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README.md

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+---
+license: cc-by-4.0
+task_categories:
+  - image-classification
+  - visual-question-answering
+language:
+  - en
+tags:
+  - astronomy
+  - astrophysics
+  - benchmark
+  - vision-language-model
+  - multimodal
+  - scientific-reasoning
+  - AGN
+  - spectroscopy
+  - radio-galaxy
+  - light-curve
+size_categories:
+  - 1K<n<10K
+---
+
+# AstroVLBench: A Benchmark for Vision-Language Models in Observational Astronomy
+
+**AstroVLBench** is a comprehensive benchmark for evaluating vision-language models (VLMs) on authentic astronomical observation tasks. It comprises over **4,100** expert-verified evaluation instances spanning five observational modalities central to extragalactic and time-domain research: optical imaging, radio interferometric imaging, multi-wavelength photometry, time-domain light curves, and optical spectroscopy.
+
+The benchmark is uniquely centered around **Active Galactic Nuclei (AGN) classification** — a fundamental task in galaxy astronomy that naturally manifests across all electromagnetic wavebands — enabling clean, controlled comparison of AI capabilities across different modalities while minimizing confounding effects of varying domain knowledge requirements. All ground truth labels are rigorously established through spectroscopic confirmation and expert identification.
+
+> **Paper:** *A systematic evaluation of vision-language models for observational astronomical reasoning tasks*
+> Anonymous, Anonymous, Anonymous, Anonymous
+
+## Tasks Overview
+
+| Task | Domain | Modality | Classes | Samples | Source Surveys |
+|------|--------|----------|---------|---------|----------------|
+| **Task 1** | QSO Host Galaxy | Optical imaging | 2 (AGN, Galaxy) | 557 | HSC-SSP |
+| **Task 2** | Radio Morphology | Radio imaging | 2 (FRI, FRII) | 833 × 2 surveys | MiraBest (FIRST + NVSS) |
+| **Task 3** | SED Classification | Multi-wavelength photometry | 3 (Type-1 AGN, Type-2 AGN, Galaxy) | 168 | AKARI NEP + HSC + Euclid + WISE |
+| **Task 4** | Light Curve | Time-domain photometry | 5 (AGN, SNIa, TDE, RRL, Mira) | 142 | PLAsTiCC |
+| **Task 5** | Spectral Interpretation | Optical spectroscopy | 3 questions | 700 | DESI DR1 |
+
+## Task Details
+
+### Task 1: QSO Host Galaxy Classification
+
+Tests the ability to distinguish between unresolved point sources (quasars) and extended galaxy bulges. The key discriminant is whether the central light profile is dominated by a seeing-limited PSF or resolved stellar emission from the host galaxy.
+
+- **Data:** 557 RGB composite images from HSC-SSP PDR3 (g, r, i bands, 10″×10″ cutouts)
+- **Labels:** 300 spectroscopically confirmed Type-1 AGN, 257 inactive galaxies
+- **Sources:** HSC-SSP ([Aihara et al. 2017](https://doi.org/10.1093/pasj/psx066)), AGN sample ([Li et al. 2021](https://doi.org/10.3847/1538-4357/ac0e3e)), Galaxy sample ([Simard et al. 2011](https://doi.org/10.1088/0067-0049/196/1/11))
+
+### Task 2: Radio Galaxy Morphology Classification
+
+Evaluates morphological classification of radio galaxies into Fanaroff-Riley Type I (FRI, core-brightened) and Type II (FRII, edge-brightened), testing how spatial resolution affects classification.
+
+- **Data:** 833 sources with matched images from two surveys:
+  - MiraBest_F (FIRST survey, higher resolution): 606 images
+  - MiraBest_N (NVSS survey, lower resolution): 833 images
+- **Labels:** 397 FRI, 436 FRII (expert-verified, excluding hybrids)
+- **Sources:** MiraBest ([Porter et al. 2023](https://doi.org/10.1093/mnras/stad256)), FIRST ([Becker et al. 1995](https://doi.org/10.1086/176166)), NVSS ([Condon et al. 1998](https://doi.org/10.1086/300337))
+
+### Task 3: SED Classification
+
+Tests the ability to diagnose source types from multi-wavelength photometry, requiring inference of relative contributions of stellar emission, unobscured accretion, and dust-reprocessed infrared emission.
