california-housing-api

grader verdict: FAIL (exit 0)
sertionError: ...
FAILED ../tests/test_outputs.py::test_api_predict_batch_endpoint - AssertionE...
FAILED ../tests/test_outputs.py::test_api_predict_batch_matches_single_predict
FAILED ../tests/test_outputs.py::test_api_predict_batch_order_preserved - ass...
FAILED ../tests/test_outputs.py::test_validation_vs_test_metrics - FileNotFou...
FAILED ../tests/test_outputs.py::test_predictions_reproducible_from_saved_model
================== 12 failed, 18 passed, 11 warnings in 5.26s ==================

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 build a complete machine learning regression pipeline for California housing price prediction. Let me start by exploring the environment and understanding the data structure.

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 06:13 ..\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     \nArchive:  /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|3.5542,43.0,6.268421052631579,1.1342105263157896,874.0,2.3,36.78,-119.8,0.965\n     7|6.6227,20.0,6.282147315855181,1.0087390761548065,2695.0,3.3645443196004994,37.42,-121.86,2.648\n     8|2.5192,28.0,4.345360824742268,1.074742268041237,1355.0,3.4922680412371134,34.04,-117.97,1.573\n     9|7.9892,37.0,6.052757793764988,0.9544364508393285,999.0,2.3956834532374103,37.91,-122.53,5.00001\n    10|1.5,5.0,3.620578778135048,1.0160771704180065,819.0,2.6334405144694535,34.13,-117.9,1.398\n    11|6.4266,5.0,6.730284479654303,1.0306085703997119,9427.0,3.394670507742168,34.02,-117.79,3.156\n    12|4.2167,52.0,5.742690058479532,1.152046783625731,653.0,3.818713450292398,37.72,-122.45,2.319\n    13|2.2303,25.0,3.2417391304347825,1.0713043478260869,1710.0,2.973913043478261,33.97,-118.35,1.594\n    14|2.875,13.0,6.490259740259741,1.2175324675324675,750.0,2.435064935064935,37.46,-119.72,0.96\n    15|2.0972,36.0,3.0126582278481013,1.0901898734177216,3561.0,5.6344936708860756,37.33,-121.88,1.875\n    16|2.8125,46.0,4.722033898305085,1.0203389830508474,714.0,2.4203389830508475,34.18,-118.34,2.299\n    17|3.1667,36.0,5.054474708171206,1.0,1872.0,3.642023346303502,33.89,-118.23,1.177\n    18|2.8924,52.0,5.355353075170843,1.0455580865603644,1169.0,2.662870159453303,36.67,-121.65,1.696\n    19|4.4063,20.0,5.900560224089636,1.0518207282913166,2071.0,2.9005602240896358,38.03,-121.34,1.308\n    20|4.233,34.0,4.9943609022556394,0.9680451127819549,1948.0,3.661654135338346,34.32,-118.43,1.574\n    21|", "total_lines": 16513, "file_size": 1516656, "truncated": true, "hint": "Use offset=21 to continue reading (showing 1-20 of 16513 lines)", "is_binary": false, "is_image": false}

{"content": "     1|MedInc,HouseAge,AveRooms,AveBedrms,Population,AveOccup,Latitude,Longitude\n     2|1.6812,25.0,4.192200557103064,1.0222841225626742,1392.0,3.8774373259052926,36.06,-119.01\n     3|2.5313,30.0,5.039383561643835,1.1934931506849316,1565.0,2.6797945205479454,35.14,-119.46\n     4|3.4801,52.0,3.977154724818276,1.185877466251298,1310.0,1.3603322949117342,37.8,-122.44\n     5|5.7376,17.0,6.163636363636364,1.02020202020202,1705.0,3.4444444444444446,34.28,-118.72\n     6|3.725,34.0,5.492990654205608,1.02803738317757,1063.0,2.4836448598130842,36.62,-121.93\n     7|4.7147,12.0,5.251482799525504,0.9750889679715302,2400.0,2.8469750889679717,34.08,-117.61\n     8|5.0839,36.0,6.221719457013575,1.0950226244343892,670.0,3.0316742081447963,33.89,-118.02\n     9|3.6908,38.0,4.962825278810409,1.0483271375464684,1011.0,3.758364312267658,33.92,-118.08\n    10|4.8036,4.0,3.9246575342465753,1.0359589041095891,1050.0,1.797945205479452,37.39,-122.08\n    11|8.1132,45.0,6.87905604719764,1.0117994100294985,943.0,2.781710914454277,34.18,-118.23\n    12|2.5417,30.0,5.086021505376344,1.1720430107526882,242.0,2.6021505376344085,38.01,-120.37\n    13|3.0,35.0,3.6141479099678455,0.9887459807073955,1762.0,2.832797427652733,34.08,-118.12\n    14|2.8594,20.0,4.151458137347131,1.118532455315146,4818.0,4.532455315145814,33.76,-117.91\n    15|3.3409,18.0,4.646983311938382,1.0937098844672657,3171.0,4.070603337612323,34.2,-119.19\n    16|", "total_lines": 4129, "file_size": 354101, "truncated": true, "hint": "Use offset=16 to continue reading (showing 1-15 of 4129 lines)", "is_binary": false, "is_image": false}

Now I understand the data structure. Let me create a comprehensive Python script to accomplish all the steps:

write_file({"path": "/app/train_and_deploy.py", "content": "#!/usr/bin/env python3\n\"\"\"\nCalifornia Housing Price Prediction Pipeline\nTrains multiple regression models, selects the best one, and deploys as FastAPI service\n\"\"\"\n\nimport os\nimport json\nimport pickle\nimport pandas as pd\nimport numpy as np\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LinearR…)

{"bytes_written": 11624, "dirs_created": true}

Now let me run the script to train the models and start the API:

terminal({"command": "cd /app && python train_and_deploy.py 2>&1", "timeout": "300.0"})

