california-housing-api

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.

# California Housing Price Prediction with FastAPI

## Goal

Build a complete machine learning regression pipeline that predicts California housing prices. Train multiple regression models, select the best performer, deploy it as a FastAPI web service, and validate predictions through API calls.

## Background

You will work with the California Housing dataset. The dataset is pre-split and provided as CSV files:
- `/app/data/train.csv`: Training data with 8 features and target column (16512 samples)
- `/app/data/test.csv`: Test data with 8 features only, no target column (4128 samples)

The 8 features are: MedInc, HouseAge, AveRooms, AveBedrms, Population, AveOccup, Latitude, Longitude
The target is median house value in $100,000s.

Available libraries: scikit-learn (1.6.0), FastAPI (0.115.6), uvicorn (0.34.0), pandas (2.2.3), numpy (2.2.1), requests (2.32.3).

## Steps

### Step 1. Load and Prepare Data

1.1. Unzip the data files:
- Unzip `/app/data/train.zip` to get `/app/data/train.csv`
- Unzip `/app/data/test.zip` to get `/app/data/test.csv`

1.2. Load training data from `/app/data/train.csv`:
- This file contains 8 feature columns and 1 target column (9 columns total)

1.3. Split the training data into train and validation sets:
- Use sklearn's train_test_split
- test_size=0.2
- random_state=42
- Reset DataFrame index before splitting to ensure consistent indexing

1.4. Save split information to `/app/output/split_info.json`:
```json
{
  "train_size": <int>,
  "validation_size": <int>,
  "random_state": 42,
  "test_size": 0.2,
  "train_indices": [<int>, ...],
  "validation_indices": [<int>, ...]
}
```
- train_indices and validation_indices are 0-based row indices from the reset DataFrame
- Indices should be sorted in ascending order

### Step 2. Train Multiple Regression Models

Train at least THREE different regression models:

**Model 1 - Linear Regression:**
- Use LinearRegression from sklearn.linear_model

**Model 2 - Ridge Regression:**
- Use Ridge from sklearn.linear_model

**Model 3 - Random Forest Regressor:**
- Use RandomForestRegressor from sklearn.ensemble

For EACH model:
- Train on the train set (X_train, y_train)
- Calculate training R² score
- Calculate validation R² score
- Calculate validation set metrics: MAE, MSE, RMSE

### Step 3. Select Best Model and Save All Models

3.1. Compare all models based on validation R² score (higher is better)

3.2. Select the model with the highest validation R² score

3.3. Save ALL trained models to `/app/models/` directory:
- `/app/models/linear_regression.pkl`
- `/app/models/ridge.pkl`
- `/app/models/random_forest.pkl`
- `/app/models/best_model.pkl` (copy of the best performing model)
- Save the trained scikit-learn model object directly (not wrapped in a dictionary or metadata object) so that pickle.load() returns an object with a callable predict() method

3.4. Save model comparison to `/app/output/model_comparison.json`:
```json
{
  "models": [
    {
      "model_name": "linear_regression",
      "train_r2": <float>,
      "val_r2": <float>,
      "mae": <float>,
      "mse": <float>,
      "rmse": <float>
    },
    ...
  ],
  "best_model_name": "<string>"
}
```

3.5. Validation quality requirements:
- A properly trained model should achieve on validation set:
  - R² > 0.6
  - MAE < 0.8

### Step 4. Build FastAPI Application

Create a FastAPI application at `/app/api/app.py` with the following requirements:

**Implementation Notes (Recommended):**
- Load the best model from `/app/models/best_model.pkl` at application startup
- Store the model in a module-level or global variable for reuse across requests
- Consider using a startup event handler (e.g., @app.on_event("startup") or lifespan context manager)

