diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000000000000000000000000000000000000..86da7d48ce8d333db460e5e9e5092d47101f338d --- /dev/null +++ b/.gitattributes @@ -0,0 +1 @@ +images/**/*.png filter=lfs diff=lfs merge=lfs -text diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..26c7e4a208d922f3f2795e27341bad08a177016a --- /dev/null +++ b/.gitignore @@ -0,0 +1,89 @@ +# Python virtual environment +hf-env/ +venv/ +env/ +*.venv + +# Python cache files +__pycache__/ +*.py[cod] +*$py.class + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IDE / Editor specific files +.idea/ +.vscode/ +*.sublime-project +*.sublime-workspace + +# OS generated files +.DS_Store +Thumbs.db + +# Results (optional - uncomment if you don't want to track results in Git) +# results/ + +# Configuration files containing secrets (uncomment if you use these names) +# configs/openrouter_config.yaml +.env* +.env + +# Logs +logs/ +*.log +npm-debug.log* +yarn-debug.log* +yarn-error.log* +pnpm-debug.log* +lerna-debug.log* + +# Other +*.swp +*~ diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..d13931bc1490ab40bc98e23b021f423db6c062bd --- /dev/null +++ b/README.md @@ -0,0 +1,132 @@ +# JEE/NEET LLM Benchmark Dataset + +[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) + +## Dataset Description + +This repository contains a benchmark dataset designed for evaluating the capabilities of Large Language Models (LLMs) on questions from the Joint Entrance Examination (JEE) and the National Eligibility cum Entrance Test (NEET) conducted in India. These are highly competitive entrance examinations for engineering and medical colleges, respectively. + +The questions are presented in image format (`.png`) as they appear in the original papers. The dataset includes metadata linking each image to its corresponding exam details, subject, question type, and correct answer(s). + +**Current Data:** +* NEET 2024 (Code T3) + +## How to Use + +### Using `datasets` Library + +The dataset is designed to be loaded using the Hugging Face `datasets` library: + +```python +from datasets import load_dataset + +# Load the evaluation split +dataset = load_dataset("your-username/your-repo-name", split='test') # Replace with your HF repo name + +# Example: Access the first question +example = dataset[0] +image = example["image"] +question_id = example["question_id"] +subject = example["subject"] +correct_answers = example["correct_answer"] + +print(f"Question ID: {question_id}") +print(f"Subject: {subject}") +print(f"Correct Answer(s): {correct_answers}") +# Display the image (requires Pillow) +# image.show() +``` + +### Manual Usage (Benchmark Scripts) + +This repository contains scripts to run the benchmark evaluation directly: + +1. **Clone the repository:** + ```bash + # Replace with your actual Hugging Face repository URL + git clone https://huggingface.co/datasets/Reja1/jee-neet-benchmark + cd your-repo-name + # Ensure Git LFS is installed and pull large files if necessary + # git lfs pull + ``` +2. **Install dependencies:** + ```bash + # It's recommended to use a virtual environment + python -m venv venv + # source venv/bin/activate # or .\venv\Scripts\activate on Windows + pip install -r requirements.txt + ``` +3. **Configure API Key:** + * Create a file named `.env` in the root directory of the project (`your-repo-name/`). + * Add your OpenRouter API key to this file: + ```dotenv + OPENROUTER_API_KEY=your_actual_openrouter_api_key + ``` + * **Important:** The `.gitignore` file is already configured to prevent committing the `.env` file. Never commit your API keys directly. +4. **Configure Models:** + * Edit the `configs/benchmark_config.yaml` file. + * Modify the `openrouter_models` list to include the specific model identifiers (e.g., `"openai/gpt-4o"`, `"google/gemini-pro-vision"`) you want to evaluate. Ensure these models support vision input on OpenRouter. + * You can also adjust other parameters like `max_tokens` and `request_timeout` if needed. +5. **Run the benchmark:** + * Execute the runner script from the root directory: + ```bash + python src/benchmark_runner.py --config configs/benchmark_config.yaml + ``` + * You can override the models list from the command line: + ```bash + python src/benchmark_runner.py --config configs/benchmark_config.yaml --models "openai/gpt-4o" "google/gemini-pro-vision" + ``` + * You can specify a different output directory: + ```bash + python src/benchmark_runner.py --config configs/benchmark_config.yaml --output_dir my_custom_results + ``` +6. **Check Results:** + * Results for each model will be saved in subdirectories within the `results/` folder (or your custom output directory). + * Each model's folder (e.g., `results/openai_gpt-4o/`) will contain: + * `predictions.jsonl`: Detailed results for each question (prediction, ground truth, raw response). + * `summary.json`: Overall accuracy and statistics for that model. + +## Dataset Structure + +* **`data/metadata.jsonl`**: Contains metadata for each question image. Each line is a JSON object with fields like `image_path`, `question_id`, `exam_name`, `exam_year`, `exam_code`, `subject`, `question_type`, `correct_answer`. +* **`images/`**: Contains subdirectories for each exam set (e.g., `images/NEET_2024_T3/`), holding the `.png` question images. +* **`src/`**: Python source code for running the benchmark (data loading, LLM interaction, evaluation). +* **`configs/`**: Configuration files for the benchmark. +* **`results/`**: Directory where benchmark results (LLM outputs) will be stored. +* **`jee_neet_benchmark_dataset.py`**: Hugging Face `datasets` loading script. +* **`requirements.txt`**: Python dependencies. +* **`README.md`**: This file. + +## Data Fields + +The dataset contains the following fields (accessible via `datasets`): + +* `image`: The question image (`datasets.Image`). +* `question_id`: Unique identifier for the question (string). +* `exam_name`: Name of the exam (e.g., "NEET", "JEE Main") (string). +* `exam_year`: Year of the exam (int). +* `exam_code`: Specific paper code/session (e.g., "T3", "S1") (string). +* `subject`: Subject (e.g., "Physics", "Chemistry", "Biology", "Mathematics") (string). +* `question_type`: Type of question (e.g., "MCQ", "Multiple Correct") (string). +* `correct_answer`: List containing the correct answer index/indices (e.g., `[2]`, `[1, 3]`) (list of int). + +## Limitations + +[TODO: Describe any known limitations, e.g., OCR quality if applicable, coverage of exams/subjects, potential biases.] + +## Citation + +If you use this dataset or benchmark code, please cite: + +```bibtex +@misc{your_name_or_org_year_jeeneetbenchmark, + title={JEE/NEET LLM Benchmark}, + author={Md Rejaullah}, + year={2025}, + howpublished={\\url{https://huggingface.co/datasets/Reja1/jee-neet-benchmark}}, +} +``` + +## License + +This dataset and associated code are licensed under the [MIT License](https://opensource.org/licenses/MIT). diff --git a/configs/benchmark_config.yaml b/configs/benchmark_config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3b0a47ecea6fc333b776a0ba50a92b2fdb7da5a2 --- /dev/null +++ b/configs/benchmark_config.yaml @@ -0,0 +1,23 @@ +# List of OpenRouter model identifiers to evaluate. +# Find identifiers at: https://openrouter.ai/docs#models +# Ensure the models support vision input. +openrouter_models: + - "google/gemini-2.5-pro-preview-03-25" + #- "openai/gpt-4o" + # - "google/gemini-pro-vision" # Example - uncomment or add others + # - "anthropic/claude-3-opus" # Example - check vision support and access + # - "anthropic/claude-3-sonnet" + # - "anthropic/claude-3-haiku" + +# Path to the dataset loading script relative to the repository root. +# This should point to the .py file that defines the dataset. +dataset_path: "jee_neet_benchmark_dataset.py" + +# Base directory where results for each model will be saved. +results_base_dir: "results" + +# Optional: API request parameters +# Maximum tokens to generate in the response. Increase to allow model more room. +max_tokens: 15000 +# Timeout for each API request in seconds. +#request_timeout: 200 diff --git a/configs/openrouter_config.yaml b/configs/openrouter_config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/data/metadata.jsonl b/data/metadata.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..e00e00b89f1d44b7eb664f60d75fe0dc1dbf489b --- /dev/null +++ b/data/metadata.jsonl @@ -0,0 +1,200 @@ +{"image_path": "images/NEET_2024_T3/NEET_2024_T3_001.png", "question_id": "NEET_2024_T3_001", "exam_name": "NEET", "exam_year": 2024, "subject": "Physics", "question_type": "MCQ", "correct_answer": [1]} +{"image_path": "images/NEET_2024_T3/NEET_2024_T3_002.png", "question_id": "NEET_2024_T3_002", "exam_name": "NEET", "exam_year": 2024, "subject": "Physics", "question_type": "MCQ", "correct_answer": [3]} +{"image_path": "images/NEET_2024_T3/NEET_2024_T3_003.png", "question_id": "NEET_2024_T3_003", "exam_name": "NEET", "exam_year": 2024, "subject": "Physics", "question_type": "MCQ", "correct_answer": [3]} +{"image_path": "images/NEET_2024_T3/NEET_2024_T3_004.png", "question_id": "NEET_2024_T3_004", "exam_name": "NEET", "exam_year": 2024, "subject": "Physics", "question_type": "MCQ", "correct_answer": [2]} +{"image_path": "images/NEET_2024_T3/NEET_2024_T3_005.png", "question_id": "NEET_2024_T3_005", "exam_name": "NEET", "exam_year": 2024, "subject": "Physics", "question_type": "MCQ", "correct_answer": [4]} +{"image_path": "images/NEET_2024_T3/NEET_2024_T3_006.png", "question_id": "NEET_2024_T3_006", "exam_name": "NEET", "exam_year": 2024, "subject": "Physics", "question_type": "MCQ", "correct_answer": [1]} +{"image_path": "images/NEET_2024_T3/NEET_2024_T3_007.png", "question_id": "NEET_2024_T3_007", "exam_name": "NEET", "exam_year": 2024, "subject": "Physics", "question_type": "MCQ", "correct_answer": [4]} +{"image_path": 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a/images/NEET_2024_T3/NEET_2024_T3_200.png b/images/NEET_2024_T3/NEET_2024_T3_200.png new file mode 100644 index 0000000000000000000000000000000000000000..bc59e19613d07ea1382470721820c3f1d4c2b054 --- /dev/null +++ b/images/NEET_2024_T3/NEET_2024_T3_200.