{"output": "============================================================\nStep 1: Loading and Preparing Data\n============================================================\nLoaded training data: 16512 samples\nColumns: ['MedInc', 'HouseAge', 'AveRooms', 'AveBedrms', 'Population', 'AveOccup', 'Latitude', 'Longitude', 'target']\nTraining set: 13209 samples\nValidation set: 3303 samples\nSaved split information to /app/output/split_info.json\n\n============================================================\nStep 2: Training Multiple Regression Models\n============================================================\n\nTraining linear_regression...\n  Train R²: 0.6131\n  Validation R²: 0.6097\n  MAE: 0.5407\n  MSE: 0.5386\n  RMSE: 0.7339\n\nTraining ridge...\n  Train R²: 0.6131\n  Validation R²: 0.6097\n  MAE: 0.5407\n  MSE: 0.5386\n  RMSE: 0.7339\n\nTraining random_forest...\n  Train R²: 0.9732\n  Validation R²: 0.7985\n  MAE: 0.3459\n  MSE: 0.2781\n  RMSE: 0.5273\n\n============================================================\nStep 3: Selecting Best Model and Saving\n============================================================\n\nBest model: random_forest\nBest validation R²: 0.7985\nSaved linear_regression to /app/models/linear_regression.pkl\nSaved ridge to /app/models/ridge.pkl\nSaved random_forest to /app/models/random_forest.pkl\nSaved best model to /app/models/best_model.pkl\nSaved model comparison to /app/output/model_comparison.json\n\n============================================================\nStep 4: Building FastAPI Application\n============================================================\nCreated FastAPI application at /app/api/app.py\n\n============================================================\nStep 5: Starting FastAPI Server\n============================================================\nError connecting to server: 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\n============================================================\nStep 6: Testing API with Validation Set\n============================================================\nTesting API with 3303 validation samples\nError predicting sample 0: HTTPConnectionPool(host='localhost', port=8000): Max retries exceeded with url: /predict (Caused by NewConnectionError(\"HTTPConnection(host='localhost', port=8000): Failed to establish a new connection: [Errno 111] Connection refused\"))\nError predicting sample 1: HTTPConnectionPool(host='localhost', port=8000): Max retries exceeded with url: /predict (Caused by NewConnectionError(\"HTTPConnection(host='localhost', port=8000): Failed to establish a new connection: [Errno 111] Connection refused\"))\nError predicting sample 2: HTTPConnectionPool(host='localhost', port=8000): Max retries exceeded with url: /predict (Caused by NewConnectionError(\"HTTPConnection(host='localhost', port=8000): Failed to establish a new connection: [Errno 111] Connection refused\"))\nError predicting sample 3: HTTPConnectionPool(host='localhost', port=8000): Max retries exceeded with url: /predict (Caused by NewConnectionError(\"HTTPConnection(host='localhost', port=8000): Failed to establish a new connection: [Errno 111] Connection refused\"))\nError predicting sample 4: HTTPConnectionPool(host='localhost', port=8000): Max retries exceeded with url: /predict (Caused by NewConnectionError(\"HTTPConnection(host='localhost', port=8000): Failed to establish a new connection: [Errno 111] Connection refused\"))\nError predicting sample 5: HTTPConnectionPool(host='localhost', port=8000): Max retries exceeded with url: /predict (Caused by NewConnectionError(\"HTTPConnection(host='localhost', port=8000): Failed to establish a new connection: [Errno 111] Connection refused\"))\nError predicting sample 6: HTTPConnectionPool(host='localhost', port=8000): Max retries exceed
… (46726 more chars)

The script ran but the server didn't start properly. Let me start the server separately and then test it:

terminal({"command": "cd /app && uvicorn api.app:app --host 0.0.0.0 --port 8000 > /tmp/uvicorn.log 2>&1 &\nsleep 3\ncat /tmp/uvicorn.log | head -20", "timeout": "30.0"})

{"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"}

{"processes": []}

{"output": "Background process started", "session_id": "proc_c78a875890c7", "pid": null, "exit_code": 0, "error": null}

{"session_id": "proc_c78a875890c7", "command": "cd /app && uvicorn api.app:app --host 0.0.0.0 --port 8000 > /tmp/uvicorn.log 2>&1 &", "status": "exited", "pid": null, "uptime_seconds": 16, "output_preview": "", "exit_code": -1}

{"content": "", "total_lines": 0, "file_size": 0, "truncated": false, "is_binary": false, "is_image": false, "error": "File not found: /tmp/uvicorn.log"}

Let me try a different approach - start the server directly:

terminal({"command": "cd /app && nohup uvicorn api.app:app --host 0.0.0.0 --port 8000 > /tmp/uvicorn.log 2>&1 & echo \"Started with PID: $!\"", "timeout": "10.0"})

{"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_745a37384d32", "pid": null, "exit_code": 0, "error": null, "notify_on_complete": true}

{"session_id": "proc_745a37384d32", "command": "cd /app && uvicorn api.app:app --host 0.0.0.0 --port 8000 --timeout-keep-alive 120", "status": "exited", "pid": null, "uptime_seconds": 16, "output_preview": "", "exit_code": -1}

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

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

{"content": "     1|#!/usr/bin/env python3\n     2|\"\"\"\n     3|FastAPI Application for California Housing Price Prediction\n     4|\"\"\"\n     5|\n     6|from fastapi import FastAPI\n     7|from fastapi.responses import JSONResponse\n     8|import pickle\n     9|import numpy as np\n    10|import os\n    11|\n    12|# Load the best model at startup\n    13|model_path = \"/app/models/best_model.pkl\"\n    14|with open(model_path, 'rb') as f:\n    15|    best_model = pickle.load(f)\n    16|\n    17|# Get model type\n    18|model_type = type(best_model).__name__\n    19|\n    20|app = FastAPI(title=\"California Housing Price Prediction API\")\n    21|\n    22|@app.on_event(\"startup\")\n    23|async def startup_event():\n    24|    \"\"\"Initialize model on startup\"\"\"\n    25|    pass\n    26|\n    27|@app.get(\"/health\")\n    28|async def health():\n    29|    \"\"\"Health check endpoint\"\"\"\n    30|    return {\n    31|        \"status\": \"healthy\",\n    32|        \"model_loaded\": True\n    33|    }\n    34|\n    35|@app.get(\"/model-info\")\n    36|async def model_info():\n    37|    \"\"\"Get model information\"\"\"\n    38|    return {\n    39|        \"model_name\": \"california_housing_predictor\",\n    40|        \"model_type\": model_type,\n    41|        \"features_count\": 8\n    42|    }\n    43|\n    44|@app.post(\"/predict\")\n    45|async def predict(features: list):\n    46|    \"\"\"Predict house price for a single sample\"\"\"\n    47|    # Validate input\n    48|    if len(features) != 8:\n    49|        return {\"error\": \"Expected 8 features\"}\n    50|    \n    51|    # Make prediction\n    52|    features_array = np.array(features).reshape(1, -1)\n    53|    prediction = best_model.predict(features_array)[0]\n    54|    \n    55|    return {\n    56|        \"prediction\": float(prediction),\n    57|        \"model_name\": \"california_housing_predictor\"\n    58|    }\n    59|\n    60|@app.post(\"/predict-batch\")\n    61|async def predict_batch(features_list: list):\n    62|    \"\"\"Predict house prices for multiple samples\"\"\"\n    63|    # Validate input\n    64|    for i, features in enumerate(features_list):\n    65|        if len(features) != 8:\n    66|            return {\"error\": f\"Sample {i}: Expected 8 features, got {len(features)}\"}\n    67|    \n    68|    # Make predictions\n    69|    features_array = np.array(features_list)\n    70|    predictions = best_model.predict(features_array)\n    71|    \n    72|    return {\n    73|        \"predictions\": [float(p) for p in predictions],\n    74|        \"model_name\": \"california_housing_predictor\",\n    75|        \"count\": len(predictions)\n    76|    }\n    77|", "total_lines": 76, "file_size": 2002, "truncated": false, "is_binary": false, "is_image": false}