**Endpoints:**

**Endpoint 1: POST /predict**
- Input schema:
  ```json
  {
    "features": [float, float, float, float, float, float, float, float]
  }
  ```
- Use the pre-loaded model from application startup
- Output schema:
  ```json
  {
    "prediction": <flo
… (4272 more chars)

I'll help you build this complete machine learning pipeline for California housing price prediction. Let me start by exploring the environment and then proceed step by step.

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

{"output": "total 24\ndrwxr-xr-x 1 root root 4096 Mar  4 09:21 .\ndrwxr-xr-x 1 root root 4096 Apr 23 09:34 ..\ndrwxr-xr-x 2 root root 4096 Mar  4 09:21 api\ndrwxr-xr-x 2 root root 4096 Mar  4 09:21 data\ndrwxr-xr-x 2 root root 4096 Mar  4 09:21 models\ndrwxr-xr-x 2 root root 4096 Mar  4 09:21 output", "exit_code": 0, "error": null}

{"output": "total 736\ndrwxr-xr-x 2 root root   4096 Mar  4 09:21 .\ndrwxr-xr-x 1 root root   4096 Mar  4 09:21 ..\n-rw-rw-r-- 1 root root 139441 Mar  4 09:05 test.zip\n-rw-rw-r-- 1 root root 601889 Mar  4 09:05 train.zip", "exit_code": 0, "error": null}

{"output": "Archive:  /app/data/train.zip\n  inflating: /app/data/train.csv", "exit_code": 0, "error": null}

{"output": "Archive:  /app/data/test.zip\n  inflating: /app/data/test.csv", "exit_code": 0, "error": null}

{"content": "     1|MedInc,HouseAge,AveRooms,AveBedrms,Population,AveOccup,Latitude,Longitude,target\n     2|3.2596,33.0,5.017656500802568,1.0064205457463884,2300.0,3.691813804173355,32.71,-117.03,1.03\n     3|3.8125,49.0,4.473544973544974,1.041005291005291,1314.0,1.7380952380952381,33.77,-118.16,3.821\n     4|4.1563,4.0,5.645833333333333,0.9851190476190477,915.0,2.7232142857142856,34.66,-120.48,1.726\n     5|1.9425,36.0,4.002816901408451,1.0338028169014084,1418.0,3.9943661971830986,32.69,-117.11,0.934\n     6|", "total_lines": 16513, "file_size": 1516656, "truncated": true, "hint": "Use offset=6 to continue reading (showing 1-5 of 16513 lines)", "is_binary": false, "is_image": false}

Now let me create the data loading and preparation script:

write_file({"path": "/app/train_models.py", "content": "import pandas as pd\nimport numpy as np\nimport json\nimport pickle\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LinearRegression, Ridge\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error\nimport os\n\n# Set random seed for reproduc…)

{"bytes_written": 4918, "dirs_created": true}

{"output": "Loading data...\nResetting index...\nSplitting data...\nTrain size: 13209, Validation size: 3303\n\nTraining models...\nTraining Linear Regression...\nLinear Regression - Train R²: 0.6131, Val R²: 0.6097\nTraining Ridge Regression...\nRidge Regression - Train R²: 0.6131, Val R²: 0.6097\nTraining Random Forest Regressor...\nRandom Forest - Train R²: 0.9732, Val R²: 0.7985\n\nSelecting best model...\nBest model: random_forest with validation R²: 0.7985\nModels saved successfully!\n\nValidation quality check:\nBest model R²: 0.7985 (requirement: > 0.6)\nBest model MAE: 0.3459 (requirement: < 0.8)", "exit_code": 0, "error": null}

Great! The models are trained and saved. Let me verify the outputs:

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

{"content": "     1|{\n     2|  \"train_size\": 13209,\n     3|  \"validation_size\": 3303,\n     4|  \"random_state\": 42,\n     5|  \"test_size\": 0.2,\n     6|  \"train_indices\": [\n     7|    1,\n     8|    2,\n     9|    4,\n    10|    5,\n    11|    6,\n    12|    7,\n    13|    9,\n    14|    10,\n    15|    11,\n    16|    12,\n    17|    13,\n    18|    16,\n    19|    17,\n    20|    18,\n    21|    20,\n    22|    21,\n    23|    22,\n    24|    23,\n    25|    24,\n    26|    25,\n    27|    26,\n    28|    28,\n    29|    29,\n    30|    30,\n    31|    32,\n    32|    33,\n    33|    34,\n    34|    36,\n    35|    37,\n    36|    38,\n    37|    39,\n    38|    40,\n    39|    42,\n    40|    43,\n    41|    44,\n    42|    45,\n    43|    48,\n    44|    49,\n    45|    51,\n    46|    52,\n    47|    53,\n    48|    54,\n    49|    55,\n    50|    56,\n    51|    57,\n    52|    58,\n    53|    60,\n    54|    62,\n    55|    64,\n    56|    65,\n    57|    66,\n    58|    67,\n    59|    68,\n    60|    69,\n    61|    71,\n    62|    72,\n    63|    73,\n    64|    74,\n    65|    75,\n    66|    76,\n    67|    77,\n    68|    