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:541c1eb6c9622e1e1ca916e9bc37ce30385116e2a1c9c368300595bbb8de181b +size 81036 diff --git a/jee_neet_benchmark_dataset.py b/jee_neet_benchmark_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..168840ac804799a3f512f2729329d4b9797342f3 --- /dev/null +++ b/jee_neet_benchmark_dataset.py @@ -0,0 +1,139 @@ +import json +import os +import datasets + +_CITATION = """\ +@misc{jee-neet-benchmark, + title={JEE/NEET LLM Benchmark}, + author={Md Rejaullah}, + year={2025}, + howpublished={\\url{https://huggingface.co/datasets/Reja1/jee-neet-benchmark}}, +} +""" + +_DESCRIPTION = """\ +A benchmark dataset for evaluating Large Language Models (LLMs) on Joint Entrance Examination (JEE) +and National Eligibility cum Entrance Test (NEET) questions from India. Questions are provided as +images, and metadata includes exam details, subject, and correct answers. +""" + +_HOMEPAGE = "https://huggingface.co/datasets/Reja1/jee-neet-benchmark" + +_LICENSE = "MIT License" + + +class JeeNeetBenchmarkConfig(datasets.BuilderConfig): + """BuilderConfig for JeeNeetBenchmark.""" + + def __init__(self, **kwargs): + """BuilderConfig for JeeNeetBenchmark. + Args: + **kwargs: keyword arguments forwarded to super. + """ + super(JeeNeetBenchmarkConfig, self).__init__(**kwargs) + + +class JeeNeetBenchmark(datasets.GeneratorBasedBuilder): + """JEE/NEET LLM Benchmark Dataset.""" + + VERSION = datasets.Version("1.0.0") # Start with version 1.0.0 + + BUILDER_CONFIGS = [ + JeeNeetBenchmarkConfig( + name="default", + version=VERSION, + description="Default config for JEE/NEET Benchmark", + ), + ] + + DEFAULT_CONFIG_NAME = "default" + + def _info(self): + features = datasets.Features( + { + "image": datasets.Image(), + "question_id": datasets.Value("string"), + "exam_name": datasets.Value("string"), + "exam_year": datasets.Value("int32"), + "exam_code": datasets.Value("string"), + "subject": datasets.Value("string"), + "question_type": datasets.Value("string"), + "correct_answer": datasets.Sequence(datasets.Value("int32")), # List of integers + } + ) + return datasets.DatasetInfo( + description=_DESCRIPTION, + features=features, + homepage=_HOMEPAGE, + license=_LICENSE, + citation=_CITATION, + ) + + def _split_generators(self, dl_manager): + """Returns SplitGenerators.""" + # dl_manager is useful if downloading/extracting files, but here we use local paths + # Determine the base directory for data files + # Use data_dir if provided (for local loading), otherwise use the script's directory + base_dir = self.config.data_dir if self.config.data_dir is not None else os.path.dirname(__file__) + metadata_path = os.path.join(base_dir, "data", "metadata.jsonl") + image_dir = os.path.join(base_dir, "images") + + # Check if metadata file exists + if not os.path.exists(metadata_path): + raise FileNotFoundError( + f"Metadata file not found at {metadata_path}. " + f"Make sure 'data/metadata.jsonl' exists in your dataset repository. " + f"If running locally, you might need to specify the path using --data_dir argument " + f"or ensure the script is run from the project root." + ) + + return [ + datasets.SplitGenerator( + name=datasets.Split.TEST, # Using TEST split as it's standard for evaluation-only data + # Or use name="evaluate" if you prefer that specific name + gen_kwargs={ + "metadata_filepath": metadata_path, + "image_base_dir": image_dir, # Pass the base image directory + }, + ), + ] + + def _generate_examples(self, metadata_filepath, image_base_dir): + """Yields examples.""" + with open(metadata_filepath, "r", encoding="utf-8") as f: + for idx, line in enumerate(f): + try: + row = json.loads(line) + except json.JSONDecodeError as e: + print(f"Error decoding JSON on line {idx+1}: {e}") + continue # Skip malformed lines + + image_path_relative = row.get("image_path") + if not image_path_relative: + print(f"Warning: Missing 'image_path' on line {idx+1}. Skipping.") + continue + + # Construct the full path relative to the dataset root + image_path_full = os.path.join(image_base_dir, os.path.relpath(image_path_relative, start="images")) + # Alternative if image_path is already relative to root: + # image_path_full = os.path.join(image_base_dir, image_path_relative) + + if not os.path.exists(image_path_full): + print(f"Warning: Image file not found at {image_path_full} referenced on line {idx+1}. Skipping.") + # Yielding with None image might cause issues later, better to skip or handle + # image_data = None + continue + # else: + # Let datasets.Image() handle the loading by passing the path + # image_data = image_path_full + + yield idx, { + "image": image_path_full, # Pass the full path to the image feature + "question_id": row.get("question_id", ""), + "exam_name": row.get("exam_name", ""), + "exam_year": row.get("exam_year", -1), # Use a default if missing + "exam_code": row.get("exam_code", ""), + "subject": row.get("subject", ""), + "question_type": row.get("question_type", ""), + "correct_answer": row.get("correct_answer", []), + } diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..c14eaec10c0812c71600a0a90f7ed82e9501ed5e --- /dev/null +++ b/requirements.txt @@ -0,0 +1,22 @@ +# Core Hugging Face library for dataset loading +datasets>=2.0.0 + +# For interacting with the Hugging Face Hub (optional but recommended) +huggingface_hub>=0.10.0 + +# Image processing library (required by datasets.Image) +Pillow>=9.0.0 + +# For making API calls (e.g., to OpenRouter) +requests>=2.25.0 + +# For handling YAML configuration files (if used in configs/) +PyYAML>=5.4.0 + +# For managing environment variables (e.g., API keys) +python-dotenv>=0.19.0 + +# For handling retries during API calls +tenacity>=8.0.0 + +# Add other dependencies as needed for your benchmark scripts (e.g., numpy, scikit-learn for evaluation) diff --git a/results/.gitkeep b/results/.gitkeep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/results/google_gemini-2.5-pro-preview-03-25/predictions.jsonl b/results/google_gemini-2.5-pro-preview-03-25/predictions.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..cddf0ab604a12a0e4d3e7d9fd817ee91b743d085 --- /dev/null +++ b/results/google_gemini-2.5-pro-preview-03-25/predictions.jsonl @@ -0,0 +1,200 @@ +{"question_id": "NEET_2024_T3_001", "ground_truth": [1], "predicted_answer": [1], "raw_response": "1", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_002", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```json\n3", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_003", "ground_truth": [3], "predicted_answer": [3], "raw_response": "3", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_004", "ground_truth": [2], "predicted_answer": [2], "raw_response": "2", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_005", "ground_truth": [4], "predicted_answer": [4], "raw_response": "4", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_006", "ground_truth": [1], "predicted_answer": [1], "raw_response": "1", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_007", "ground_truth": [4], "predicted_answer": [4], "raw_response": "``` C\n4\n```", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_008", "ground_truth": [3], "predicted_answer": [3], "raw_response": "3", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_009", "ground_truth": [1], "predicted_answer": [1], "raw_response": "1", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_010", "ground_truth": [1], "predicted_answer": [1], "raw_response": "```json\n{\"reasoning\": \"The question asks about the result of using white light instead of monochromatic light in Young's double-slit experiment. White light consists of multiple wavelengths (colors). In Young's experiment, the condition for constructive interference (bright fringes) is path difference = n\u03bb, and for destructive interference (dark fringes) is path difference = (n+1/2)\u03bb. At the center of the screen, the path difference from both slits is zero. For zero path difference, all wavelengths interfere constructively. Therefore, the central fringe (n=0) will be bright and formed by the superposition of all colors, resulting in a white fringe. Away from the center, the positions of bright fringes depend on the wavelength (y_bright = n\u03bbD/d). Since different colors have different wavelengths, their bright fringes will occur at slightly different positions for n \u2260 0. This leads to the formation of colored fringes flanking the central white fringe. For example, the first-order bright fringe (n=1) will be spread out, with violet (shortest visible wavelength) closest to the center and red (longest visible wavelength) farthest. Only the first few orders of fringes will be distinctly colored before they overlap significantly and wash out. Option (1) correctly describes this phenomenon: a central bright white fringe surrounded by a few colored fringes. Option (2) is incorrect because fringe width (\u03b2 = \u03bbD/d) depends on wavelength, so fringes for different colors (and the composite colored fringes) will not have equal widths. Option (3) is incorrect because an interference pattern is observed, albeit different from the monochromatic case. Option (4) is incorrect because the central fringe is bright, not dark (assuming 'dear' is a typo for 'dark').\", \"correct_options\": [\"1\"]}\n```\n1", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_011", "ground_truth": [2], "predicted_answer": [2], "raw_response": "```json\n2\n```", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_012", "ground_truth": [3], "predicted_answer": [3], "raw_response": "3", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_013", "ground_truth": [3], "predicted_answer": [4], "raw_response": "```\n4\n```", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_014", "ground_truth": [3], "predicted_answer": [3], "raw_response": "3", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_015", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```json\n{\n \"reasoning\": \"The user wants to find the refractive index (n) of the prism based on the given information and diagram, and then identify the corresponding option number.