The app looks correct. Let me check if there's an issue with the import or model loading:

terminal({"command": "cd /app && python -c \"import pickle; import numpy as np; model = pickle.load(open('/app/models/best_model.pkl', 'rb')); print(type(model)); print(model)\"", "timeout": "10.0"})

{"output": "<class 'sklearn.ensemble._forest.RandomForestRegressor'>\nRandomForestRegressor(random_state=42)", "exit_code": 0, "error": null}

The model loads correctly. Let me try starting the server with a different approach:

terminal({"command": "cd /app && python -c \"import uvicorn; uvicorn.run('api.app:app', host='0.0.0.0', port=8000)\" 2>&1 &", "background": true})

{"output": "Background process started", "session_id": "proc_1cf6f7c1c267", "pid": null, "exit_code": 0, "error": null}

{"session_id": "proc_1cf6f7c1c267", "command": "cd /app && python -c \"import uvicorn; uvicorn.run('api.app:app', host='0.0.0.0', port=8000)\" 2>&1 &", "status": "exited", "pid": null, "uptime_seconds": 17, "output_preview": "", "exit_code": -1}

Let me check if there's an issue with the port or if the process is exiting:

terminal({"command": "cd /app && python -c \"\nimport uvicorn\nimport logging\nlogging.basicConfig(level=logging.DEBUG)\nuvicorn.run('api.app:app', host='0.0.0.0', port=8000, log_level='debug')\n\" > /tmp/uvicorn_debug.log 2>&1 &", "background": true})

{"output": "Background process started", "session_id": "proc_0fac80e1a4e4", "pid": null, "exit_code": 0, "error": null}

{"session_id": "proc_0fac80e1a4e4", "command": "cd /app && python -c \"\nimport uvicorn\nimport logging\nlogging.basicConfig(level=logging.DEBUG)\nuvicorn.run('api.app:app', host='0.0.0.0', port=8000, log_level='debug')\n\" > /tmp/uvicorn_debug.log 2>&1 &", "status": "exited", "pid": null, "uptime_seconds": 19, "output_preview": "", "exit_code": -1}

{"session_id": "proc_0fac80e1a4e4", "status": "exited", "output": "", "total_lines": 0, "showing": "0 lines"}

{"content": "", "total_lines": 0, "file_size": 0, "truncated": false, "is_binary": false, "is_image": false, "error": "File not found: /tmp/uvicorn_debug.log"}

Let me try a different approach - run uvicorn in a subprocess with explicit error capture:

terminal({"command": "cd /app && python -c \"import uvicorn; uvicorn.run('api.app:app', host='0.0.0.0', port=8000, workers=1)\" 2>&1 &", "background": true})

{"output": "Background process started", "session_id": "proc_9accd89c7cae", "pid": null, "exit_code": 0, "error": null}

{"session_id": "proc_9accd89c7cae", "command": "cd /app && python -c \"import uvicorn; uvicorn.run('api.app:app', host='0.0.0.0', port=8000, workers=1)\" 2>&1 &", "status": "exited", "pid": null, "uptime_seconds": 17, "output_preview": "", "exit_code": -1}

Let me check if the issue is with the port already being in use or something else:

terminal({"command": "netstat -tlnp 2>/dev/null | grep 8000 || ss -tlnp | grep 8000 || echo \"Port 8000 not in use\""})

{"output": "bash: line 3: ss: command not found\nPort 8000 not in use", "exit_code": 0, "error": null}

Let me try running the server in a different way:

terminal({"command": "cd /app && timeout 5 uvicorn api.app:app --host 0.0.0.0 --port 8000 2>&1 || true", "timeout": "10.0"})

{"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"}

{"processes": [{"session_id": "proc_c78a875890c7", "command": "cd /app && uvicorn api.app:app --host 0.0.0.0 --port 8000 > /tmp/uvicorn.log 2>&1 &", "cwd": "/app", "pid": null, "started_at": "2026-04-23T01:27:23", "uptime_seconds": 346, "status": "exited", "output_preview": "", "exit_code": -1}, {"session_id": "proc_745a37384d32", "command": "cd /app && uvicorn api.app:app --host 0.0.0.0 --port 8000 --timeout-keep-alive 120", "cwd": "/app", "pid": null, "started_at": "2026-04-23T01:28:40", "uptime_seconds": 270, "status": "exited", "output_preview": "", "exit_code": -1}, {"session_id": "proc_1cf6f7c1c267", "command": "cd /app && python -c \"import uvicorn; uvicorn.run('api.app:app', host='0.0.0.0', port=8000)\" 2>&1 &", "cwd": "/app", "pid": null, "started_at": "2026-04-23T01:30:18", "uptime_seconds": 171, "status": "exited", "output_preview": "", "exit_code": -1}, {"session_id": "proc_0fac80e1a4e4", "command": "cd /app && python -c \"\nimport uvicorn\nimport logging\nlogging.basicConfig(level=logging.DEBUG)\nuvicorn.run('api.app:app', host='0.0.0.0', port=8000, log_level='debug')\n\" > /tmp/uvicorn_debug.log 2>&1 &", "cwd": "/app", "pid": null, "started_at": "2026-04-23T01:30:53", "uptime_seconds": 136, "status": "exited", "output_preview": "", "exit_code": -1}, {"session_id": "proc_9accd89c7cae", "command": "cd /app && python -c \"import uvicorn; uvicorn.run('api.app:app', host='0.0.0.0', port=8000, workers=1)\" 2>&1 &", "cwd": "/app", "pid": null, "started_at": "2026-04-23T01:31:57", "uptime_seconds": 73, "status": "exited", "output_preview": "", "exit_code": -1}]}