78,\n    69|    80,\n    70|    81,\n    71|    82,\n    72|    86,\n    73|    87,\n    74|    89,\n    75|    90,\n    76|    91,\n    77|    92,\n    78|    93,\n    79|    94,\n    80|    95,\n    81|    96,\n    82|    97,\n    83|    98,\n    84|    100,\n    85|    103,\n    86|    104,\n    87|    105,\n    88|    106,\n    89|    107,\n    90|    108,\n    91|    109,\n    92|    110,\n    93|    111,\n    94|    112,\n    95|    114,\n    96|    115,\n    97|    116,\n    98|    117,\n    99|    118,\n   100|    120,\n   101|    122,\n   102|    124,\n   103|    125,\n   104|    126,\n   105|    128,\n   106|    129,\n   107|    130,\n   108|    131,\n   109|    132,\n   110|    133,\n   111|    134,\n   112|    136,\n   113|    137,\n   114|    138,\n   115|    139,\n   116|    140,\n   117|    141,\n   118|    142,\n   119|    143,\n   120|    144,\n   121|    145,\n   122|    146,\n   123|    147,\n   124|    148,\n   125|    151,\n   126|    152,\n   127|    153,\n   128|    154,\n   129|    155,\n   130|    156,\n   131|    158,\n   132|    159,\n   133|    160,\n   134|    161,\n   135|    162,\n   136|    163,\n   137|    164,\n   138|    165,\n   139|    166,\n   140|    171,\n   141|    172,\n   142|    174,\n   143|    175,\n   144|    178,\n   145|    179,\n   146|    180,\n   147|    182,\n   148|    183,\n   149|    185,\n   150|    186,\n   151|    187,\n   152|    189,\n   153|    190,\n   154|    191,\n   155|    192,\n   156|    193,\n   157|    194,\n   158|    195,\n   159|    197,\n   160|    198,\n   161|    199,\n   162|    201,\n   163|    202,\n   164|    203,\n   165|    204,\n   166|    205,\n   167|    206,\n   168|    207,\n   169|    208,\n   170|    209,\n   171|    210,\n   172|    211,\n   173|    212,\n   174|    213,\n   175|    215,\n   176|    216,\n   177|    219,\n   178|    220,\n   179|    221,\n   180|    222,\n   181|    223,\n   182|    224,\n   183|    225,\n   184|    226,\n   185|    227,\n   186|    228,\n   187|    229,\n   188|    230,\n   189|    231,\n   190|    232,\n   191|    233,\n   192|    234,\n   193|    235,\n   194|    236,\n   195|    237,\n   196|    238,\n   197|    239,\n   198|    240,\n   199|    241,\n   200|    242,\n   201|    243,\n   202|    244,\n   203|    245,\n   204|    246,\n   205|    249,\n   206|    250,\n   207|    252,\n   208|    253,\n   209|    255,\n   210|    256,\n   211|    258,\n   212|    260,\n   213|    261,\n   214|    262,\n   215|    263,\n   216|    264,\n   217|    265,\n   218|    266,\n   219|    267,\n   220|    268,\n   221|    269,\n   222|    270,\n   223|    271,\n   224|    272,\n   225|    276,\n   226|    277,\n   227|    278,\n   228|    279,\n   229|    281,\n   230|    282,\n   231|    283,\n   232|    285,\n   233|    287,\n   234|    288,\n   235|    289,\n   23
… (4687 more chars)

{"content": "     1|{\n     2|  \"models\": [\n     3|    {\n     4|      \"model_name\": \"linear_regression\",\n     5|      \"train_r2\": 0.6131488911003316,\n     6|      \"val_r2\": 0.6097023914123513,\n     7|      \"mae\": 0.5407140746076833,\n     8|      \"mse\": 0.5386059660291211,\n     9|      \"rmse\": 0.733897789906143\n    10|    },\n    11|    {\n    12|      \"model_name\": \"ridge\",\n    13|      \"train_r2\": 0.6131487849169935,\n    14|      \"val_r2\": 0.6096997448135448,\n    15|      \"mae\": 0.5407313629383259,\n    16|      \"mse\": 0.5386096183033741,\n    17|      \"rmse\": 0.733900278173659\n    18|    },\n    19|    {\n    20|      \"model_name\": \"random_forest\",\n    21|      \"train_r2\": 0.9732129585612862,\n    22|      \"val_r2\": 0.7984855340683008,\n    23|      \"mae\": 0.3458606364819863,\n    24|      \"mse\": 0.278087518867314,\n    25|      \"rmse\": 0.5273400410241138\n    26|    }\n    27|  ],\n    28|  \"best_model_name\": \"random_forest\"\n    29|}", "total_lines": 28, "file_size": 720, "truncated": false, "is_binary": false, "is_image": false}

{"output": "total 226596\ndrwxr-xr-x 1 root root      4096 Apr 23 09:40 .\ndrwxr-xr-x 1 root root      4096 Apr 23 09:40 ..\n-rw-r--r-- 1 root root 116002611 Apr 23 09:40 best_model.pkl\n-rw-r--r-- 1 root root       709 Apr 23 09:40 linear_regression.pkl\n-rw-r--r-- 1 root root 116002611 Apr 23 09:40 random_forest.pkl\n-rw-r--r-- 1 root root       681 Apr 23 09:40 ridge.pkl", "exit_code": 0, "error": null}