\\n\\n1. **Analyze the setup:** A light ray enters face AB of a right-angled prism (A=90\u00b0) with an angle of incidence i = 30\u00b0. The ray travels parallel to the base BC and emerges along face AC (angle of emergence e = 90\u00b0).\\n2. **Apply Snell's Law at face AB:** Let r1 be the angle of refraction at face AB. The refractive index of air is n_air \u2248 1.\\n n_air * sin(i) = n * sin(r1)\\n 1 * sin(30\u00b0) = n * sin(r1)\\n 1/2 = n * sin(r1) (Equation 1)\\n3. **Apply Snell's Law at face AC:** Let r2 be the angle of incidence at face AC. The ray emerges along the face, so the angle of emergence e = 90\u00b0.\\n n * sin(r2) = n_air * sin(e)\\n n * sin(r2) = 1 * sin(90\u00b0)\\n n * sin(r2) = 1 * 1\\n n * sin(r2) = 1 (Equation 2)\\n4. **Use Prism Geometry:** For a prism with angle A, the sum of the internal angles of refraction and incidence is equal to the prism angle: A = r1 + r2. Since the prism is right-angled at A, A = 90\u00b0.\\n r1 + r2 = 90\u00b0\\n Therefore, r2 = 90\u00b0 - r1.\\n5. **Substitute r2 into Equation 2:**\\n n * sin(90\u00b0 - r1) = 1\\n Using the trigonometric identity sin(90\u00b0 - \u03b8) = cos(\u03b8):\\n n * cos(r1) = 1 (Equation 3)\\n6. **Solve for n using Equations 1 and 3:** We have two equations:\\n n * sin(r1) = 1/2\\n n * cos(r1) = 1\\n Square both equations and add them:\\n (n * sin(r1))\u00b2 + (n * cos(r1))\u00b2 = (1/2)\u00b2 + 1\u00b2\\n n\u00b2 * sin\u00b2(r1) + n\u00b2 * cos\u00b2(r1) = 1/4 + 1\\n n\u00b2 * (sin\u00b2(r1) + cos\u00b2(r1)) = 5/4\\n Using the identity sin\u00b2(\u03b8) + cos\u00b2(\u03b8) = 1:\\n n\u00b2 * 1 = 5/4\\n n\u00b2 = 5/4\\n n = sqrt(5/4) (Refractive index must be positive)\\n n = sqrt(5) / sqrt(4)\\n n = sqrt(5) / 2\\n7. **Match the result with the options:**\\n (1) sqrt(3)/4\\n (2) sqrt(3)/2\\n (3) sqrt(5)/4\\n (4) sqrt(5)/2\\n The calculated refractive index n = sqrt(5) / 2 matches option (4).\\n8. **Format the output:** The correct option number is 4.\",\n \"answer\": \"4\"\n}\n```\n4", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_016", "ground_truth": [1], "predicted_answer": [1], "raw_response": "1", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_017", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```json\n3\n```", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_018", "ground_truth": [1], "predicted_answer": [1], "raw_response": "1", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_019", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```xml\n4\n```", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_020", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```json\n4", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_021", "ground_truth": [2], "predicted_answer": [2], "raw_response": "2", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_022", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```json\n3\n```", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_023", "ground_truth": [2], "predicted_answer": [2], "raw_response": "```json\n2\n```", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_024", "ground_truth": [2], "predicted_answer": [2], "raw_response": "2", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_025", "ground_truth": [1, 3], "predicted_answer": null, "raw_response": "```json\n{\"thought\": \"1. **Analyze Statement I:** \\\"Atoms are electrically neutral as they contain equal number of positive and negative charges.\\\" An atom has protons (positive) in the nucleus and electrons (negative) orbiting. In a neutral atom, the number of protons equals the number of electrons. Thus, the total positive charge cancels the total negative charge, making the atom electrically neutral. Statement I is correct.\n\n2. **Analyze Statement II:** \\\"Atoms of each element are stable and emit their characteristic spectrum.\\\" Atoms are generally stable in their ground state. However, they only emit a characteristic spectrum when their electrons are excited to higher energy levels and then fall back to lower levels, releasing energy as photons. They do not continuously emit spectra in their stable ground state. Also, not all atoms are inherently stable (e.g., radioactive isotopes). Therefore, Statement II is incorrect as a general, unconditional statement.\n\n3. **Evaluate the Options:**\n * Option (1): Statement I is correct but Statement II is incorrect. This aligns with the analysis.\n * Option (2): Statement I is incorrect but Statement II is correct. This contradicts the analysis.\n * Option (3): Both Statement I and Statement II are correct. This contradicts the analysis.\n * Option (4): Both Statement I and Statement II are incorrect. This contradicts the analysis.\n\n4. **Determine the Correct Integer:** The analysis shows that Statement I is correct and Statement II is incorrect. This corresponds to option (1).\n\n5. **Format the Output:** Enclose the integer 1 within tags.\", \"answer\": \"1\"}\n```", "parse_successful": false, "api_call_successful": true} +{"question_id": "NEET_2024_T3_026", "ground_truth": [4], "predicted_answer": [4], "raw_response": "4", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_027", "ground_truth": [2], "predicted_answer": [2], "raw_response": "2", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_028", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```python\nimport math\n\n# Define the given values\nradius_cm = 4.5 # cm\nsurface_tension = 0.07 # Nm^-1\n\n# Convert radius to meters\nradius_m = radius_cm / 100.0 # m\n\n# Calculate the circumference (length of contact)\nlength_contact = 2 * math.pi * radius_m\n\n# Calculate the excess force required\n# The force acts along the circumference\n# F = T * L\nforce = surface_tension * length_contact\n\n# Calculate the numerical value\nforce_value = 0.07 * (2 * math.pi * (4.5 / 100.0))\n# force_value = 0.07 * 2 * 3.14159 * 0.045\n# force_value = 0.14 * 3.14159 * 0.045\n# force_value = 0.43982 * 0.045\n# force_value = 0.0197919 N\n\n# Convert the force to milliNewtons (mN)\n# 1 N = 1000 mN\nforce_mN = force_value * 1000\n# force_mN = 19.7919 mN\n\n# Round the value to match the options\nforce_mN_rounded = round(force_mN, 1) # Round to one decimal place as in option (3)\n\n# Compare with options\n# (1) 1.98 mN\n# (2) 99 N\n# (3) 19.8 mN\n# (4) 198 N\n\n# The calculated value 19.7919 mN is approximately 19.8 mN.\n\ncorrect_option = 3\n```\n\n3", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_029", "ground_truth": [2], "predicted_answer": [2], "raw_response": "2", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_030", "ground_truth": [4], "predicted_answer": [4], "raw_response": "4", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_031", "ground_truth": [4], "predicted_answer": [4], "raw_response": "4", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_032", "ground_truth": [3], "predicted_answer": [3], "raw_response": "3", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_033", "ground_truth": [2], "predicted_answer": [2], "raw_response": "```2", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_034", "ground_truth": [4], "predicted_answer": [4], "raw_response": "4", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_035", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```json\n4\n```", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_036", "ground_truth": [2], "predicted_answer": [2], "raw_response": "``` C\n2\n```", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_037", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```json\n4", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_038", "ground_truth": [2], "predicted_answer": [2], "raw_response": "2", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_039", "ground_truth": [3], "predicted_answer": [4], "raw_response": "1. **Analyze the question:** The question asks to identify the correct circuit diagram that represents a Wheatstone bridge capable of achieving balance. This means we need to find the circuit(s) with the correct topology for a Wheatstone bridge measurement, where the source, resistors, and detector(s) are placed correctly. The bridge doesn't necessarily need to be balanced with the given resistance values.\n\n2. **Analyze the options:**\n * A standard Wheatstone bridge consists of four resistors (forming a diamond or square), a voltage source connected across one pair of opposite junctions, and a detector (like a galvanometer) connected across the other pair of opposite junctions. The balance condition (zero current through the detector) is achieved when the ratios of resistances in adjacent arms are equal (e.g., R1/R2 = R3/R4).\n * **Option (1):** The voltage source (E) is connected vertically. The galvanometer (G) is connected horizontally between the resistor junctions. However, the detector (D) is placed in series with the 5\u03a9 resistor (R4). This series placement is incorrect for a standard bridge detector.\n * **Option (2):** The voltage source (E) is connected horizontally. The galvanometer (G) is connected vertically between the top and bottom junctions. Similar to option (1), the detector (D) is incorrectly placed in series with the 5\u03a9 resistor (R4).\n * **Option (3):** The voltage source (E) is connected horizontally. Both the galvanometer (G) and the detector (D) are connected in parallel across the vertical junctions (top and bottom). This topology (source across one diagonal, detector(s) across the other) is a valid Wheatstone bridge configuration.\n * **Option (4):** The voltage source (E) is connected vertically. Both the galvanometer (G) and the detector (D) are connected in parallel across the horizontal junctions (left and right midpoints). This topology is also a valid Wheatstone bridge configuration and is perhaps the most common way it is drawn.