Let me try a different approach - use the uvicorn command directly with proper arguments:

terminal({"command": "cd /app && uvicorn api.app:app --host 0.0.0.0 --port 8000 --timeout-keep-alive 120", "background": true})

{"output": "Background process started", "session_id": "proc_ffbdebee5202", "pid": null, "exit_code": 0, "error": null}

{"session_id": "proc_ffbdebee5202", "command": "cd /app && uvicorn api.app:app --host 0.0.0.0 --port 8000 --timeout-keep-alive 120", "status": "exited", "pid": null, "uptime_seconds": 18, "output_preview": "", "exit_code": -1}

Let me check the exit code and try to understand what's happening:

terminal({"command": "cd /app && python -c \"\nimport subprocess\nimport time\n\n# Start the server\nproc = subprocess.Popen(\n    ['uvicorn', 'api.app:app', '--host', '0.0.0.0', '--port', '8000'],\n    stdout=subprocess.PIPE,\n    stderr=subprocess.STDOUT\n)\ntime.sleep(3)\n\n# Check if it's running\nimport requests\ntry:\n    resp = requests.get('http://localhost:8000/health', timeout=5)\n    print(f'Ser…)

{"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"}

I see - the server process is being killed. Let me try a different approach - use a subprocess that I can monitor:

process({"action": "close", "session_id": "proc_c78a875890c7"})

{"status": "already_exited", "error": "Process has already finished"}

Let me try a cleaner approach - start the server in a way that we can monitor it:

terminal({"command": "cd /app && nohup uvicorn api.app:app --host 0.0.0.0 --port 8000 --workers 1 > /tmp/uvicorn.log 2>&1; sleep 2; cat /tmp/uvicorn.log", "timeout": "15.0"})

{"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"}

Let me try a different approach - start the server and then use a separate process to check it:

process({"action": "list"})

{"processes": [{"session_id": "proc_c78a875890c7", "command": "cd /app && uvicorn api.app:app --host 0.0.0.0 --port 8000 > /tmp/uvicorn.log 2>&1 &", "cwd": "/app", "pid": null, "started_at": "2026-04-23T01:27:23", "uptime_seconds": 466, "status": "exited", "output_preview": "", "exit_code": -1}, {"session_id": "proc_745a37384d32", "command": "cd /app && uvicorn api.app:app --host 0.0.0.0 --port 8000 --timeout-keep-alive 120", "cwd": "/app", "pid": null, "started_at": "2026-04-23T01:28:40", "uptime_seconds": 389, "status": "exited", "output_preview": "", "exit_code": -1}, {"session_id": "proc_1cf6f7c1c267", "command": "cd /app && python -c \"import uvicorn; uvicorn.run('api.app:app', host='0.0.0.0', port=8000)\" 2>&1 &", "cwd": "/app", "pid": null, "started_at": "2026-04-23T01:30:18", "uptime_seconds": 291, "status": "exited", "output_preview": "", "exit_code": -1}, {"session_id": "proc_0fac80e1a4e4", "command": "cd /app && python -c \"\nimport uvicorn\nimport logging\nlogging.basicConfig(level=logging.DEBUG)\nuvicorn.run('api.app:app', host='0.0.0.0', port=8000, log_level='debug')\n\" > /tmp/uvicorn_debug.log 2>&1 &", "cwd": "/app", "pid": null, "started_at": "2026-04-23T01:30:53", "uptime_seconds": 255, "status": "exited", "output_preview": "", "exit_code": -1}, {"session_id": "proc_9accd89c7cae", "command": "cd /app && python -c \"import uvicorn; uvicorn.run('api.app:app', host='0.0.0.0', port=8000, workers=1)\" 2>&1 &", "cwd": "/app", "pid": null, "started_at": "2026-04-23T01:31:57", "uptime_seconds": 192, "status": "exited", "output_preview": "", "exit_code": -1}, {"session_id": "proc_ffbdebee5202", "command": "cd /app && uvicorn api.app:app --host 0.0.0.0 --port 8000 --timeout-keep-alive 120", "cwd": "/app", "pid": null, "started_at": "2026-04-23T01:33:26", "uptime_seconds": 103, "status": "exited", "output_preview": "", "exit_code": -1}]}

I see the issue - the processes are being killed. Let me try a different approach - start the server and then run the test in a separate command:

process({"action": "list"})

{"processes": [{"session_id": "proc_c78a875890c7", "command": "cd /app && uvicorn api.app:app --host 0.0.0.0 --port 8000 > /tmp/uvicorn.log 2>&1 &", "cwd": "/app", "pid": null, "started_at": "2026-04-23T01:27:23", "uptime_seconds": 481, "status": "exited", "output_preview": "", "exit_code": -1}, {"session_id": "proc_745a37384d32", "command": "cd /app && uvicorn api.app:app --host 0.0.0.0 --port 8000 --timeout-keep-alive 120", "cwd": "/app", "pid": null, "started_at": "2026-04-23T01:28:40", "uptime_seconds": 404, "status": "exited", "output_preview": "", "exit_code": -1}, {"session_id": "proc_1cf6f7c1c267", "command": "cd /app && python -c \"import uvicorn; uvicorn.run('api.app:app', host='0.0.0.0', port=8000)\" 2>&1 &", "cwd": "/app", "pid": null, "started_at": "2026-04-23T01:30:18", "uptime_seconds": 306, "status": "exited", "output_preview": "", "exit_code": -1}, {"session_id": "proc_0fac80e1a4e4", "command": "cd /app && python -c \"\nimport uvicorn\nimport logging\nlogging.basicConfig(level=logging.DEBUG)\nuvicorn.run('api.app:app', host='0.0.0.0', port=8000, log_level='debug')\n\" > /tmp/uvicorn_debug.log 2>&1 &", "cwd": "/app", "pid": null, "started_at": "2026-04-23T01:30:53", "uptime_seconds": 271, "status": "exited", "output_preview": "", "exit_code": -1}, {"session_id": "proc_9accd89c7cae", "command": "cd /app && python -c \"import uvicorn; uvicorn.run('api.app:app', host='0.0.0.0', port=8000, workers=1)\" 2>&1 &", "cwd": "/app", "pid": null, "started_at": "2026-04-23T01:31:57", "uptime_seconds": 207, "status": "exited", "output_preview": "", "exit_code": -1}, {"session_id": "proc_ffbdebee5202", "command": "cd /app && uvicorn api.app:app --host 0.0.0.0 --port 8000 --timeout-keep-alive 120", "cwd": "/app", "pid": null, "started_at": "2026-04-23T01:33:26", "uptime_seconds": 118, "status": "exited", "output_preview": "", "exit_code": -1}]}

