california-housing-api

{"output": "total 24\ndrwxr-xr-x 1 root root 4096 Mar  4 09:21 .\ndrwxr-xr-x 1 root root 4096 Apr 22 18:11 ..\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}

I'll start by exploring the environment and then work through each step systematically.

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

{"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}

{"output": "MedInc,HouseAge,AveRooms,AveBedrms,Population,AveOccup,Latitude,Longitude,target\n3.2596,33.0,5.017656500802568,1.0064205457463884,2300.0,3.691813804173355,32.71,-117.03,1.03\n3.8125,49.0,4.473544973544974,1.041005291005291,1314.0,1.7380952380952381,33.77,-118.16,3.821\n4.1563,4.0,5.645833333333333,0.9851190476190477,915.0,2.7232142857142856,34.66,-120.48,1.726\n1.9425,36.0,4.002816901408451,1.0338028169014084,1418.0,3.9943661971830986,32.69,-117.11,0.934", "exit_code": 0, "error": null}

{"output": "16513 /app/data/train.csv\n   4129 /app/data/test.csv\n  20642 total", "exit_code": 0, "error": null}

{"bytes_written": 13609, "dirs_created": true}

Good! Now I have 16512 training samples (plus header) and 4128 test samples. Let me create a comprehensive Python script to complete all the steps.

→ write_file({"path": "/app/workflow.py", "content": "#!/usr/bin/env python3\n\"\"\"\nCalifornia Housing Price Prediction - Complete Workflow\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 LinearRegression, Ridge\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.met…)

{"output": "[Command timed out after 300s]", "exit_code": 124, "error": null}

{"output": "/app/models/:\ntotal 226596\ndrwxr-xr-x 1 root root      4096 Apr 22 18:24 .\ndrwxr-xr-x 1 root root      4096 Apr 22 18:23 ..\n-rw-r--r-- 1 root root 116002615 Apr 22 18:24 best_model.pkl\n-rw-r--r-- 1 root root       709 Apr 22 18:24 linear_regression.pkl\n-rw-r--r-- 1 root root 116002615 Apr 22 18:24 random_forest.pkl\n-rw-r--r-- 1 root root       681 Apr 22 18:24 ridge.pkl\n\n/app/output/:\ntotal 184\ndrwxr-xr-x 1 root root   4096 Apr 22 18:24 .\ndrwxr-xr-x 1 root root   4096 Apr 22 18:23 ..\n-rw-r--r-- 1 root root    720 Apr 22 18:24 model_comparison.json\n-rw-r--r-- 1 root root 170671 Apr 22 18:23 split_info.json", "exit_code": 0, "error": null}

The script timed out. Let me check what happened and run it in smaller steps:

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

{"output": "total 20\ndrwxr-xr-x 1 root root 4096 Apr 22 18:24 .\ndrwxr-xr-x 1 root root 4096 Apr 22 18:23 ..\ndrwxr-xr-x 2 root root 4096 Apr 22 18:24 __pycache__\n-rw-r--r-- 1 root root 2265 Apr 22 18:24 app.py", "exit_code": 0, "error": null}

Good, the models and split info are saved. Let me check if the API app was created and run a simpler version:

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

{"output": "{\n  \"models\": [\n    {\n      \"model_name\": \"linear_regression\",\n      \"train_r2\": 0.6131488911003316,\n      \"val_r2\": 0.6097023914123513,\n      \"mae\": 0.5407140746076833,\n      \"mse\": 0.5386059660291211,\n      \"rmse\": 0.733897789906143\n    },\n    {\n      \"model_name\": \"ridge\",\n      \"train_r2\": 0.6131487849169935,\n      \"val_r2\": 0.6096997448135448,\n      \"mae\": 0.5407313629383259,\n      \"mse\": 0.5386096183033741,\n      \"rmse\": 0.733900278173659\n    },\n    {\n      \"model_name\": \"random_forest\",\n      \"train_r2\": 0.9732129585612862,\n      \"val_r2\": 0.7984855340683008,\n      \"mae\": 0.3458606364819863,\n      \"mse\": 0.278087518867314,\n      \"rmse\": 0.5273400410241138\n    }\n  ],\n  \"best_model_name\": \"random_forest\"\n}", "exit_code": 0, "error": null}

