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
23s - 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 3s 53.8
100.0% openrouter/anthropic/claude-opus-4.5 28s 44.5
100.0% openrouter/openai/gpt-oss-120b 32s 193.7
100.0% openrouter/qwen/qwen3-coder 38s 137.0
100.0% openrouter/x-ai/grok-3-mini 53s 265.7
100.0% openrouter/openai/gpt-oss-20b 23s 179.8
100.0% openrouter/anthropic/claude-haiku-4.5 28s 49.7
100.0% openrouter/deepseek/deepseek-v3.1-terminus 32s 48.5
100.0% litellm/GLM-4.5-Air-FP8-dev 45s 83.0
100.0% openrouter/anthropic/claude-sonnet-4.5 45s 59.7
100.0% openrouter/x-ai/grok-code-fast-1 27s 64.1
66.7% openrouter/google/gemini-2.5-pro 34s 44.5
66.7% openrouter/openai/gpt-4o-mini 1m 36s 134.6
33.3% openrouter/google/gemini-2.5-flash-preview-09-2025 31s 86.0
16.7% openrouter/google/gemini-2.5-flash-lite-preview-09-2025 46s 188.2
0.0% openrouter/deepseek/deepseek-chat-v3-0324 10m 0s 0.0