\n\n3. **Determine the correct option(s):** Options (1) and (2) are incorrect because the detector D is misplaced in series with an arm resistor. Options (3) and (4) both represent topologically correct Wheatstone bridge circuits. In both, the source is connected across one pair of opposite nodes, and the detectors (G and D shown in parallel, which is redundant but topologically valid) are connected across the other pair of opposite nodes. Since the question asks for the correct circuit which *can* achieve balance, and both (3) and (4) are valid configurations, we need to consider if there's a reason to prefer one or if the intended answer might select the most conventional representation. Option (4) is the most conventional diagram. However, *both* 3 and 4 depict circuits that represent a Wheatstone bridge structure capable of balance if the resistance values were appropriate. Assuming the question expects the standard, well-formed bridge circuit diagrams, both 3 and 4 fit. If only one answer can be selected, often the most standard representation (Option 4) is intended. But without further clarification or context suggesting only one is correct, both topologies are valid. Let's check if any option *is* balanced with the given values (although the question asks which *can* achieve balance).\n * Option 3 Balance: (10/15) vs (10/5) or (10/10) vs (15/5). Neither matches.\n * Option 4 Balance: (10/15) vs (10/5) or (10/10) vs (15/5). Neither matches.\n Since neither circuit is balanced as shown, the question must refer to the correctness of the circuit topology. Both 3 and 4 have correct topology. In multiple-choice questions where more than one option seems plausible based on strict criteria, sometimes the most standard or common representation is considered the single \"best\" answer. Option 4 is generally the most common depiction. However, it is possible that the exam allows multiple correct answers or intended both 3 and 4. Given the constraint to provide integer numbers, and the possibility of ambiguity, if only one answer is accepted, option 4 is the most likely intended answer due to convention. If multiple answers were allowed, it would be 3 and 4. Assuming a single best answer based on conventional representation.\n\nLet's assume the question expects the most standard configuration.\nFinal check: Option 4 has source top-bottom, detector left-right. Resistors 10, 10, 15, 5. Topology is correct. Option 3 has source left-right, detector top-bottom. Resistors 10, 10, 15, 5. Topology is correct. Options 1 and 2 have Detector D in series with resistor - incorrect topology. So, 3 and 4 are potentially correct. Given it's a single-choice question format usually, 4 is the most standard representation.\n\nReconsidering common errors or interpretations: sometimes the labelling implies ratios. For example, in Circuit 4, ratio 10/15 compared to 10/5; or 10/10 compared to 15/5. No balance. The wording \"can achieve the bridge balance\" usually refers to the structural possibility.\n\nLet's select 4 as the most standard representation of a correct Wheatstone bridge topology.\n\n4", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_040", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```4", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_041", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```4", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_042", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```cpp\n4", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_043", "ground_truth": [4], "predicted_answer": [4], "raw_response": "4", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_044", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```json\n```json\n4\n```", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_045", "ground_truth": [4], "predicted_answer": [4], "raw_response": "``` Mmh ``` 4", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_046", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```json\n4", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_047", "ground_truth": [1], "predicted_answer": [1], "raw_response": "```json\n1", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_048", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```json\n```json\n4\n```", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_049", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```json\n3\n```", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_050", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```xml\n4\n```", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_051", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```xml\n3\n```", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_052", "ground_truth": [2], "predicted_answer": [2], "raw_response": "2", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_053", "ground_truth": [3], "predicted_answer": [3], "raw_response": "3", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_054", "ground_truth": [3], "predicted_answer": [3], "raw_response": "3", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_055", "ground_truth": [4], "predicted_answer": [4], "raw_response": "``` Phemistry\n4", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_056", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```xml\n3\n```", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_057", "ground_truth": [1], "predicted_answer": [1], "raw_response": "1", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_058", "ground_truth": [2], "predicted_answer": [2], "raw_response": "2", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_059", "ground_truth": [2], "predicted_answer": [2], "raw_response": "2", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_060", "ground_truth": [1], "predicted_answer": [1], "raw_response": "1", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_061", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```3", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_062", "ground_truth": [3], "predicted_answer": [3], "raw_response": "3", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_063", "ground_truth": [2], "predicted_answer": [2], "raw_response": "2", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_064", "ground_truth": [3], "predicted_answer": [3], "raw_response": "3", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_065", "ground_truth": [3], "predicted_answer": [3], "raw_response": "3", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_066", "ground_truth": [4], "predicted_answer": [4], "raw_response": "4", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_067", "ground_truth": [1], "predicted_answer": [1], "raw_response": "```json\n1\n```", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_068", "ground_truth": [4], "predicted_answer": [4], "raw_response": "4", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_069", "ground_truth": [1], "predicted_answer": [1], "raw_response": "```json\n1", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_070", "ground_truth": [1], "predicted_answer": [1], "raw_response": "``` C2 molecule bonding clarification:\nMolecular Orbital Theory for C2 (8 valence electrons):\n(\u03c32s)\u00b2 (\u03c3*2s)\u00b2 (\u03c02py)\u00b2 (\u03c02pz)\u00b2\nBond Order = (Number of bonding electrons - Number of antibonding electrons) / 2\nBond Order = (2 + 2 + 2 - 2) / 2 = 6 / 2 = 3 -> incorrect count, it's (2+4 - 2)/2 = 4/2 = 2.\nThe bonding consists of two \u03c0 bonds from (\u03c02py)\u00b2 and (\u03c02pz)\u00b2. The \u03c32p orbital is higher in energy than the \u03c0 orbitals for C2.\nSo, C2 has a bond order of 2, consisting solely of two \u03c0 bonds according to simple MO theory for second-period homonuclear diatomics up to N2.\n\nMatching:\nA. Ethane (C-C): Single bond = one \u03c3-bond -> III\nB. Ethene (C=C): Double bond = one \u03c3-bond and one \u03c0-bond -> IV\nC. Carbon molecule (C\u2082): Two \u03c0-bonds -> II\nD. Ethyne (C\u2261C): Triple bond = one \u03c3-bond and two \u03c0-bonds -> I\n\nMatches: A-III, B-IV, C-II, D-I\n\nChecking options:\n(1) A-III, B-IV, C-II, D-I -> This matches.\n(2) A-III, B-IV, C-I, D-II\n(3) A-I, B-IV, C-II, D-III\n(4) A-IV, B-III, C-II, D-I\n\nThe correct option is (1).\n```\n1", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_071", "ground_truth": [2], "predicted_answer": [2], "raw_response": "```json\n2", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_072", "ground_truth": [1], "predicted_answer": [1], "raw_response": "```json\n```\n1\n```", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_073", "ground_truth": [2], "predicted_answer": [2], "raw_response": "```json\n2", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_074", "ground_truth": [3], "predicted_answer": [3], "raw_response": "3", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_075", "ground_truth": [3], "predicted_answer": [3], "raw_response": "3", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_076", "ground_truth": [2], "predicted_answer": [2], "raw_response": "2", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_077", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```json\n\"3\"\n```", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_078", "ground_truth": [1], "predicted_answer": [1], "raw_response": "1", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_079", "ground_truth": [4], "predicted_answer": [4], "raw_response": "4", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_080", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```json\n3\n```", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_081", "ground_truth": [3], "predicted_answer": [3], "raw_response": "3", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_082", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```json\n4\n```", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_083", "ground_truth": [3], "predicted_answer": [3], "raw_response": "3", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_084", "ground_truth": [2], "predicted_answer": [2], "raw_response": "2", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_085", "ground_truth": [4], "predicted_answer": [4], "raw_response": "``` D:\\code-generation\\Grounded_generation\\Exam_Analysis\\extraction\\21637196\\ocr_image.png\n4", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_086", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```4```", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_087", "ground_truth": [2], "predicted_answer": [2], "raw_response": "2", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_088", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```json\n{\n \"query\": \"The plot of osmotic pressure (\u03a0) vs concentration (mol L\u207b\u00b9) for a solution gives a straight line with slope 25.73 L bar mol\u207b\u00b9. The temperature at which the osmotic pressure measurement is done is: (Use R = 0.083 L bar mol\u207b\u00b9 K\u207b\u00b9)\",\n \"options\": {\n \"1\": \"25.73\u00b0C\",\n \"2\": \"12.05\u00b0C\",\n \"3\": \"37\u00b0C\",\n \"4\": \"310\u00b0C\"\n },\n \"solution\": {\n \"step1\": \"Recall the van't Hoff equation for osmotic pressure: \u03a0 = iCRT. Assuming a non-electrolyte or that concentration refers to the total solute particle concentration, we can often use \u03a0 = CRT.