Perfect! Now let me create the FastAPI application:

write_file({"path": "/app/api/app.py", "content": "import pickle\nimport json\nfrom fastapi import FastAPI, HTTPException\nfrom pydantic import BaseModel\nfrom typing import List\nimport os\n\napp = FastAPI()\n\n# Global variables for model\nmodel = None\nmodel_name = None\n\nclass PredictionRequest(BaseModel):\n    features: List[float]\n    \nclass BatchPredictionRequest(BaseModel):\n    features_list: Lis…)

{"bytes_written": 2660, "dirs_created": true}

Now let me start the FastAPI server and test it:

terminal({"command": "cd /app && uvicorn api.app:app --host 0.0.0.0 --port 8000 --reload"})

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

{"output": "Background process started", "session_id": "proc_8269160dd498", "pid": null, "exit_code": 0, "error": null, "notify_on_complete": true}

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

Let me check what went wrong:

process({"action": "log", "session_id": "proc_8269160dd498", "offset": 0})

{"session_id": "proc_8269160dd498", "status": "exited", "output": "", "total_lines": 0, "showing": "0 lines"}

Let me try starting the server without the reload option:

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

{"output": "Background process started", "session_id": "proc_fe40b77fb2f7", "pid": null, "exit_code": 0, "error": null, "notify_on_complete": true}

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

{"session_id": "proc_fe40b77fb2f7", "status": "exited", "output": "", "total_lines": 0, "showing": "0 lines"}

Let me check if there are any issues with the API code:

terminal({"command": "cd /app && python -c \"import api.app\""})

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

Let me try a different approach and start uvicorn directly:

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

{"output": "", "exit_code": -1, "error": "Foreground command uses shell-level background wrappers (nohup/disown/setsid). Use terminal(background=true) so Hermes can track the process, then run readiness checks and tests in separate commands.", "status": "error"}

{"output": "Background process started", "session_id": "proc_02ddeac0b249", "pid": null, "exit_code": 0, "error": null, "notify_on_complete": true}

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

Let me check the logs:

terminal({"command": "cd /app && python -c \"import sys; sys.path.insert(0, '.'); from api.app import app, load_model; load_model()\""})

{"output": "Model loaded successfully: RandomForestRegressor", "exit_code": 0, "error": null}

Good! The model loads correctly. Let me check if there's a port conflict:

terminal({"command": "netstat -tuln | grep 8000"})

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

{"output": "bash: line 3: ss: 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": 127, "error": null}

Let me try a simple test to see if uvicorn works:

terminal({"command": "cd /app && python -c \"import uvicorn; print('uvicorn imported successfully')\""})

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

{"output": "", "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"}

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

write_file({"path": "/app/start_server.py", "content": "#!/usr/bin/env python3\nimport subprocess\nimport time\nimport requests\nimport sys\n\n# Start uvicorn server\nprint(\"Starting FastAPI server...\")\nprocess = subprocess.Popen([\n    sys.executable, \"-m\", \"uvicorn\", \n    \"api.app:app\",\n    \"--host\", \"0.0.0.0\",\n    \"--port\", \"8000\"\n], stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n\n…)

{"bytes_written": 812, "dirs_created": true}

{"output": "Background process started", "session_id": "proc_8441e0ee7d25", "pid": null, "exit_code": 0, "error": null, "notify_on_complete": true}

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

{"session_id": "proc_8441e0ee7d25", "status": "exited", "output": "", "total_lines": 0, "showing": "0 lines"}

Let me try a simpler approach - let me just test if I can import and run uvicorn:

terminal({"command": "cd /app && python -c \"import uvicorn, sys; print('uvicorn version:', uvicorn.__version__)\""})

{"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"}

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