+
+- **Data:** 168 sources with SED plots (ν f_ν vs. wavelength, log-log scale) and 21-band photometry (HSC g/r/i/z/y, Euclid Y/J/H, AKARI N2–L24, WISE W1–W4)
+- **Labels:** 44 Type-1 AGN, 40 Type-2 AGN, 84 Galaxy (redshift range 0.04 < z < 0.45)
+- **Sources:** AKARI NEP ([Kim et al. 2020](https://doi.org/10.1093/mnras/staa3359)), cross-matched with DESI DR1 AGN/QSO catalog; BPT classifications from [Baldwin et al. 1981](https://doi.org/10.1086/130766), [Veilleux & Osterbrock 1987](https://doi.org/10.1086/191166)
+
+### Task 4: Light Curve Classification
+
+Evaluates classification of five physically distinct classes spanning persistent variables and transient phenomena from multi-band light curves.
+
+- **Data:** 142 objects with light curve plots and flux time-series across 6 LSST-like bands (u, g, r, i, z, y)
+- **Labels:** 30 AGN, 30 SNIa, 30 TDE, 30 RRL, 22 Mira
+- **Sources:** PLAsTiCC ([The PLAsTiCC team 2019](https://doi.org/10.1016/j.ascom.2019.100357))
+
+### Task 5: Spectral Interpretation (ASIB)
+
+Evaluates hierarchical spectral characterization mirroring the logic of astronomical spectral analysis: emission line detection → broadening assessment → line-ratio diagnostics.
+
+- **Data:** 700 optical spectra organized into 7 sub-groups (A, B, C1–C4, D), 100 per group
+- **Questions:**
+  - **Q1** (700 samples, all groups): Both H-alpha and H-beta emission lines present?
+  - **Q2** (500 samples, groups C1–C4, D): Is this a Broad-Line AGN (BLAGN)?
+  - **Q3** (400 samples, groups C1–C4): BPT classification (Star-Forming, Composite, Seyfert, LINER)
+- **Sources:** DESI DR1 ([DESI Collaboration 2025](https://doi.org/10.48550/arXiv.2503.14745)), emission-line measurements from FastSpecFit ([Moustakas et al. 2023](https://doi.org/10.5281/zenodo.8302583))
+
+## Evaluation Protocol
+
+The benchmark supports three levels of evaluation:
+
+1. **Baseline performance** across five modalities with guided prompts
+2. **Mechanistic ablations:**
+   - *Perceptual vs. conceptual guidance:* unguided / phenomenological / physical prompts
+   - *Visual vs. numerical input:* image plots vs. structured magnitude/flux tables
+   - *Few-shot instruction:* zero-shot vs. few-shot with visual exemplars
+3. **Reasoning quality analysis:** evaluating whether correct predictions are accompanied by physically valid justifications
+
+## Directory Structure
+
+```
+AstroVLBench/
+├── data/
+│   ├── Task1_QSOHost/
+│   │   ├── image_labels.csv              # Labels: image path, AGN/Galaxy
+│   │   └── images/                       # 557 RGB galaxy images (JPG)
+│   ├── Task2_RadioMorph/
+│   │   ├── MiraBest_F/
+│   │   │   ├── metadata.jsonl            # Labels: filename, FRI/FRII
+│   │   │   └── images/{FRI,FRII}/        # 606 FIRST radio images (PNG)
+│   │   └── MiraBest_N/
+│   │       ├── metadata.jsonl
+│   │       └── images/{FRI,FRII}/        # 833 NVSS radio images (PNG)
+│   ├── Task3_SED/
+│   │   ├── nirsed_v2_catalog.csv         # Catalog with photometry + labels
+│   │   └── images/                       # 168 SED plot images (PNG)
+│   ├── Task4_LightCurve/
+│   │   ├── manifest.csv
+│   │   ├── figures/{AGN,SNIa,TDE,RRL,Mira}/  # 142 light curve plots (PNG)
+│   │   └── csv/{AGN,SNIa,TDE,RRL,Mira}/      # 142 flux time-series (CSV)
+│   └── Task5_SpecType/
+│       ├── ASIB_v1_selection_with_snr.csv     # Catalog with group labels
+│       ├── figures/{Group_A,...,Group_D}/      # 700 spectrum plots (PNG)
+│       └── spectra/                           # Spectrum CSV files
