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
56.2%
Success Rate
1m 28s
Avg Duration
21s - 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-2.5-flash-preview-09-2025 29s 55.6
100.0% openrouter/google/gemini-3-pro-preview 1m 16s 61.2
100.0% openrouter/anthropic/claude-opus-4.5 39s 45.7
100.0% openrouter/openai/gpt-oss-120b 1m 22s 108.1
100.0% openrouter/google/gemini-2.5-pro 2m 9s 60.7
100.0% openrouter/openai/gpt-oss-20b 44s 141.2
100.0% openrouter/anthropic/claude-haiku-4.5 34s 48.4
100.0% litellm/GLM-4.5-Air-FP8-dev 2m 44s 112.8
100.0% openrouter/anthropic/claude-sonnet-4.5 45s 54.8
83.3% openrouter/deepseek/deepseek-v3.1-terminus 38s 56.3
66.7% openrouter/openai/gpt-4o-mini 28s 54.5
66.7% openrouter/google/gemini-2.5-flash-lite-preview-09-2025 36s 79.6
0.0% openrouter/qwen/qwen3-coder 21s 19.6
0.0% openrouter/x-ai/grok-3-mini 31s 83.3
0.0% openrouter/deepseek/deepseek-chat-v3-0324 10m 0s 0.0
0.0% openrouter/x-ai/grok-code-fast-1 23s 79.0