write_file({"path": "/app/test_imports.py", "content": "import sys\nsys.path.insert(0, '.')\n\nimport uvicorn\nprint(f\"uvicorn version: {uvicorn.__version__}\")\n\nfrom api.app import app\nprint(\"FastAPI app created successfully\")\n\n# Test model loading\nfrom api.app import model, model_name\nif model:\n    print(f\"Model loaded: {model_name}\")\nelse:\n    print(\"Model not loaded\")\n"})

{"bytes_written": 314, "dirs_created": true}

{"output": "uvicorn version: 0.34.0\nFastAPI app created successfully\nModel not loaded", "exit_code": 0, "error": null}

I see the issue! The model is not being loaded at startup. Let me check the startup event:

read_file({"path": "/app/api/app.py"})

{"content": "     1|import pickle\n     2|import json\n     3|from fastapi import FastAPI, HTTPException\n     4|from pydantic import BaseModel\n     5|from typing import List\n     6|import os\n     7|\n     8|app = FastAPI()\n     9|\n    10|# Global variables for model\n    11|model = None\n    12|model_name = None\n    13|\n    14|class PredictionRequest(BaseModel):\n    15|    features: List[float]\n    16|    \n    17|class BatchPredictionRequest(BaseModel):\n    18|    features_list: List[List[float]]\n    19|\n    20|@app.on_event(\"startup\")\n    21|def load_model():\n    22|    global model, model_name\n    23|    \n    24|    # Load the best model\n    25|    model_path = \"/app/models/best_model.pkl\"\n    26|    if os.path.exists(model_path):\n    27|        try:\n    28|            model = pickle.load(open(model_path, 'rb'))\n    29|            \n    30|            # Determine model type\n    31|            model_type = type(model).__name__\n    32|            if hasattr(model, 'feature_importances_'):\n    33|                model_name = f\"{model_type}\"\n    34|            else:\n    35|                model_name = f\"{model_type}\"\n    36|            \n    37|            print(f\"Model loaded successfully: {model_name}\")\n    38|        except Exception as e:\n    39|            print(f\"Error loading model: {e}\")\n    40|            model = None\n    41|            model_name = None\n    42|    else:\n    43|        print(f\"Model file not found: {model_path}\")\n    44|        model = None\n    45|        model_name = None\n    46|\n    47|@app.get(\"/health\")\n    48|def health():\n    49|    return {\n    50|        \"status\": \"healthy\",\n    51|        \"model_loaded\": model is not None\n    52|    }\n    53|\n    54|@app.get(\"/model-info\")\n    55|def model_info():\n    56|    if model is None:\n    57|        raise HTTPException(status_code=500, detail=\"Model not loaded\")\n    58|    \n    59|    return {\n    60|        \"model_name\": model_name,\n    61|        \"model_type\": type(model).__name__,\n    62|        \"features_count\": 8\n    63|    }\n    64|\n    65|@app.post(\"/predict\")\n    66|def predict(request: PredictionRequest):\n    67|    if model is None:\n    68|        raise HTTPException(status_code=500, detail=\"Model not loaded\")\n    69|    \n    70|    # Validate input\n    71|    if len(request.features) != 8:\n    72|        raise HTTPException(status_code=400, detail=\"Exactly 8 features required\")\n    73|    \n    74|    # Make prediction\n    75|    prediction = model.predict([request.features])[0]\n    76|    \n    77|    return {\n    78|        \"prediction\": float(prediction),\n    79|        \"model_name\": model_name\n    80|    }\n    81|\n    82|@app.post(\"/predict-batch\")\n    83|def predict_batch(request: BatchPredictionRequest):\n    84|    if model is None:\n    85|        raise HTTPException(status_code=500, detail=\"Model not loaded\")\n    86|    \n    87|    # Validate input\n    88|    for features in request.features_list:\n    89|        if len(features) != 8:\n    90|            raise HTTPException(status_code=400, detail=\"Each sample must have exactly 8 features\")\n    91|    \n    92|    # Make batch prediction\n    93|    predictions = model.predict(request.features_list).tolist()\n    94|    \n    95|    return {\n    96|        \"predictions\": predictions,\n    97|        \"model_name\": model_name,\n    98|        \"count\": len(predictions)\n    99|    }\n   100|", "total_lines": 99, "file_size": 2660, "truncated": false, "is_binary": false, "is_image": false}

The startup event should work. Let me try to manually load the model and test the endpoints:

write_file({"path": "/app/test_server.py", "content": "#!/usr/bin/env python3\nimport sys\nsys.path.insert(0, '.')\n\nimport pickle\nimport json\nfrom fastapi import FastAPI, HTTPException\nfrom pydantic import BaseModel\nfrom typing import List\nimport os\n\n# Load model first\nmodel_path = \"/app/models/best_model.pkl\"\nif os.path.exists(model_path):\n    model = pickle.load(open(model_path, 'rb'))\n    m…)

{"bytes_written": 1369, "dirs_created": true}

{"output": "Model loaded: random_forest\nTesting endpoints...\nTraceback (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_server.py\", line 29, 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}

{"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": "Model loaded: random_forest\nTesting endpoints...\nHealth: 200 - {'status': 'healthy', 'model_loaded': False}\nModel Info: 500 - {'detail': 'Model not loaded'}\nPredict: 500 - {'detail': 'Model not loaded'}\nBatch Predict: 500 - {'detail': 'Model not loaded'}\nAll tests passed!", "exit_code": 0, "error": null}

I see the issue! The startup event is not being triggered in the test client. Let me modify the approach to load the model directly in the app initialization:

patch({"path": "/app/api/app.py", "old_string": "app = FastAPI()\n\n# Global variables for model\nmodel = None\nmodel_name = None", "new_string": "# Global variables for model\nmodel = None\nmodel_name = None\n\n# Load model at module import time\ntry:\n    import pickle\n    import os\n    model_path = \"/app/models/best_model.pkl\"\n    if os.path.exists(model_path):\n        model = pickle.load(open(…)

{"success": true, "diff": "--- a//app/api/app.py\n+++ b//app/api/app.py\n@@ -5,11 +5,24 @@\n from typing import List\n import os\n \n-app = FastAPI()\n-\n # Global variables for model\n model = None\n model_name = None\n+\n+# Load model at module import time\n+try:\n+    import pickle\n+    import os\n+    model_path = \"/app/models/best_model.pkl\"\n+    if os.path.exists(model_path):\n+        model = pickle.load(open(model_path, 'rb'))\n+        model_type = type(model).__name__\n+        model_name = model_type\n+        print(f\"Model loaded successfully: {model_name}\")\n+except Exception as e:\n+    print(f\"Error loading model: {e}\")\n+\n+app = FastAPI()\n \n class PredictionRequest(BaseModel):\n     features: List[float]\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@@ -30,32 +30,7 @@\n class BatchPredictionRequest(BaseModel):\n     features_list: List[List[float]]\n \n-@app.on_event(\"startup\")\n-def load_model():\n-    global model, model_name\n-    \n-    # Load the best model\n-    model_path = \"/app/models/best_model.pkl\"\n-    if os.path.exists(model_path):\n-        try:\n-            model = pickle.load(open(model_path, 'rb'))\n-            \n-            # Determine model type\n-            model_type = type(model).__name__\n-            if hasattr(model, 'feature_importances_'):\n-                model_name = f\"{model_type}\"\n-            else:\n-                model_name = f\"{model_type}\"\n-            \n-            print(f\"Model loaded successfully: {model_name}\")\n-        except Exception as e:\n-            print(f\"Error loading model: {e}\")\n-            model = None\n-            model_name = None\n-    else:\n-        print(f\"Model file not found: {model_path}\")\n-        model = None\n-        model_name = None\n+\n \n @app.get(\"/health\")\n def health():\n", "files_modified": ["/app/api/app.py"], "lint": {"status": "ok", "output": ""}}

{"content": "     1|import pickle\n     2|import json\n     3|from fastapi import FastAPI, HTTPException\n     4|from pydantic import BaseModel\n     5|from typing import List\n     6|import os\n     7|\n     8|# Global variables for model\n     9|model = None\n    10|model_name = None\n    11|\n    12|# Load model at module import time\n    13|try:\n    14|    import pickle\n    15|    import os\n    16|    model_path = \"/app/models/best_model.pkl\"\n    17|    if os.path.exists(model_path):\n    18|        model = pickle.load(open(model_path, 'rb'))\n    19|        model_type = type(model).__name__\n    20|        model_name = model_type\n    21|        print(f\"Model loaded successfully: {model_name}\")\n    22|except Exception as e:\n    23|    print(f\"Error loading model: {e}\")\n    24|\n    25|app = FastAPI()\n    26|\n    27|class PredictionRequest(BaseModel):\n    28|    features: List[float]\n    29|    \n    30|class BatchPredictionRequest(BaseModel):\n    31|", "total_lines": 87, "file_size": 2233, "truncated": true, "hint": "Use offset=31 to continue reading (showing 1-30 of 87 lines)", "is_binary": false, "is_image": false}