Let me try a different approach - start the server with a simple Python script that will run in the background:

write_file({"path": "/app/start_server.py", "content": "#!/usr/bin/env python3\n\"\"\"Start the FastAPI server\"\"\"\nimport uvicorn\nimport sys\n\nif __name__ == \"__main__\":\n    uvicorn.run(\n        \"api.app:app\",\n        host=\"0.0.0.0\",\n        port=8000,\n        workers=1,\n        log_level=\"info\"\n    )\n"})

{"bytes_written": 241, "dirs_created": true}

{"output": "Background process started", "session_id": "proc_5b5e2769c459", "pid": null, "exit_code": 0, "error": null}

{"session_id": "proc_5b5e2769c459", "command": "cd /app && python start_server.py", "status": "exited", "pid": null, "uptime_seconds": 15, "output_preview": "", "exit_code": -1}

The process is exiting. Let me check if there's an error in the app or if the server is starting and then exiting:

terminal({"command": "cd /app && python -c \"\nimport sys\nimport time\nimport subprocess\n\n# Start the server\nproc = subprocess.Popen(\n    [sys.executable, 'start_server.py'],\n    stdout=subprocess.PIPE,\n    stderr=subprocess.STDOUT,\n    cwd='/app'\n)\n\n# Wait for server to start\ntime.sleep(3)\n\n# Check if process is still running\nif proc.poll() is not None:\n    print('Process has exited')\n   …)

{"output": "Process has exited\nExit code: 1\nError: 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\"))", "exit_code": 0, "error": null}

The process is exiting with code 1. Let me check what error is happening:

terminal({"command": "cd /app && python -c \"\nimport sys\nimport subprocess\n\n# Start the server with stderr captured\nproc = subprocess.Popen(\n    [sys.executable, 'start_server.py'],\n    stdout=subprocess.PIPE,\n    stderr=subprocess.PIPE,\n    cwd='/app'\n)\n\n# Wait a bit and then check stderr\nimport time\ntime.sleep(2)\n\n# Get any output\nstdout, stderr = proc.communicate(timeout=1)\nprint('STDO…)

{"output": "STDOUT: None\nSTDERR: Form data requires \"python-multipart\" to be installed. \nYou can install \"python-multipart\" with: \n\npip install python-multipart\n\nTraceback (most recent call last):\n  File \"/app/start_server.py\", line 7, in <module>\n    uvicorn.run(\n    ~~~~~~~~~~~^\n        \"api.app:app\",\n        ^^^^^^^^^^^^^^\n    ...<3 lines>...\n        log_level=\"info\"\n        ^^^^^^^^^^^^^^^^\n    )\n    ^\n  File \"/usr/local/lib/python3.13/site-packages/uvicorn/main.py\", line 579, in run\n    server.run()\n    ~~~~~~~~~~^^\n  File \"/usr/local/lib/python3.13/site-packages/uvicorn/server.py\", line 66, in run\n    return asyncio.run(self.serve(sockets=sockets))\n           ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.13/asyncio/runners.py\", line 194, in run\n    return runner.run(main)\n           ~~~~~~~~~~^^^^^^\n  File \"/usr/local/lib/python3.13/asyncio/runners.py\", line 118, in run\n    return self._loop.run_until_complete(task)\n           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^\n  File \"/usr/local/lib/python3.13/asyncio/base_events.py\", line 720, in run_until_complete\n    return future.result()\n           ~~~~~~~~~~~~~^^\n  File \"/usr/local/lib/python3.13/site-packages/uvicorn/server.py\", line 70, in serve\n    await self._serve(sockets)\n  File \"/usr/local/lib/python3.13/site-packages/uvicorn/server.py\", line 77, in _serve\n    config.load()\n    ~~~~~~~~~~~^^\n  File \"/usr/local/lib/python3.13/site-packages/uvicorn/config.py\", line 435, in load\n    self.loaded_app = import_from_string(self.app)\n                      ~~~~~~~~~~~~~~~~~~^^^^^^^^^^\n  File \"/usr/local/lib/python3.13/site-packages/uvicorn/importer.py\", line 19, in import_from_string\n    module = importlib.import_module(module_str)\n  File \"/usr/local/lib/python3.13/importlib/__init__.py\", line 88, in import_module\n    return _bootstrap._gcd_import(name[level:], package, level)\n           ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"<frozen importlib._bootstrap>\", line 1387, in _gcd_import\n  File \"<frozen importlib._bootstrap>\", line 1360, in _find_and_load\n  File \"<frozen importlib._bootstrap>\", line 1331, in _find_and_load_unlocked\n  File \"<frozen importlib._bootstrap>\", line 935, in _load_unlocked\n  File \"<frozen importlib._bootstrap_external>\", line 1026, in exec_module\n  File \"<frozen importlib._bootstrap>\", line 488, in _call_with_frames_removed\n  File \"/app/api/app.py\", line 44, in <module>\n    @app.post(\"/predict\")\n     ~~~~~~~~^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.13/site-packages/fastapi/routing.py\", line 994, in decorator\n    self.add_api_route(\n    ~~~~~~~~~~~~~~~~~~^\n        path,\n        ^^^^^\n    ...<23 lines>...\n        generate_unique_id_function=generate_unique_id_function,\n        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n    )\n    ^\n  File \"/usr/local/lib/python3.13/site-packages/fastapi/routing.py\", line 933, in add_api_route\n    route = route_class(\n        self.prefix + path,\n    ...<24 lines>...\n        generate_unique_id_function=current_generate_unique_id,\n    )\n  File \"/usr/local/lib/python3.13/site-packages/fastapi/routing.py\", line 554, in __init__\n    self.dependant = get_dependant(path=self.path_format, call=self.endpoint)\n                     ~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.13/site-packages/fastapi/dependencies/utils.py\", line 285, in get_dependant\n    param_details = analyze_param(\n        param_name=param_name,\n    ...<2 lines>...\n        is_path_param=is_path_param,\n    )\n  File \"/usr/local/lib/python3.13/site-packages/fastapi/dependencies/utils.py\", line 482, in analyze_param\n    ensure_multipart_is_installed()\n    ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^\n  File \"/usr/local/lib/python3.13/site-packages/fastapi/dependencies/utils.py\", line 115, in ensure_multipart_is_installed\n    raise RuntimeError(mu
… (239 more chars)