\",\n \"step2\": \"Identify the variables from the plot described. The plot is \u03a0 vs C. This matches the form y = mx + b, where \u03a0 is y, C is x.\",\n \"step3\": \"Comparing \u03a0 = CRT to y = mx, we see that the slope (m) of the \u03a0 vs C plot is equal to RT.\",\n \"step4\": \"Use the given values: slope = 25.73 L bar mol\u207b\u00b9 and R = 0.083 L bar mol\u207b\u00b9 K\u207b\u00b9.\",\n \"step5\": \"Set the slope equal to RT: 25.73 L bar mol\u207b\u00b9 = (0.083 L bar mol\u207b\u00b9 K\u207b\u00b9) * T.\",\n \"step6\": \"Solve for the absolute temperature T (in Kelvin): T = (25.73 L bar mol\u207b\u00b9) / (0.083 L bar mol\u207b\u00b9 K\u207b\u00b9) = 25.73 / 0.083 K \u2248 310 K.\",\n \"step7\": \"Convert the temperature from Kelvin to Celsius: T(\u00b0C) = T(K) - 273.15. Using 273 for approximation: T(\u00b0C) = 310 K - 273 = 37\u00b0C.\",\n \"step8\": \"Compare the calculated temperature with the given options: Option (3) is 37\u00b0C.\",\n \"final_answer\": 3\n }\n}\n```\n3", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_089", "ground_truth": [4], "predicted_answer": [1, 2, 3, 4], "raw_response": "``` C-H KMnO4/H+ OH OH (1) (major product) 'P\" CH-CH (2) C-C (3) CHO (4)COOH ```", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_090", "ground_truth": [3], "predicted_answer": [3], "raw_response": "3", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_091", "ground_truth": [2], "predicted_answer": [2], "raw_response": "2", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_092", "ground_truth": [2], "predicted_answer": [2], "raw_response": "2", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_093", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```json\n3", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_094", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```json\n3", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_095", "ground_truth": [3], "predicted_answer": [3], "raw_response": "3", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_096", "ground_truth": [2], "predicted_answer": [2], "raw_response": "2", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_097", "ground_truth": [4], "predicted_answer": [4], "raw_response": "``` MSON ```\n4\n```", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_098", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```4", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_099", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```3", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_100", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```xml\n3\n```", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_101", "ground_truth": [2], "predicted_answer": [2], "raw_response": "```xml\n2\n```", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_102", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```xml\n4\n```", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_103", "ground_truth": [1], "predicted_answer": [1], "raw_response": "1", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_104", "ground_truth": [1], "predicted_answer": [2], "raw_response": "2", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_105", "ground_truth": [3], "predicted_answer": [3], "raw_response": "3", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_106", "ground_truth": [1], "predicted_answer": [1], "raw_response": "1", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_107", "ground_truth": [1], "predicted_answer": [1], "raw_response": "1", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_108", "ground_truth": [4], "predicted_answer": [4], "raw_response": "4", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_109", "ground_truth": [3], "predicted_answer": [3], "raw_response": "3", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_110", "ground_truth": [4], "predicted_answer": [4], "raw_response": "4", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_111", "ground_truth": [2], "predicted_answer": [2], "raw_response": "2", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_112", "ground_truth": [2], "predicted_answer": [2], "raw_response": "2", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_113", "ground_truth": [3], "predicted_answer": [3], "raw_response": "3", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_114", "ground_truth": [1], "predicted_answer": [1], "raw_response": "1", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_115", "ground_truth": [4], "predicted_answer": [4], "raw_response": "4", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_116", "ground_truth": [2], "predicted_answer": [4], "raw_response": "4", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_117", "ground_truth": [3], "predicted_answer": [3], "raw_response": "3", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_118", "ground_truth": [4], "predicted_answer": [4], "raw_response": "4", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_119", "ground_truth": [4], "predicted_answer": [4], "raw_response": "4", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_120", "ground_truth": [1], "predicted_answer": [1], "raw_response": "1", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_121", "ground_truth": [4], "predicted_answer": [4], "raw_response": "4", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_122", "ground_truth": [2], "predicted_answer": [1], "raw_response": "1", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_123", "ground_truth": [3], "predicted_answer": [2], "raw_response": "2", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_124", "ground_truth": [3], "predicted_answer": [3], "raw_response": "3", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_125", "ground_truth": [2], "predicted_answer": [2], "raw_response": "2", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_126", "ground_truth": [1], "predicted_answer": [1], "raw_response": "```xml\n1\n```", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_127", "ground_truth": [1], "predicted_answer": [1], "raw_response": "1", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_128", "ground_truth": [3], "predicted_answer": [3], "raw_response": "3", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_129", "ground_truth": [1], "predicted_answer": [1], "raw_response": "1", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_130", "ground_truth": [4], "predicted_answer": [4], "raw_response": "4", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_131", "ground_truth": [1], "predicted_answer": [1], "raw_response": "1", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_132", "ground_truth": [2], "predicted_answer": [2], "raw_response": "2", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_133", "ground_truth": [3], "predicted_answer": [3], "raw_response": "3", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_134", "ground_truth": [3], "predicted_answer": [3], "raw_response": "3", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_135", "ground_truth": [2], "predicted_answer": [2], "raw_response": "2", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_136", "ground_truth": [4], "predicted_answer": [4], "raw_response": "4", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_137", "ground_truth": [1], "predicted_answer": [1], "raw_response": "1", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_138", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```xml\n3\n```", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_139", "ground_truth": [4], "predicted_answer": [4], "raw_response": "4", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_140", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```xml\n4\n```", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_141", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```json\n3\n```", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_142", "ground_truth": [1], "predicted_answer": [1], "raw_response": "```xml\n1\n```", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_143", "ground_truth": [1], "predicted_answer": [1], "raw_response": "1", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_144", "ground_truth": [4], "predicted_answer": [4], "raw_response": "4", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_145", "ground_truth": [2], "predicted_answer": [2], "raw_response": "2", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_146", "ground_truth": [1], "predicted_answer": [4], "raw_response": "```xml\n4\n```", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_147", "ground_truth": [3], "predicted_answer": [3], "raw_response": "3", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_148", "ground_truth": [4], "predicted_answer": [4], "raw_response": "4", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_149", "ground_truth": [3], "predicted_answer": [3], "raw_response": "3", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_150", "ground_truth": [1], "predicted_answer": [1], "raw_response": "1", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_151", "ground_truth": [4], "predicted_answer": [4], "raw_response": "4", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_152", "ground_truth": [2], "predicted_answer": [2], "raw_response": "2", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