{"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"}

The models are trained. The best model is Random Forest with validation R² of 0.798 (well above 0.6) and MAE of 0.346 (well below 0.8). Now let me start the server and test it:

→ terminal({"command": "cd /app && uvicorn api.app:app --host 0.0.0.0 --port 8000 &", "timeout": "10.0"})

{"output": "Background process started", "session_id": "proc_1c9415a0a7b2", "pid": null, "exit_code": 0, "error": null}

{"session_id": "proc_1c9415a0a7b2", "command": "cd /app && uvicorn api.app:app --host 0.0.0.0 --port 8000", "status": "exited", "pid": null, "uptime_seconds": 12, "output_preview": "", "exit_code": -1}

{"session_id": "proc_1c9415a0a7b2", "status": "exited", "output": "", "total_lines": 0, "showing": "0 lines"}

Let me check what happened:

→ process({"action": "log", "session_id": "proc_1c9415a0a7b2"})

{"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 running the server in a different way:

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

{"processes": [{"session_id": "proc_1c9415a0a7b2", "command": "cd /app && uvicorn api.app:app --host 0.0.0.0 --port 8000", "cwd": "/app", "pid": null, "started_at": "2026-04-22T13:29:41", "uptime_seconds": 32, "status": "exited", "output_preview": "", "exit_code": -1}]}

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

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

→ process({"action": "close", "session_id": "proc_1c9415a0a7b2"})

{"output": "Background process started", "session_id": "proc_fab6b8f35fa2", "pid": null, "exit_code": 0, "error": null}

{"session_id": "proc_fab6b8f35fa2", "command": "cd /app && python -c \"import uvicorn; uvicorn.run('api.app:app', host='0.0.0.0', port=8000, log_level='info')\" &", "status": "exited", "pid": null, "uptime_seconds": 6, "output_preview": "", "exit_code": -1}

{"content": "     1|\n     2|from fastapi import FastAPI\n     3|from fastapi.responses import JSONResponse\n     4|import pickle\n     5|import numpy as np\n     6|import os\n     7|\n     8|# Create FastAPI app\n     9|app = FastAPI(title=\"California Housing Price Prediction\")\n    10|\n    11|# Global variable to store the loaded model\n    12|loaded_model = None\n    13|model_name = None\n    14|\n    15|# Load model on startup\n    16|@app.on_event(\"startup\")\n    17|async def load_model():\n    18|    global loaded_model, model_name\n    19|    model_path = \"/app/models/best_model.pkl\"\n    20|    with open(model_path, \"rb\") as f:\n    21|        loaded_model = pickle.load(f)\n    22|    \n    23|    # Get model name based on type\n    24|    model_type = type(loaded_model).__name__\n    25|    if model_type == \"LinearRegression\":\n    26|        model_name = \"linear_regression\"\n    27|    elif model_type == \"Ridge\":\n    28|        model_name = \"ridge\"\n    29|    elif model_type == \"RandomForestRegressor\":\n    30|        model_name = \"random_forest\"\n    31|    else:\n    32|        model_name = model_type.lower()\n    33|    \n    34|    print(f\"Model loaded: {model_name}\")\n    35|\n    36|# Helper function for prediction\n    37|def predict_single(features):\n    38|    features_array = np.array(features).reshape(1, -1)\n    39|    prediction = loaded_model.predict(features_array)[0]\n    40|    return float(prediction)\n    41|\n    42|def predict_batch(features_list):\n    43|    features_array = np.array(features_list)\n    44|    predictions = loaded_model.predict(features_array)\n    45|    return [float(p) for p in predictions]\n    46|\n    47|# Endpoint 1: POST /predict\n    48|@app.post(\"/predict\")\n    49|async def predict(input_data: dict):\n    50|    features = input_data[\"features\"]\n    51|    prediction = predict_single(features)\n    52|    return {\n    53|        \"prediction\": prediction,\n    54|        \"model_name\": model_name\n    55|    }\n    56|\n    57|# Endpoint 2: GET /health\n    58|@app.get(\"/health\")\n    59|async def health():\n    60|    return {\n    61|        \"status\": \"healthy\",\n    62|        \"model_loaded\": loaded_model is not None\n    63|    }\n    64|\n    65|# Endpoint 3: GET /model-info\n    66|@app.get(\"/model-info\")\n    67|async def model_info():\n    68|    return {\n    69|        \"model_name\": model_name,\n    70|        \"model_type\": type(loaded_model).__name__,\n    71|        \"features_count\": 8\n    72|    }\n    73|\n    74|# Endpoint 4: POST /predict-batch\n    75|@app.post(\"/predict-batch\")\n    76|async def predict_batch_endpoint(input_data: dict):\n    77|    features_list = input_data[\"features_list\"]\n    78|    predictions = predict_batch(features_list)\n    79|    return {\n    80|        \"predictions\": predictions,\n    81|        \"model_name\": model_name,\n    82|        \"count\": len(predictions)\n    83|    }\n    84|", "total_lines": 83, "file_size": 2265, "truncated": false, "is_binary": false, "is_image": false}