Now let me test again:

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

{"output": "/usr/local/lib/python3.13/site-packages/sklearn/utils/validation.py:2739: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n  warnings.warn(\n/usr/local/lib/python3.13/site-packages/sklearn/utils/validation.py:2739: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n  warnings.warn(\nModel loaded: random_forest\nModel loaded successfully: RandomForestRegressor\nTesting endpoints...\nHealth: 200 - {'status': 'healthy', 'model_loaded': True}\nModel Info: 200 - {'model_name': 'RandomForestRegressor', 'model_type': 'RandomForestRegressor', 'features_count': 8}\nPredict: 200 - {'prediction': 1.3311700000000002, 'model_name': 'RandomForestRegressor'}\nBatch Predict: 200 - {'predictions': [1.3311700000000002, 1.3311700000000002], 'model_name': 'RandomForestRegressor', 'count': 2}\nAll tests passed!", "exit_code": 0, "error": null}

Excellent! Now let me start the actual server and test it:

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

{"output": "Background process started", "session_id": "proc_12fa554e9307", "pid": null, "exit_code": 0, "error": null, "notify_on_complete": true}

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

{"session_id": "proc_12fa554e9307", "status": "exited", "output": "", "total_lines": 0, "showing": "0 lines"}

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

write_file({"path": "/app/run_server.sh", "content": "#!/bin/bash\ncd /app\npython -m uvicorn api.app:app --host 0.0.0.0 --port 8000 &\necho $! > /tmp/uvicorn.pid\nsleep 5\n"})

{"bytes_written": 114, "dirs_created": true}

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

{"output": "INFO:     Started server process [1058]\nINFO:     Waiting for application startup.\nINFO:     Application startup complete.\nINFO:     Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit)", "exit_code": 0, "error": null}

Great! The server is running. Now let me test it:

terminal({"command": "sleep 3 && curl -X GET http://localhost:8000/health"})

{"output": "% Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n                                 Dload  Upload   Total   Spent    Left  Speed\n\r  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\r100    40  100    40    0     0   8598      0 --:--:-- --:--:-- --:--:-- 10000\n{\"status\":\"healthy\",\"model_loaded\":true}", "exit_code": 0, "error": null}

Perfect! Now let me test the other endpoints:

terminal({"command": "curl -X GET http://localhost:8000/model-info"})

{"output": "% Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n                                 Dload  Upload   Total   Spent    Left  Speed\n\r  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\r100    94  100    94    0     0  62128      0 --:--:-- --:--:-- --:--:-- 94000\n{\"model_name\":\"RandomForestRegressor\",\"model_type\":\"RandomForestRegressor\",\"features_count\":8}", "exit_code": 0, "error": null}

{"output": "% Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n                                 Dload  Upload   Total   Spent    Left  Speed\n\r  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\r100   180  100    70  100   110   6545  10286 --:--:-- --:--:-- --:--:-- 18000\n{\"prediction\":1.3311700000000002,\"model_name\":\"RandomForestRegressor\"}", "exit_code": 0, "error": null}

Excellent! Now let me create a script to test the API with the validation set and save the predictions:

write_file({"path": "/app/test_api.py", "content": "import pandas as pd\nimport numpy as np\nimport json\nimport requests\nfrom sklearn.model_selection import train_test_split\n\n# Load the original data\ndf = pd.read_csv('/app/data/train.csv')\ndf = df.reset_index(drop=True)\n\n# Split into features and target\nX = df.drop('target', axis=1)\ny = df['target']\n\n# Split into train and validation sets using t…)

{"bytes_written": 2368, "dirs_created": true}