_153", "ground_truth": [1], "predicted_answer": [1], "raw_response": "1", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_154", "ground_truth": [4], "predicted_answer": [4], "raw_response": "4", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_155", "ground_truth": [4], "predicted_answer": [4], "raw_response": "4", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_156", "ground_truth": [2], "predicted_answer": [2], "raw_response": "2", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_157", "ground_truth": [3], "predicted_answer": [3], "raw_response": "3", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_158", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```shell\n4\n```", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_159", "ground_truth": [2], "predicted_answer": [2], "raw_response": "2", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_160", "ground_truth": [4], "predicted_answer": [4], "raw_response": "4", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_161", "ground_truth": [1], "predicted_answer": [1], "raw_response": "```xml\n1\n```", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_162", "ground_truth": [1], "predicted_answer": [1], "raw_response": "1", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_163", "ground_truth": [3], "predicted_answer": [3], "raw_response": "3", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_164", "ground_truth": [2], "predicted_answer": [3], "raw_response": "``` BASH\n3\n```", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_165", "ground_truth": [2], "predicted_answer": [2], "raw_response": "```json\n2\n```", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_166", "ground_truth": [3], "predicted_answer": [3], "raw_response": "3", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_167", "ground_truth": [4], "predicted_answer": [4], "raw_response": "4", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_168", "ground_truth": [1], "predicted_answer": [1], "raw_response": "1", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_169", "ground_truth": [2], "predicted_answer": [2], "raw_response": "2", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_170", "ground_truth": [4], "predicted_answer": [3], "raw_response": "3", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_171", "ground_truth": [2], "predicted_answer": [2], "raw_response": "2", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_172", "ground_truth": [4], "predicted_answer": [4], "raw_response": "4", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_173", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```xml\n4\n```", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_174", "ground_truth": [2], "predicted_answer": [2], "raw_response": "```swift\n2\n```", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_175", "ground_truth": [2], "predicted_answer": [2], "raw_response": "2", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_176", "ground_truth": [4], "predicted_answer": [4], "raw_response": "4", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_177", "ground_truth": [2], "predicted_answer": [2], "raw_response": "```json\n2\n```", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_178", "ground_truth": [1], "predicted_answer": [1], "raw_response": "1", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_179", "ground_truth": [1], "predicted_answer": [1], "raw_response": "1", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_180", "ground_truth": [1], "predicted_answer": [1], "raw_response": "1", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_181", "ground_truth": [3], "predicted_answer": [3], "raw_response": "3", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_182", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```json\n4\n```", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_183", "ground_truth": [1], "predicted_answer": [1], "raw_response": "1", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_184", "ground_truth": [3], "predicted_answer": [3], "raw_response": "3", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_185", "ground_truth": [1], "predicted_answer": [1], "raw_response": "1", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_186", "ground_truth": [1], "predicted_answer": [1], "raw_response": "1", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_187", "ground_truth": [2], "predicted_answer": [2], "raw_response": "2", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_188", "ground_truth": [1], "predicted_answer": [1], "raw_response": "1", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_189", "ground_truth": [3], "predicted_answer": [3], "raw_response": "``` MSON\n3\n```", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_190", "ground_truth": [2], "predicted_answer": [2], "raw_response": "```json\n2\n```", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_191", "ground_truth": [2], "predicted_answer": [2], "raw_response": "```json\n2\n```", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_192", "ground_truth": [1], "predicted_answer": [1], "raw_response": "1", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_193", "ground_truth": [3], "predicted_answer": [1], "raw_response": "1", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_194", "ground_truth": [3], "predicted_answer": [3], "raw_response": "3", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_195", "ground_truth": [1], "predicted_answer": [1], "raw_response": "1", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_196", "ground_truth": [4], "predicted_answer": [4], "raw_response": "4", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_197", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```xml\n3\n```", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_198", "ground_truth": [2], "predicted_answer": [2], "raw_response": "2", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_199", "ground_truth": [3], "predicted_answer": [3], "raw_response": "3", "parse_successful": true, "api_call_successful": true} +{"question_id": "NEET_2024_T3_200", "ground_truth": [1], "predicted_answer": [3], "raw_response": "3", "parse_successful": true, "api_call_successful": true} diff --git a/results/google_gemini-2.5-pro-preview-03-25/summary.json b/results/google_gemini-2.5-pro-preview-03-25/summary.json new file mode 100644 index 0000000000000000000000000000000000000000..a7f572b19af5e1081cc8628503b97ef83506b024 --- /dev/null +++ b/results/google_gemini-2.5-pro-preview-03-25/summary.json @@ -0,0 +1,7 @@ +{ + "model_name": "google/gemini-2.5-pro-preview-03-25", + "total_questions": 200, + "successful_predictions": 199, + "api_failures": 0, + "accuracy": 0.9396984924623115 +} \ No newline at end of file diff --git a/src/__init__.py b/src/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/benchmark_runner.py b/src/benchmark_runner.py new file mode 100644 index 0000000000000000000000000000000000000000..40937eba4f99ad19a79803e5bfd74bf9398bf50c --- /dev/null +++ b/src/benchmark_runner.py @@ -0,0 +1,218 @@ +import argparse +import yaml +import os +import json +import logging +from datasets import load_dataset, Image as HFImage # Import Image feature type +from tqdm import tqdm +from PIL import Image as PILImage # Import PIL for type hinting + +# Import local modules +from utils import load_api_key +from llm_interface import get_openrouter_prediction +from evaluation import calculate_accuracy + +# Configure logging +logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') + +def load_config(config_path: str) -> dict: + """Loads the benchmark configuration from a YAML file.""" + try: + with open(config_path, 'r') as f: + config = yaml.safe_load(f) + logging.info(f"Configuration loaded from {config_path}") + return config + except FileNotFoundError: + logging.error(f"Configuration file not found at {config_path}") + raise + except yaml.YAMLError as e: + logging.error(f"Error parsing configuration file {config_path}: {e}") + raise + +def save_results(output_dir: str, model_name: str, results: list, summary: dict): + """Saves detailed predictions and summary results to files.""" + # Sanitize model name for directory/filename usage + safe_model_name = model_name.replace('/', '_') + model_output_dir = os.path.join(output_dir, safe_model_name) + os.makedirs(model_output_dir, exist_ok=True) + + # Save detailed predictions + predictions_path = os.path.join(model_output_dir, "predictions.jsonl") + try: + with open(predictions_path, 'w') as f: + for result in results: + json.dump(result, f) + f.write('\n') + logging.info(f"Detailed predictions saved to {predictions_path}") + except IOError as e: + logging.error(f"Failed to save detailed predictions to {predictions_path}: {e}") + + # Save summary + summary_path = os.path.join(model_output_dir, "summary.json") + try: + with open(summary_path, 'w') as f: + json.dump(summary, f, indent=4) + logging.info(f"Summary results saved to {summary_path}") + except IOError as e: + logging.error(f"Failed to save summary results to {summary_path}: {e}") + + +def run_benchmark(config: dict, api_key: str, models_override: list[str] | None = None, output_dir_override: str | None = None): + """Runs the benchmark evaluation loop.""" + + # Determine models to run + models_to_run = models_override if models_override else config.get("openrouter_models", []) + if not models_to_run: + logging.error("No models specified in config or via --models argument.") + return + + # Determine output directory + output_dir = output_dir_override if output_dir_override else config.get("results_base_dir", "results") + os.makedirs(output_dir, exist_ok=True) + + # Load dataset + dataset_path = config.get("dataset_path", ".") # Default to current dir if not specified + try: + # Load the dataset using the loading script from the specified path + # Ensure the 'image' column is decoded + # Explicitly specify data_files and data_dir for local loading. + # data_dir should be the project root ('.') when loading a local script, + # as the script is copied to a cache and needs to know where the actual data is. + dataset = load_dataset(dataset_path, split='test', data_files={'test': 'data/metadata.jsonl'}, data_dir='.', trust_remote_code=True) + dataset = dataset.cast_column("image", HFImage(decode=True)) # Ensure images are loaded as PIL + logging.info(f"Dataset loaded successfully from path: {dataset_path}. Number of questions: {len(dataset)}") + except Exception as e: + logging.error(f"Failed to load dataset from path '{dataset_path}': {e}") + logging.error("Ensure the path is correct and 'jee_neet_benchmark_dataset.py' exists.") + return + + total_questions = len(dataset) + + # --- Main Loop: Iterate through models --- + for model_id in models_to_run: + logging.info(f"--- Starting benchmark for model: {model_id} ---") + model_results = [] + predictions = [] + ground_truths = [] + api_failures = 0 + + # --- Inner Loop: Iterate through questions --- + for example in tqdm(dataset, desc=f"Processing {model_id}", total=total_questions): + question_id = example["question_id"] + image: PILImage.Image = example["image"] # Image should be decoded to PIL by cast_column + truth = example["correct_answer"] + ground_truths.append(truth) # Store ground truth for accuracy calculation + + try: + # Get prediction from LLM interface + parsed_answer, raw_response = get_openrouter_prediction( + image=image, + model_identifier=model_id, + api_key=api_key, + max_tokens=config.get("max_tokens", 50), # Get from config or default + request_timeout=config.get("request_timeout", 60) # Get from config or default + ) + predictions.append(parsed_answer) # Store parsed answer (list or None) + + # Provide live feedback on correctness + if parsed_answer is not None: + is_correct = sorted(parsed_answer) == sorted(truth) + logging.info(f"Question {question_id}: {'Correct' if is_correct else 'Incorrect'}") + else: + logging.info(f"Question {question_id}: Failed to get or parse answer") + + + # Store detailed result for this question + model_results.append({ + "question_id": question_id, + "ground_truth": truth, + "predicted_answer": parsed_answer, + "raw_response": raw_response, + "parse_successful": parsed_answer is not None, + "api_call_successful": True + }) + + except Exception as e: + # Catch potential failures from get_openrouter_prediction after retries + logging.error(f"API call failed permanently for question {question_id} with model {model_id}: {e}") + predictions.append(None) # Mark prediction as failed (None) + api_failures += 1 + model_results.append({ + "question_id": question_id, + "ground_truth": truth, + "predicted_answer": None, + "raw_response": None, + "parse_successful": False, + "api_call_successful": False, + "error": str(e) + }) + # Provide live feedback for failed questions + logging.info(f"Question {question_id}: API call failed") + + + # --- Evaluation for the current model --- + # Filter out None predictions before calculating accuracy + valid_predictions = [p for p in predictions if p is not None] + corresponding_ground_truths = [truth for p, truth in zip(predictions, ground_truths) if p is not None] + + if not valid_predictions: + logging.warning(f"No valid predictions were generated for model {model_id}. Skipping accuracy calculation.") + accuracy = 0.0 + else: + try: + accuracy = calculate_accuracy(valid_predictions, corresponding_ground_truths) + except ValueError as e: + logging.error(f"Error calculating accuracy for model {model_id}: {e}") + accuracy = 0.0 # Or handle as appropriate + + summary = { + "model_name": model_id, + "total_questions": total_questions, + "successful_predictions": len([p for p in predictions if p is not None]), + "api_failures": api_failures, + "accuracy": accuracy + } + + logging.info(f"--- Results for model: {model_id} ---") + logging.info(f"Accuracy: {accuracy:.4f}") + logging.info(f"API Failures: {api_failures}/{total_questions}") + logging.info("-------------------------------------") + + # Save results for the current model + save_results(output_dir, model_id, model_results, summary) + + logging.info("Benchmark run completed.") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Run JEE/NEET LLM Benchmark.") + parser.add_argument( + "--config", + type=str, + default="configs/benchmark_config.yaml", + help="Path to the benchmark configuration YAML file." + ) + parser.add_argument( + "--models", + nargs='+', # Allows specifying multiple models + type=str, + help="Override the list of models specified in the config file." + ) + parser.add_argument( + "--output_dir", + type=str, + help="Override the base output directory specified in the config file." + ) + args = parser.parse_args() + + try: + # Load API key first - fail fast if not set + api_key = load_api_key() + # Load configuration + config = load_config(args.config) + # Run the benchmark + run_benchmark(config, api_key, args.models, args.output_dir) + except (ValueError, FileNotFoundError, yaml.YAMLError) as e: + logging.error(f"Setup failed: {e}") + except Exception as e: + logging.error(f"An unexpected error occurred during benchmark execution: {e}", exc_info=True) diff --git a/src/data_loader.py b/src/data_loader.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/evaluation.py b/src/evaluation.py new file mode 100644 index 0000000000000000000000000000000000000000..afd0685fa8e4a2016ee67ba84acd5138cfd0d9f6 --- /dev/null +++ b/src/evaluation.py @@ -0,0 +1,102 @@ +import logging +from typing import List, Optional, Union + +# Configure logging +logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') + +def calculate_accuracy(predictions: List[Optional[List[int]]], ground_truths: List[List[int]]) -> float: + """ + Calculates the accuracy of LLM predictions against ground truths. + + Accuracy is defined as the percentage of predictions where the predicted list + of answer numbers exactly matches the ground truth list of answer numbers + (order within the list does not matter). A prediction of None (due to parsing failure) + is considered incorrect. + + Args: + predictions (List[Optional[List[int]]]): A list where each element is either a list + of predicted answer integers or None if + parsing failed for that question. + ground_truths (List[List[int]]): A list where each element is a list of + ground truth answer integers. + + Returns: + float: The calculated accuracy (between 0.0 and 1.0). + + Raises: + ValueError: If the lengths of predictions and ground_truths lists do not match. + """ + if len(predictions) != len(ground_truths): + raise ValueError(f"Length mismatch: Predictions ({len(predictions)}) vs Ground Truths ({len(ground_truths)})") + + if not ground_truths: + return 0.0 # Avoid division by zero if the list is empty + + correct_count = 0 + for i, pred in enumerate(predictions): + truth = ground_truths[i] + + # Sort both lists for order-independent comparison + # Handle None case for prediction + sorted_pred = sorted(pred) if pred is not None else None + sorted_truth = sorted(truth) + + if sorted_pred is not None and sorted_pred == sorted_truth: + correct_count += 1 + else: + logging.debug(f"Incorrect prediction for index {i}: Pred={sorted_pred}, Truth={sorted_truth}") + + + accuracy = correct_count / len(ground_truths) + logging.info(f"Accuracy calculated: {correct_count}/{len(ground_truths)} = {accuracy:.4f}") + return accuracy + +# Example Usage (for testing) +if __name__ == '__main__': + print("Running evaluation tests...") + + # Test case 1: Perfect match + preds1 = [[1], [2], [1, 3]] + truths1 = [[1], [2], [3, 1]] + acc1 = calculate_accuracy(preds1, truths1) + print(f"Test Case 1: Preds={preds1}, Truths={truths1} -> Accuracy: {acc1} (Expected: 1.0)") + assert acc1 == 1.0 + + # Test case 2: One wrong + preds2 = [[1], [4], [1, 3]] + truths2 = [[1], [2], [3, 1]] + acc2 = calculate_accuracy(preds2, truths2) + print(f"Test Case 2: Preds={preds2}, Truths={truths2} -> Accuracy: {acc2} (Expected: ~0.6667)") + assert abs(acc2 - 2/3) < 1e-6 + + # Test case 3: Parsing failure (None) + preds3 = [[1], None, [1, 3]] + truths3 = [[1], [2], [3, 1]] + acc3 = calculate_accuracy(preds3, truths3) + print(f"Test Case 3: Preds={preds3}, Truths={truths3} -> Accuracy: {acc3} (Expected: ~0.6667)") + assert abs(acc3 - 2/3) < 1e-6 + + # Test case 4: Empty lists + preds4 = [] + truths4 = [] + acc4 = calculate_accuracy(preds4, truths4) + print(f"Test Case 4: Preds={preds4}, Truths={truths4} -> Accuracy: {acc4} (Expected: 0.0)") + assert acc4 == 0.0 + + # Test case 5: Length mismatch (should raise ValueError) + preds5 = [[1]] + truths5 = [[1], [2]] + try: + calculate_accuracy(preds5, truths5) + print("Test Case 5: FAILED - ValueError not raised") + except ValueError as e: + print(f"Test Case 5: PASSED - Raised ValueError: {e}") + + # Test case 6: Partial match in multiple correct is still wrong + preds6 = [[1], [2], [1]] + truths6 = [[1], [2], [1, 3]] + acc6 = calculate_accuracy(preds6, truths6) + print(f"Test Case 6: Preds={preds6}, Truths={truths6} -> Accuracy: {acc6} (Expected: ~0.6667)") + assert abs(acc6 - 2/3) < 1e-6 + + print("\nEvaluation tests completed.") diff --git a/src/llm_interface.py b/src/llm_interface.py new file mode 100644 index 0000000000000000000000000000000000000000..a514d4d7db38a3381c4e382fa3ad6430e127f8d3 --- /dev/null +++ b/src/llm_interface.py @@ -0,0 +1,182 @@ +import base64 +import requests +import time +import logging +from io import BytesIO +from PIL import Image +from tenacity import retry, stop_after_attempt, wait_exponential, retry_if_exception_type + +from utils import parse_llm_answer + +# Configure logging +logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') + +# OpenRouter API endpoint +OPENROUTER_API_ENDPOINT = "https://openrouter.ai/api/v1/chat/completions" + +# Define exceptions for retry logic +RETRYABLE_EXCEPTIONS = ( + requests.exceptions.Timeout, + requests.exceptions.ConnectionError, + requests.exceptions.RequestException # Catch broader request errors for retries +) + +# Define status codes that warrant a retry +RETRYABLE_STATUS_CODES = {500, 502, 503, 504} + +def should_retry_response(response): + """Check if the response status code warrants a retry.""" + return response.status_code in RETRYABLE_STATUS_CODES + +# Retry decorator