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

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

{"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"}

The API looks fine. Let me try running it directly with Python to see if there's an error:

→ terminal({"command": "cd /app && timeout 5 python -c \"import uvicorn; uvicorn.run('api.app:app', host='0.0.0.0', port=8000, log_level='info')\" 2>&1 | head -30", "timeout": "15.0"})

{"processes": [{"session_id": "proc_1c9415a0a7b2", "command": "cd /app && uvicorn api.app:app --host 0.0.0.0 --port 8000", "cwd": "/app", "pid": null, "started_at": "2026-04-22T13:29:41", "uptime_seconds": 75, "status": "exited", "output_preview": "", "exit_code": -1}, {"session_id": "proc_fab6b8f35fa2", "command": "cd /app && python -c \"import uvicorn; uvicorn.run('api.app:app', host='0.0.0.0', port=8000, log_level='info')\" &", "cwd": "/app", "pid": null, "started_at": "2026-04-22T13:30:28", "uptime_seconds": 28, "status": "exited", "output_preview": "", "exit_code": -1}]}

{"bytes_written": 92, "dirs_created": true}

Let me try a different approach - use a script file and run it:

→ write_file({"path": "/app/start_server.sh", "content": "#!/bin/bash\ncd /app\nuvicorn api.app:app --host 0.0.0.0 --port 8000 --timeout-keep-alive 120\n"})

{"output": "Background process started", "session_id": "proc_56b0703bb3d7", "pid": null, "exit_code": 0, "error": null}

{"session_id": "proc_56b0703bb3d7", "command": "chmod +x /app/start_server.sh && /app/start_server.sh", "status": "exited", "pid": null, "uptime_seconds": 7, "output_preview": "", "exit_code": -1}

{"session_id": "proc_56b0703bb3d7", "status": "exited", "output": "", "total_lines": 0, "showing": "0 lines"}

Let me check if there's an issue with the environment or if I need to wait longer:

→ process({"action": "log", "session_id": "proc_56b0703bb3d7"})

{"processes": [{"session_id": "proc_1c9415a0a7b2", "command": "cd /app && uvicorn api.app:app --host 0.0.0.0 --port 8000", "cwd": "/app", "pid": null, "started_at": "2026-04-22T13:29:41", "uptime_seconds": 113, "status": "exited", "output_preview": "", "exit_code": -1}, {"session_id": "proc_fab6b8f35fa2", "command": "cd /app && python -c \"import uvicorn; uvicorn.run('api.app:app', host='0.0.0.0', port=8000, log_level='info')\" &", "cwd": "/app", "pid": null, "started_at": "2026-04-22T13:30:28", "uptime_seconds": 66, "status": "exited", "output_preview": "", "exit_code": -1}, {"session_id": "proc_56b0703bb3d7", "command": "chmod +x /app/start_server.sh && /app/start_server.sh", "cwd": "/app", "pid": null, "started_at": "2026-04-22T13:31:13", "uptime_seconds": 21, "status": "exited", "output_preview": "", "exit_code": -1}]}

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

→ process({"action": "list"})