The issue is that FastAPI needs `python-multipart` for form data handling. Let me install it:

terminal({"command": "pip install python-multipart -q", "timeout": "30.0"})

{"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.\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}

Now let me start the server again:

process({"action": "list"})

{"processes": [{"session_id": "proc_c78a875890c7", "command": "cd /app && uvicorn api.app:app --host 0.0.0.0 --port 8000 > /tmp/uvicorn.log 2>&1 &", "cwd": "/app", "pid": null, "started_at": "2026-04-23T01:27:23", "uptime_seconds": 636, "status": "exited", "output_preview": "", "exit_code": -1}, {"session_id": "proc_745a37384d32", "command": "cd /app && uvicorn api.app:app --host 0.0.0.0 --port 8000 --timeout-keep-alive 120", "cwd": "/app", "pid": null, "started_at": "2026-04-23T01:28:40", "uptime_seconds": 559, "status": "exited", "output_preview": "", "exit_code": -1}, {"session_id": "proc_1cf6f7c1c267", "command": "cd /app && python -c \"import uvicorn; uvicorn.run('api.app:app', host='0.0.0.0', port=8000)\" 2>&1 &", "cwd": "/app", "pid": null, "started_at": "2026-04-23T01:30:18", "uptime_seconds": 461, "status": "exited", "output_preview": "", "exit_code": -1}, {"session_id": "proc_0fac80e1a4e4", "command": "cd /app && python -c \"\nimport uvicorn\nimport logging\nlogging.basicConfig(level=logging.DEBUG)\nuvicorn.run('api.app:app', host='0.0.0.0', port=8000, log_level='debug')\n\" > /tmp/uvicorn_debug.log 2>&1 &", "cwd": "/app", "pid": null, "started_at": "2026-04-23T01:30:53", "uptime_seconds": 426, "status": "exited", "output_preview": "", "exit_code": -1}, {"session_id": "proc_9accd89c7cae", "command": "cd /app && python -c \"import uvicorn; uvicorn.run('api.app:app', host='0.0.0.0', port=8000, workers=1)\" 2>&1 &", "cwd": "/app", "pid": null, "started_at": "2026-04-23T01:31:57", "uptime_seconds": 362, "status": "exited", "output_preview": "", "exit_code": -1}, {"session_id": "proc_ffbdebee5202", "command": "cd /app && uvicorn api.app:app --host 0.0.0.0 --port 8000 --timeout-keep-alive 120", "cwd": "/app", "pid": null, "started_at": "2026-04-23T01:33:26", "uptime_seconds": 273, "status": "exited", "output_preview": "", "exit_code": -1}, {"session_id": "proc_5b5e2769c459", "command": "cd /app && python start_server.py", "cwd": "/app", "pid": null, "started_at": "2026-04-23T01:36:03", "uptime_seconds": 116, "status": "exited", "output_preview": "", "exit_code": -1}]}

{"status": "already_exited", "error": "Process has already finished"}

Now let me start the server again:

process({"action": "list"})

{"processes": [{"session_id": "proc_c78a875890c7", "command": "cd /app && uvicorn api.app:app --host 0.0.0.0 --port 8000 > /tmp/uvicorn.log 2>&1 &", "cwd": "/app", "pid": null, "started_at": "2026-04-23T01:27:23", "uptime_seconds": 671, "status": "exited", "output_preview": "", "exit_code": -1}, {"session_id": "proc_745a37384d32", "command": "cd /app && uvicorn api.app:app --host 0.0.0.0 --port 8000 --timeout-keep-alive 120", "cwd": "/app", "pid": null, "started_at": "2026-04-23T01:28:40", "uptime_seconds": 594, "status": "exited", "output_preview": "", "exit_code": -1}, {"session_id": "proc_1cf6f7c1c267", "command": "cd /app && python -c \"import uvicorn; uvicorn.run('api.app:app', host='0.0.0.0', port=8000)\" 2>&1 &", "cwd": "/app", "pid": null, "started_at": "2026-04-23T01:30:18", "uptime_seconds": 496, "status": "exited", "output_preview": "", "exit_code": -1}, {"session_id": "proc_0fac80e1a4e4", "command": "cd /app && python -c \"\nimport uvicorn\nimport logging\nlogging.basicConfig(level=logging.DEBUG)\nuvicorn.run('api.app:app', host='0.0.0.0', port=8000, log_level='debug')\n\" > /tmp/uvicorn_debug.log 2>&1 &", "cwd": "/app", "pid": null, "started_at": "2026-04-23T01:30:53", "uptime_seconds": 460, "status": "exited", "output_preview": "", "exit_code": -1}, {"session_id": "proc_9accd89c7cae", "command": "cd /app && python -c \"import uvicorn; uvicorn.run('api.app:app', host='0.0.0.0', port=8000, workers=1)\" 2>&1 &", "cwd": "/app", "pid": null, "started_at": "2026-04-23T01:31:57", "uptime_seconds": 397, "status": "exited", "output_preview": "", "exit_code": -1}, {"session_id": "proc_ffbdebee5202", "command": "cd /app && uvicorn api.app:app --host 0.0.0.0 --port 8000 --timeout-keep-alive 120", "cwd": "/app", "pid": null, "started_at": "2026-04-23T01:33:26", "uptime_seconds": 308, "status": "exited", "output_preview": "", "exit_code": -1}, {"session_id": "proc_5b5e2769c459", "command": "cd /app && python start_server.py", "cwd": "/app", "pid": null, "started_at": "2026-04-23T01:36:03", "uptime_seconds": 151, "status": "exited", "output_preview": "", "exit_code": -1}]}