configuration +retry_config = dict( + stop=stop_after_attempt(3), # Retry up to 3 times + wait=wait_exponential(multiplier=1, min=2, max=10), # Exponential backoff: 2s, 4s, 8s... + retry=(retry_if_exception_type(RETRYABLE_EXCEPTIONS)) # Retry on specific exceptions + # We will handle status code retries manually within the function for more control +) + +def encode_image_to_base64(image: Image.Image) -> str: + """Encodes a PIL Image object to a base64 string.""" + buffered = BytesIO() + # Ensure image is in RGB format for broad compatibility + if image.mode != 'RGB': + image = image.convert('RGB') + image.save(buffered, format="JPEG") # Save as JPEG for potentially smaller size + img_str = base64.b64encode(buffered.getvalue()).decode('utf-8') + return img_str + +def construct_prompt(base64_image: str) -> list: + """Constructs the message list for the OpenRouter API call.""" + # Modified prompt to encourage structured thinking and tag usage + prompt_text = """You are an expert at analyzing exam questions and extracting the correct answer option(s). + +Examine the provided image of a multiple-choice question carefully. +1. Analyze the question and the provided options. +2. Determine the correct integer number(s) corresponding to the correct option(s). +3. Format your final answer by enclosing ONLY the integer number(s) within tags. + +Examples: +- If the correct option is 2: 2 +- If the correct options are 1 and 3: 1,3 + +It is crucial that your response contains ONLY the tags with the correct integer number(s) inside. Do not include any other text, explanation, or formatting.""" + + messages = [ + { + "role": "user", + "content": [ + {"type": "text", "text": prompt_text}, + { + "type": "image_url", + "image_url": { + "url": f"data:image/jpeg;base64,{base64_image}" + } + } + ] + } + ] + return messages + +@retry(**retry_config) +def get_openrouter_prediction(image: Image.Image, model_identifier: str, api_key: str, max_tokens: int = 100, request_timeout: int = 60) -> tuple[list[int] | None, str | None]: + """ + Gets a prediction from an OpenRouter vision model for a given image. + + Args: + image (Image.Image): The question image as a PIL Image object. + model_identifier (str): The OpenRouter model identifier (e.g., "openai/gpt-4o"). + api_key (str): The OpenRouter API key. + max_tokens (int): Max tokens for the response. + request_timeout (int): Timeout for the API request in seconds. + + Returns: + tuple[list[int] | None, str | None]: A tuple containing: + - The parsed answer as a list of integers (or None if failed). + - The raw response text from the LLM (or None if API call failed). + """ + logging.info(f"Requesting prediction from model: {model_identifier}") + try: + base64_image = encode_image_to_base64(image) + messages = construct_prompt(base64_image) + + headers = { + "Authorization": f"Bearer {api_key}", + "Content-Type": "application/json" + } + + data = { + "model": model_identifier, + "messages": messages, + "max_tokens": max_tokens + } + + response = requests.post( + OPENROUTER_API_ENDPOINT, + headers=headers, + json=data, + timeout=request_timeout + ) + + # Handle specific status code retries (though tenacity handles exceptions) + if response.status_code in RETRYABLE_STATUS_CODES: + logging.warning(f"Received retryable status code {response.status_code} from {model_identifier}. Retrying might occur if configured.") + # Raise an exception to trigger tenacity retry based on status code if needed, + # or handle retry logic more explicitly here if preferred. + # For simplicity, we rely on tenacity for exception-based retries. + # If the request fails multiple times with these codes, it will eventually raise. + response.raise_for_status() # Raise HTTPError for bad status codes after retries fail + + # Handle non-retryable client/server errors + if not response.ok: + logging.error(f"API Error for model {model_identifier}: Status {response.status_code} - {response.text}") + return None, None # Failed API call + + response_json = response.json() + raw_response_text = response_json.get("choices", [{}])[0].get("message", {}).get("content") + + if not raw_response_text: + logging.warning(f"Empty response content received from model: {model_identifier}") + return None, None + + logging.info(f"Raw response received from {model_identifier}: '{raw_response_text[:100]}...'") + parsed_answer = parse_llm_answer(raw_response_text) + + if parsed_answer is None: + logging.warning(f"Failed to parse answer from model {model_identifier}. Raw response: '{raw_response_text}'") + + return parsed_answer, raw_response_text + + except requests.exceptions.Timeout as e: + logging.error(f"Request timed out for model {model_identifier}: {e}") + raise # Re-raise to allow tenacity to handle retry + except requests.exceptions.RequestException as e: + logging.error(f"Request failed for model {model_identifier}: {e}") + raise # Re-raise to allow tenacity to handle retry + except Exception as e: + logging.error(f"An unexpected error occurred for model {model_identifier}: {e}") + # Don't re-raise unexpected errors unless retry logic is designed for them + return None, None # Failed due to unexpected error + +# Example Usage (requires a valid API key in .env and Pillow/requests/tenacity installed) +if __name__ == '__main__': + from src.utils import load_api_key + try: + # Create a dummy black image for testing + dummy_image = Image.new('RGB', (60, 30), color = 'black') + api_key = load_api_key() + # Replace with a model you have access to via OpenRouter, e.g., "openai/gpt-4o" + # Note: Free models might not support vision or follow instructions well. + test_model = "anthropic/claude-3-haiku" # Example model + + print(f"\nTesting prediction with model: {test_model}") + parsed_ans, raw_resp = get_openrouter_prediction(dummy_image, test_model, api_key) + + print(f"Model: {test_model}") + print(f"Parsed Answer: {parsed_ans}") + print(f"Raw Response: {raw_resp}") + + except ValueError as e: + print(f"Setup Error: {e}") + except Exception as e: + print(f"Runtime Error: {e}") diff --git a/src/utils.py b/src/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..ef4551327723bed3f6398a90b003da3805c67b20 --- /dev/null +++ b/src/utils.py @@ -0,0 +1,121 @@ +import os +import re +from dotenv import load_dotenv +import logging + +logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') + +def load_api_key(key_name="OPENROUTER_API_KEY"): + """ + Loads the specified API key from environment variables. + Loads .env file first if it exists. + + Args: + key_name (str): The name of the environment variable holding the API key. + + Returns: + str: The API key. + + Raises: + ValueError: If the API key environment variable is not set. + """ + load_dotenv() # Load environment variables from .env file if it exists + api_key = os.getenv(key_name) + if not api_key: + raise ValueError(f"'{key_name}' environment variable not set. Please create a .env file or set the variable.") + return api_key + +def parse_llm_answer(response_text: str) -> list[int] | None: + """ + Parses the LLM response text to extract integer answers within tags. + Handles potential newlines within the tags and comma-separated values for multiple answers. + + Args: + response_text (str): The raw text response from the LLM. + + Returns: + list[int] | None: A list of integers representing the parsed answer(s), + or None if parsing fails (no tag found, non-integer content). + """ + if not response_text: + return None + + # Regex to find content within ..., allowing for newlines (re.DOTALL) + # It captures the content inside the tags. + match = re.search(r'(.*?)', response_text, re.DOTALL | re.IGNORECASE) + + if match: + extracted_content = match.group(1).strip() + if not extracted_content: + logging.warning(f"Found tag but content is empty.") + return None # Empty content within tags + + # Split by comma and strip whitespace for each potential number + potential_answers = [item.strip() for item in extracted_content.split(',')] + + parsed_answers = [] + valid = True + for ans_str in potential_answers: + if not ans_str: continue # Skip empty strings resulting from trailing commas etc. + try: + # Attempt to convert to integer + parsed_answers.append(int(ans_str)) + except ValueError: + logging.warning(f"Could not parse '{ans_str}' as an integer within tag. Content: '{extracted_content}'") + valid = False + break # Stop parsing this tag content if any part is invalid + + if valid and parsed_answers: + # Return sorted list for consistent comparison later + return sorted(parsed_answers) + else: + # Parsing failed or resulted in empty list after validation + return None + else: + # Fallback: Try to find numbers directly if tags are missing + logging.warning(f"Could not find tag in response: '{response_text[:100]}...'. Attempting fallback parsing.") + # Regex to find one or more digits, potentially separated by commas and whitespace + # This is less precise and might pick up unintended numbers. + numbers = re.findall(r'\b\d+\b', response_text) + if numbers: + try: + parsed_answers = sorted([int(n) for n in numbers]) + logging.info(f"Fallback parsing extracted: {parsed_answers}") + return parsed_answers + except ValueError: + logging.warning(f"Fallback parsing failed to convert found numbers: {numbers}") + return None # Found digits but couldn't convert all + else: + logging.warning(f"Fallback parsing found no digits.") + return None # Tag not found and no digits found + +# Example Usage (for testing) +if __name__ == '__main__': + test_responses = [ + "Some text before 2 and after.", + "Blah blah 1 blah", + "1,3", + " 4 , 2 end", + "\n 3 \n", + "\n 1,\n 4 \n", + "No answer tag here.", + "", + " ", + "abc", + "1, abc", + "1, ", + ",2", + None, + "" + ] + + for resp in test_responses: + parsed = parse_llm_answer(resp) + print(f"Response: '{str(resp)[:50]}...' -> Parsed: {parsed}") + + # Test API key loading (will raise error if .env or env var not set) + # try: + # key = load_api_key() + # print(f"\nAPI Key loaded successfully (partially hidden): {key[:4]}...{key[-4:]}") + # except ValueError as e: + # print(f"\nError loading API key: {e}")