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
62.5%
Success Rate
1m 25s
Avg Duration
25s - 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 1m 17s 59.4
100.0% openrouter/anthropic/claude-opus-4.5 27s 41.3
100.0% openrouter/openai/gpt-oss-120b 41s 111.2
100.0% openrouter/x-ai/grok-3-mini 51s 45.3
100.0% litellm/Kimi-K2.5-sandbox 29s 73.3
100.0% openrouter/anthropic/claude-haiku-4.5 29s 51.4
100.0% openrouter/deepseek/deepseek-v3.1-terminus 37s 98.8
100.0% litellm/GLM-4.5-Air-FP8-dev 2m 47s 120.3
100.0% openrouter/anthropic/claude-sonnet-4.5 33s 50.9
100.0% openrouter/x-ai/grok-code-fast-1 1m 1s 155.9
83.3% openrouter/qwen/qwen3-coder 25s 49.6
83.3% openrouter/openai/gpt-oss-20b 1m 9s 492.2
66.7% openrouter/openai/gpt-4o-mini 38s 54.4
0.0% openrouter/google/gemini-2.5-pro 54s 0.0
0.0% openrouter/google/gemini-2.5-flash-lite-preview-09-2025 28s 61.5
0.0% openrouter/deepseek/deepseek-chat-v3-0324 10m 0s 0.0