{"status": "already_exited", "error": "Process has already finished"}

{"status": "already_exited", "error": "Process has already finished"}

{"status": "already_exited", "error": "Process has already finished"}

{"status": "already_exited", "error": "Process has already finished"}

{"status": "already_exited", "error": "Process has already finished"}

{"status": "already_exited", "error": "Process has already finished"}

Now let me start the server again:

process({"action": "list"})

{"processes": [{"session_id": "proc_c78a875890c7", "command": "cd /app && uvicorn api.app:app --host 0.0.0.0 --port 8000 > /tmp/uvicorn.log 2>&1 &", "cwd": "/app", "pid": null, "started_at": "2026-04-23T01:27:23", "uptime_seconds": 787, "status": "exited", "output_preview": "", "exit_code": -1}, {"session_id": "proc_745a37384d32", "command": "cd /app && uvicorn api.app:app --host 0.0.0.0 --port 8000 --timeout-keep-alive 120", "cwd": "/app", "pid": null, "started_at": "2026-04-23T01:28:40", "uptime_seconds": 710, "status": "exited", "output_preview": "", "exit_code": -1}, {"session_id": "proc_1cf6f7c1c267", "command": "cd /app && python -c \"import uvicorn; uvicorn.run('api.app:app', host='0.0.0.0', port=8000)\" 2>&1 &", "cwd": "/app", "pid": null, "started_at": "2026-04-23T01:30:18", "uptime_seconds": 612, "status": "exited", "output_preview": "", "exit_code": -1}, {"session_id": "proc_0fac80e1a4e4", "command": "cd /app && python -c \"\nimport uvicorn\nimport logging\nlogging.basicConfig(level=logging.DEBUG)\nuvicorn.run('api.app:app', host='0.0.0.0', port=8000, log_level='debug')\n\" > /tmp/uvicorn_debug.log 2>&1 &", "cwd": "/app", "pid": null, "started_at": "2026-04-23T01:30:53", "uptime_seconds": 577, "status": "exited", "output_preview": "", "exit_code": -1}, {"session_id": "proc_9accd89c7cae", "command": "cd /app && python -c \"import uvicorn; uvicorn.run('api.app:app', host='0.0.0.0', port=8000, workers=1)\" 2>&1 &", "cwd": "/app", "pid": null, "started_at": "2026-04-23T01:31:57", "uptime_seconds": 513, "status": "exited", "output_preview": "", "exit_code": -1}, {"session_id": "proc_ffbdebee5202", "command": "cd /app && uvicorn api.app:app --host 0.0.0.0 --port 8000 --timeout-keep-alive 120", "cwd": "/app", "pid": null, "started_at": "2026-04-23T01:33:26", "uptime_seconds": 424, "status": "exited", "output_preview": "", "exit_code": -1}, {"session_id": "proc_5b5e2769c459", "command": "cd /app && python start_server.py", "cwd": "/app", "pid": null, "started_at": "2026-04-23T01:36:03", "uptime_seconds": 267, "status": "exited", "output_preview": "", "exit_code": -1}]}

Let me clear the process list and start fresh:

process({"action": "list"})

{"processes": [{"session_id": "proc_c78a875890c7", "command": "cd /app && uvicorn api.app:app --host 0.0.0.0 --port 8000 > /tmp/uvicorn.log 2>&1 &", "cwd": "/app", "pid": null, "started_at": "2026-04-23T01:27:23", "uptime_seconds": 803, "status": "exited", "output_preview": "", "exit_code": -1}, {"session_id": "proc_745a37384d32", "command": "cd /app && uvicorn api.app:app --host 0.0.0.0 --port 8000 --timeout-keep-alive 120", "cwd": "/app", "pid": null, "started_at": "2026-04-23T01:28:40", "uptime_seconds": 727, "status": "exited", "output_preview": "", "exit_code": -1}, {"session_id": "proc_1cf6f7c1c267", "command": "cd /app && python -c \"import uvicorn; uvicorn.run('api.app:app', host='0.0.0.0', port=8000)\" 2>&1 &", "cwd": "/app", "pid": null, "started_at": "2026-04-23T01:30:18", "uptime_seconds": 628, "status": "exited", "output_preview": "", "exit_code": -1}, {"session_id": "proc_0fac80e1a4e4", "command": "cd /app && python -c \"\nimport uvicorn\nimport logging\nlogging.basicConfig(level=logging.DEBUG)\nuvicorn.run('api.app:app', host='0.0.0.0', port=8000, log_level='debug')\n\" > /tmp/uvicorn_debug.log 2>&1 &", "cwd": "/app", "pid": null, "started_at": "2026-04-23T01:30:53", "uptime_seconds": 593, "status": "exited", "output_preview": "", "exit_code": -1}, {"session_id": "proc_9accd89c7cae", "command": "cd /app && python -c \"import uvicorn; uvicorn.run('api.app:app', host='0.0.0.0', port=8000, workers=1)\" 2>&1 &", "cwd": "/app", "pid": null, "started_at": "2026-04-23T01:31:57", "uptime_seconds": 530, "status": "exited", "output_preview": "", "exit_code": -1}, {"session_id": "proc_ffbdebee5202", "command": "cd /app && uvicorn api.app:app --host 0.0.0.0 --port 8000 --timeout-keep-alive 120", "cwd": "/app", "pid": null, "started_at": "2026-04-23T01:33:26", "uptime_seconds": 440, "status": "exited", "output_preview": "", "exit_code": -1}, {"session_id": "proc_5b5e2769c459", "command": "cd /app && python start_server.py", "cwd": "/app", "pid": null, "started_at": "2026-04-23T01:36:03", "uptime_seconds": 283, "status": "exited", "output_preview": "", "exit_code": -1}]}

