\nprint(f\…)

{"bytes_written": 1041, "dirs_created": true}

{"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 \"/app/test_api.py\", line 8, 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}

Let me install httpx and try again:

terminal({"command": "pip install httpx"})

{"output": "WARNING: 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.\nCollecting httpx\n  Downloading httpx-0.28.1-py3-none-any.whl.metadata (7.1 kB)\nRequirement already satisfied: anyio in /usr/local/lib/python3.13/site-packages (from httpx) (4.12.1)\nRequirement already satisfied: certifi in /usr/local/lib/python3.13/site-packages (from httpx) (2026.2.25)\nCollecting httpcore==1.* (from httpx)\n  Downloading httpcore-1.0.9-py3-none-any.whl.metadata (21 kB)\nRequirement already satisfied: idna in /usr/local/lib/python3.13/site-packages (from httpx) (3.11)\nRequirement already satisfied: h11>=0.16 in /usr/local/lib/python3.13/site-packages (from httpcore==1.*->httpx) (0.16.0)\nDownloading httpx-0.28.1-py3-none-any.whl (73 kB)\nDownloading httpcore-1.0.9-py3-none-any.whl (78 kB)\nInstalling collected packages: httpcore, httpx\nSuccessfully 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}

{"output": "Testing API endpoints...\nHealth endpoint: 200\nResponse: {'status': 'healthy', 'model_loaded': False, 'mlflow_tracking_uri': 'http://127.0.0.1:5000'}\nModel-info endpoint: 500\nResponse: {'detail': 'Model not loaded'}\nPredict endpoint: 500\nResponse: {'detail': 'Model not loaded'}\nBatch predict endpoint: 500\nResponse: {'detail': 'Model not loaded'}\nAPI tests completed!", "exit_code": 0, "error": null}

I see the issue - the model is not loading in the test client because the startup event doesn't run. Let me fix the FastAPI app to handle this better:

patch({"path": "/app/api/app.py", "old_string": "async def load_model():\n    \"\"\"Load the model from MLflow Model Registry\"\"\"\n    global model, model_info\n    \n    try:\n        # Load model from registry\n        model = mlflow.sklearn.load_model(\n            model_uri=\"models:/breast-cancer-classifier/Production\"\n        )\n        \n        # Get model info\n        client = mlflow.track…)

{"success": true, "diff": "--- a//app/api/app.py\n+++ b//app/api/app.py\n@@ -46,7 +46,7 @@\n     model_stage: str\n     model_type: str\n \n-async def load_model():\n+def load_model():\n     \"\"\"Load the model from MLflow Model Registry\"\"\"\n     global model, model_info\n     \n", "files_modified": ["/app/api/app.py"], "lint": {"status": "ok", "output": ""}}

{"success": true, "diff": "--- a//app/api/app.py\n+++ b//app/api/app.py\n@@ -73,10 +73,11 @@\n         print(f\"Error loading model: {e}\")\n         raise\n \n-@app.on_event(\"startup\")\n-async def startup_event():\n-    \"\"\"Load model on startup\"\"\"\n-    await load_model()\n+# Load model at module import time\n+try:\n+    load_model()\n+except Exception as e:\n+    print(f\"Warning: Model not loaded at startup: {e}\")\n \n @app.get(\"/health\", response_model=HealthResponse)\n async def health():\n", "files_modified": ["/app/api/app.py"], "lint": {"status": "ok", "output": ""}}

{"output": "/usr/local/lib/python3.13/site-packages/mlflow/store/artifact/utils/models.py:31: FutureWarning: ``mlflow.tracking.client.MlflowClient.get_latest_versions`` 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  latest = client.get_latest_versions(name, None if stage is None else [stage])\n/app/api/app.py:61: FutureWarning: ``mlflow.tracking.client.MlflowClient.get_latest_versions`` 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  model_version = client.get_latest_versions(\"breast-cancer-classifier\", stages=[\"Production\"])[0]\nModel loaded successfully: {'model_name': 'breast-cancer-classifier', 'model_version': '1', 'model_stage': 'Production', 'model_type': 'random_forest'}\nTesting API endpoints...\nHealth endpoint: 200\nResponse: {'status': 'healthy', 'model_loaded': True, 'mlflow_tracking_uri': 'http://127.0.0.1:5000'}\nModel-info endpoint: 200\nResponse: {'model_name': 'breast-cancer-classifier', 'model_version': 1, 'model_stage': 'Production', 'model_type': 'random_forest'}\nPredict endpoint: 200\nResponse: {'prediction': 1, 'prediction_label': 'benign', 'model_name': 'breast-cancer-classifier', 'model_version': 1}\nTraceback (most recent call last):\n  File \"/app/test_api.py\", line 33, in <module>\n    response = client.post(\"/predict-batch\", json={\"samples\": batch_samples})\n  File \"/usr/local/lib/python3.13/site-packages/starlette/testclient.py\", line 593, in post\n    return super().post(\n           ~~~~~~~~~~~~^\n        url,\n        ^^^^\n    ...<10 lines>...\n        extensions=extensions,\n        ^^^^^^^^^^^^^^^^^^^^^^\n    )\n    ^\n  File \"/usr/local/lib/python3.13/site-packages/httpx/_client.py\", line 1144, in post\n    return self.request(\n           ~~~~~~~~~~~~^\n        \"POST\",\n        ^^^^^^^\n    ...<11 lines>...\n        extensions=extensions,\n        ^^^^^^^^^^^^^^^^^^^^^^\n    )\n    ^\n  File \"/usr/local/lib/python3.13/site-packages/starlette/testclient.py\", line 484, in request\n    return super().request(\n           ~~~~~~~~~~~~~~~^\n        method,\n        ^^^^^^^\n    ...<11 lines>...\n        extensions=extensions,\n        ^^^^^^^^^^^^^^^^^^^^^^\n    )\n    ^\n  File \"/usr/local/lib/python3.13/site-packages/httpx/_client.py\", line 825, in request\n    return self.send(request, auth=auth, follow_redirects=follow_redirects)\n           ~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.13/site-packages/httpx/_client.py\", line 914, in send\n    response = self._send_handling_auth(\n        request,\n    ...<2 lines>...\n        history=[],\n    )\n  File \"/usr/local/lib/python3.13/site-packages/httpx/_client.py\", line 942, in _send_handling_auth\n    response = self._send_handling_redirects(\n        request,\n        follow_redirects=follow_redirects,\n        history=history,\n    )\n  File \"/usr/local/lib/python3.13/site-packages/httpx/_client.py\", line 979, in _send_handling_redirects\n    response = self._send_single_request(request)\n  File \"/usr/local/lib/python3.13/site-packages/httpx/_client.py\", line 1014, in _send_single_request\n    response = transport.handle_request(request)\n  File \"/usr/local/lib/python3.13/site-packages/starlette/testclient.py\", line 377, in handle_request\n    raise exc\n  File \"/usr/local/lib/python3.13/site-packages/starlette/testclient.py\", line 374, in handle_request\n    portal.call(self.app, scope, receive, send)\n    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.13/site-packages/anyio/from_thread.py\", line 334, in call\
… (3801 more chars)