{"output": "Testing API with 3303 validation samples...\nTraceback (most recent call last):\n  File \"/usr/local/lib/python3.13/site-packages/pandas/core/indexes/base.py\", line 3805, in get_loc\n    return self._engine.get_loc(casted_key)\n           ~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^\n  File \"index.pyx\", line 167, in pandas._libs.index.IndexEngine.get_loc\n  File \"index.pyx\", line 196, in pandas._libs.index.IndexEngine.get_loc\n  File \"pandas/_libs/hashtable_class_helper.pxi\", line 2606, in pandas._libs.hashtable.Int64HashTable.get_item\n  File \"pandas/_libs/hashtable_class_helper.pxi\", line 2630, in pandas._libs.hashtable.Int64HashTable.get_item\nKeyError: 1\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n  File \"/app/test_api.py\", line 48, in <module>\n    'absolute_error': [abs(y_val[i] - predictions[i]) for i in range(len(predictions))]\n                           ~~~~~^^^\n  File \"/usr/local/lib/python3.13/site-packages/pandas/core/series.py\", line 1121, in __getitem__\n    return self._get_value(key)\n           ~~~~~~~~~~~~~~~^^^^^\n  File \"/usr/local/lib/python3.13/site-packages/pandas/core/series.py\", line 1237, in _get_value\n    loc = self.index.get_loc(label)\n  File \"/usr/local/lib/python3.13/site-packages/pandas/core/indexes/base.py\", line 3812, in get_loc\n    raise KeyError(key) from err\nKeyError: 1", "exit_code": 1, "error": null}

Let me fix the indexing issue:

patch({"path": "/app/test_api.py", "old_string": "# Save predictions to CSV\nresults = pd.DataFrame({\n    'sample_index': range(len(predictions)),\n    'y_true': y_val.values,\n    'y_pred': predictions,\n    'absolute_error': [abs(y_val[i] - predictions[i]) for i in range(len(predictions))]\n})", "new_string": "# Save predictions to CSV\nabsolute_errors = [abs(y_val.iloc[i] - predictions[i]) for i in …)

{"success": true, "diff": "--- a//app/test_api.py\n+++ b//app/test_api.py\n@@ -41,11 +41,12 @@\n         predictions.append(None)\n \n # Save predictions to CSV\n+absolute_errors = [abs(y_val.iloc[i] - predictions[i]) for i in range(len(predictions))]\n results = pd.DataFrame({\n     'sample_index': range(len(predictions)),\n     'y_true': y_val.values,\n     'y_pred': predictions,\n-    'absolute_error': [abs(y_val[i] - predictions[i]) for i in range(len(predictions))]\n+    'absolute_error': absolute_errors\n })\n \n results.to_csv('/app/output/api_predictions.csv', index=False)\n", "files_modified": ["/app/test_api.py"], "lint": {"status": "ok", "output": ""}}

{"output": "Testing API with 3303 validation samples...\nPredictions saved to /app/output/api_predictions.csv\nAPI Metrics:\n  Sample Count: 3303\n  MAE: 0.3459\n  MSE: 0.2781\n  RMSE: 0.5273\n  R² Score: 0.7985\n\nQuality Check:\n  R² > 0.6: True\n  MAE < 0.8: True", "exit_code": 0, "error": null}

Perfect! Let me verify the outputs:

read_file({"path": "/app/output/api_predictions.csv", "limit": 10})

{"content": "     1|sample_index,y_true,y_pred,absolute_error\n     2|0,1.03,1.3311700000000002,0.30117000000000016\n     3|1,0.934,0.9149899999999999,0.019010000000000193\n     4|2,1.398,1.77787,0.37987000000000015\n     5|3,2.299,2.1243099999999995,0.17469000000000046\n     6|4,1.177,1.6062700000000003,0.42927000000000026\n     7|5,0.941,1.12416,0.1831600000000001\n     8|6,1.875,1.6925700000000008,0.1824299999999992\n     9|7,1.374,1.475070000000001,0.10107000000000088\n    10|8,2.76,1.891749999999999,0.8682500000000009\n    11|", "total_lines": 3304, "file_size": 155245, "truncated": true, "hint": "Use offset=11 to continue reading (showing 1-10 of 3304 lines)", "is_binary": false, "is_image": false}

{"content": "     1|{\n     2|  \"sample_count\": 3303,\n     3|  \"mae\": 0.34586063648198623,\n     4|  \"mse\": 0.278087518867314,\n     5|  \"rmse\": 0.5273400410241138,\n     6|  \"r2_score\": 0.7984855340683008\n     7|}", "total_lines": 6, "file_size": 148, "truncated": false, "is_binary": false, "is_image": false}

Excellent! Now let me test the batch prediction endpoint as well:

write_file({"path": "/app/test_batch.py", "content": "import pandas as pd\nimport json\nimport requests\nfrom sklearn.model_selection import train_test_split\n\n# Load the original data\ndf = pd.read_csv('/app/data/train.csv')\ndf = df.reset_index(drop=True)\n\n# Split into features and target\nX = df.drop('target', axis=1)\ny = df['target']\n\n# Load split info to get the validation indices\nwith open('/app…)

{"bytes_written": 1269, "dirs_created": true}