Let me try starting the server with a different approach - using a simple Python script that will run in the background:

write_file({"path": "/app/start_server.py", "content": "#!/usr/bin/env python3\n\"\"\"Start the FastAPI server\"\"\"\nimport uvicorn\nimport sys\n\nif __name__ == \"__main__\":\n    uvicorn.run(\n        \"api.app:app\",\n        host=\"0.0.0.0\",\n        port=8000,\n        workers=1,\n        log_level=\"info\"\n    )\n"})

{"bytes_written": 241, "dirs_created": true}

{"output": "Background process started", "session_id": "proc_4c33963cedbd", "pid": null, "exit_code": 0, "error": null, "notify_on_complete": true}

{"session_id": "proc_4c33963cedbd", "command": "cd /app && python start_server.py", "status": "exited", "pid": null, "uptime_seconds": 17, "output_preview": "", "exit_code": -1}

The process is still exiting. Let me check if there's an issue with the FastAPI app:

terminal({"command": "cd /app && python -c \"\nimport sys\nimport time\nimport subprocess\n\n# Start the server\nproc = subprocess.Popen(\n    [sys.executable, 'start_server.py'],\n    stdout=subprocess.PIPE,\n    stderr=subprocess.STDOUT,\n    cwd='/app'\n)\n\n# Wait for server to start\ntime.sleep(3)\n\n# Check if process is still running\nif proc.poll() is not None:\n    print('Process has exited')\n   …)

{"output": "Process is still running\nHealth check: {'status': 'healthy', 'model_loaded': True}", "exit_code": 0, "error": null}

The server is running! The process was still running. Let me now test the API endpoints:

terminal({"command": "cd /app && python -c \"\nimport requests\n\n# Test health endpoint\nresp = requests.get('http://localhost:8000/health')\nprint('Health:', resp.json())\n\n# Test model-info endpoint\nresp = requests.get('http://localhost:8000/model-info')\nprint('Model info:', resp.json())\n\n# Test predict endpoint\nresp = requests.post('http://localhost:8000/predict', json={'features': [1.0, 20.0, 5.…)

{"output": "Health: {'status': 'healthy', 'model_loaded': True}\nModel info: {'model_name': 'california_housing_predictor', 'model_type': 'RandomForestRegressor', 'features_count': 8}\nPredict: {'detail': [{'type': 'missing', 'loc': ['body', 'features'], 'msg': 'Field required', 'input': None}]}\nPredict batch: {'detail': [{'type': 'missing', 'loc': ['body', 'features_list'], 'msg': 'Field required', 'input': None}]}", "exit_code": 0, "error": null}

The predict endpoints are having issues. Let me fix the API app to use Pydantic models:

write_file({"path": "/app/api/app.py", "content": "#!/usr/bin/env python3\n\"\"\"\nFastAPI Application for California Housing Price Prediction\n\"\"\"\n\nfrom fastapi import FastAPI\nfrom pydantic import BaseModel\nimport pickle\nimport numpy as np\nimport os\n\n# Load the best model at startup\nmodel_path = \"/app/models/best_model.pkl\"\nwith open(model_path, 'rb') as f:\n    best_model = pickle.load(f)\n\…)

{"bytes_written": 2128, "dirs_created": true}

Now let me restart the server with the updated app:

process({"action": "list"})

{"processes": [{"session_id": "proc_c78a875890c7", "command": "cd /app && uvicorn api.app:app --host 0.0.0.0 --port 8000 > /tmp/uvicorn.log 2>&1 &", "cwd": "/app", "pid": null, "started_at": "2026-04-23T01:27:23", "uptime_seconds": 1007, "status": "exited", "output_preview": "", "exit_code": -1}, {"session_id": "proc_745a37384d32", "command": "cd /app && uvicorn api.app:app --host 0.0.0.0 --port 8000 --timeout-keep-alive 120", "cwd": "/app", "pid": null, "started_at": "2026-04-23T01:28:40", "uptime_seconds": 930, "status": "exited", "output_preview": "", "exit_code": -1}, {"session_id": "proc_1cf6f7c1c267", "command": "cd /app && python -c \"import uvicorn; uvicorn.run('api.app:app', host='0.0.0.0', port=8000)\" 2>&1 &", "cwd": "/app", "pid": null, "started_at": "2026-04-23T01:30:18", "uptime_seconds": 832, "status": "exited", "output_preview": "", "exit_code": -1}, {"session_id": "proc_0fac80e1a4e4", "command": "cd /app && python -c \"\nimport uvicorn\nimport logging\nlogging.basicConfig(level=logging.DEBUG)\nuvicorn.run('api.app:app', host='0.0.0.0', port=8000, log_level='debug')\n\" > /tmp/uvicorn_debug.log 2>&1 &", "cwd": "/app", "pid": null, "started_at": "2026-04-23T01:30:53", "uptime_seconds": 797, "status": "exited", "output_preview": "", "exit_code": -1}, {"session_id": "proc_9accd89c7cae", "command": "cd /app && python -c \"import uvicorn; uvicorn.run('api.app:app', host='0.0.0.0', port=8000, workers=1)\" 2>&1 &", "cwd": "/app", "pid": null, "started_at": "2026-04-23T01:31:57", "uptime_seconds": 733, "status": "exited", "output_preview": "", "exit_code": -1}, {"session_id": "proc_ffbdebee5202", "command": "cd /app && uvicorn api.app:app --host 0.0.0.0 --port 8000 --timeout-keep-alive 120", "cwd": "/app", "pid": null, "started_at": "2026-04-23T01:33:26", "uptime_seconds": 644, "status": "exited", "output_preview": "", "exit_code": -1}, {"session_id": "proc_5b5e2769c459", "command": "cd /app && python start_server.py", "cwd": "/app", "pid": null, "started_at": "2026-04-23T01:36:03", "uptime_seconds": 487, "status": "exited", "output_preview": "", "exit_code": -1}, {"session_id": "proc_4c33963cedbd", "command": "cd /app && python start_server.py", "cwd": "/app", "pid": null, "started_at": "2026-04-23T01:41:26", "uptime_seconds": 164, "status": "exited", "output_preview": "", "exit_code": -1}]}

Let me kill all the processes and start fresh:

process({"action": "kill", "session_id": "proc_c78a875890c7"})

{"status": "already_exited", "exit_code": -1}