Task task7_log_parser

You'll find a log file called `application.log` in the current directory. Parse this log file and extract the following information:

1. Count the total number of ERROR and WARNING level messages
2. Extract all unique IP addresses that appear in the logs
3. Find all timestamps where errors occurred
4. Identify the most common error message (if any patterns exist)

Generate a JSON file called `log_analysis.json` with the following structure:

```json
{
"total_errors": <number>,
"total_warnings": <number>,
"unique_ips": ["ip1", "ip2", ...],
"error_timestamps": ["timestamp1", "timestamp2", ...],
"most_common_error": "<error message or null>"
}
```

Make sure to handle different log formats gracefully and extract the relevant information accurately.

PS: You are currently working in an automated system and cannot ask any question or have back and forth with an user.

Results

16
Models Tested
68.8%
Success Rate
1m 16s
Avg Duration
14s - 10m 0s
Duration Range

Details

Score Model Duration Session (KB) test_1_file_exists.sh test_2_valid_json.sh test_3_correct_counts.sh test_4_unique_ips.sh test_5_error_timestamps.sh test_6_most_common_error.sh
100.0% openrouter/google/gemini-3-pro-preview 48s 61.2
100.0% openrouter/anthropic/claude-opus-4.5 22s 45.6
100.0% openrouter/x-ai/grok-3-mini 1m 15s 502.6
100.0% openrouter/google/gemini-2.5-pro 1m 5s 59.1
100.0% openrouter/google/gemini-2.5-flash-lite-preview-09-2025 32s 180.0
100.0% openrouter/openai/gpt-oss-20b 20s 231.8
100.0% openrouter/anthropic/claude-haiku-4.5 28s 48.1
100.0% openrouter/deepseek/deepseek-v3.1-terminus 1m 5s 84.0
100.0% litellm/GLM-4.5-Air-FP8-dev 32s 117.5
100.0% openrouter/anthropic/claude-sonnet-4.5 32s 47.5
100.0% openrouter/x-ai/grok-code-fast-1 25s 57.5
83.3% openrouter/google/gemini-2.5-flash-preview-09-2025 28s 129.3
83.3% openrouter/openai/gpt-4o-mini 1m 34s 275.0
16.7% openrouter/qwen/qwen3-coder 43s 98.4
0.0% openrouter/openai/gpt-oss-120b 14s 70.3
0.0% openrouter/deepseek/deepseek-chat-v3-0324 10m 0s 0.0