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
2m 4s
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 12s 59.6
100.0% openrouter/anthropic/claude-opus-4.5 27s 44.9
100.0% openrouter/openai/gpt-oss-120b 2m 51s 362.3
100.0% openrouter/x-ai/grok-3-mini 35s 219.8
100.0% openrouter/google/gemini-2.5-pro 59s 52.9
100.0% openrouter/anthropic/claude-haiku-4.5 25s 55.1
100.0% openrouter/deepseek/deepseek-v3.1-terminus 53s 220.5
100.0% litellm/GLM-4.5-Air-FP8-dev 40s 141.2
100.0% openrouter/anthropic/claude-sonnet-4.5 45s 58.0
100.0% openrouter/x-ai/grok-code-fast-1 2m 14s 242.3
66.7% openrouter/qwen/qwen3-coder 45s 83.1
16.7% openrouter/openai/gpt-4o-mini 27s 56.1
16.7% openrouter/openai/gpt-oss-20b 27s 144.6
0.0% openrouter/google/gemini-2.5-flash-preview-09-2025 10m 0s 0.0
0.0% openrouter/google/gemini-2.5-flash-lite-preview-09-2025 26s 56.5
0.0% openrouter/deepseek/deepseek-chat-v3-0324 10m 0s 0.0