+└── code/
+    ├── task1/llm.py
+    ├── task2/llm.py
+    ├── task3/llm.py
+    ├── task4/llm.py
+    └── task5/llm.py
+```
+
+## Inference Code
+
+Each task includes a self-contained `llm.py` script supporting:
+
+- Multiple VLMs via OpenAI-compatible API (GPT, Claude, Gemini, Grok, Qwen, InternVL)
+- Multiple prompt strategies (`--prompt-type guided/woguide/phenomenological`)
+- Multiple modalities where applicable (`--modality image/text`)
+- Redshift ablation for Task 3 (`--redshift-mode with/without`)
+- Resume capability (`--resume`)
+
+### Example Usage
+
+```bash
+# Task 1: Classify galaxy images with physical prompt
+python code/task1/llm.py --model gpt-4o --prompt-type guided
+
+# Task 2: Classify radio galaxies (FIRST survey)
+python code/task2/llm.py --dataset MiraBest_F --model gemini-3-pro-preview
+
+# Task 3: SED classification — image vs. text modality
+python code/task3/llm.py --model gpt-4o --modality image --redshift-mode with
+python code/task3/llm.py --model gpt-4o --modality text --redshift-mode with
+
+# Task 4: Light curve classification
+python code/task4/llm.py --model gpt-4o --modality image
+
+# Task 5: Spectral interpretation (three questions)
+python code/task5/llm.py --question Q1 --model gemini-3-pro-preview
+python code/task5/llm.py --question Q2 --model gemini-3-pro-preview
+python code/task5/llm.py --question Q3 --model gemini-3-pro-preview
+```
+
+### Environment Variables
+
+All API credentials are configured via environment variables (no hardcoded URLs):
+
+```bash
+OPENAI_API_KEY=...        # OpenAI API key
+OPENAI_BASE_URL=...       # OpenAI-compatible base URL
+CLAUDE_API_KEY=...        # Anthropic Claude API key
+GROK_API_KEY=...          # xAI Grok API key
+QWEN_API_KEY=...          # Alibaba Qwen API key
+QWEN_BASE_URL=...         # Qwen API base URL
+INTERN_API_KEY=...        # InternVL API key
+INTERN_BASE_URL=...       # InternVL API base URL
+```
+
+## Citation
+
+```bibtex
+@article{anonymous2025astrovlbench,
+  title={A systematic evaluation of vision-language models for observational astronomical reasoning tasks},
+  author={Anonymous and Anonymous and Anonymous and Anonymous},
+  year={2025},
+  url={https://huggingface.co/datasets/anonymous4ai/AstroVLBench}
+}
+```
+
+## License
+
+This dataset is released under the [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) license. The underlying astronomical data are derived from publicly available surveys: HSC-SSP, MiraBest, AKARI NEP, PLAsTiCC, and DESI DR1.

code/task1/llm.py

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+#!/usr/bin/env python3
+"""
+Task 1: QSO Host Galaxy Classification (AGN vs Galaxy)
+
+Classifies galaxy images using vision LLMs with a physics-based prompt
+focusing on accretion disk vs stellar bulge emission mechanisms.
+"""
+
+import argparse
+import base64
+import csv
+import json
+import os
+import pathlib
+import re
+import time
+from typing import Optional
+
+from dotenv import load_dotenv
+load_dotenv(override=True)
+
+from openai import OpenAI
+
+
+# =========================
+# CONFIGURATION
+# =========================
+
+DATA_DIR = pathlib.Path(__file__).resolve().parent.parent.parent / "data" / "Task1_QSOHost"
+
+
+# =========================
+# CLIENT
+# =========================
+
+def get_client(model: str) -> OpenAI:
+    """Create OpenAI-compatible client based on model name.
+
+    Requires environment variables:
+      - OPENAI_API_KEY / OPENAI_BASE_URL for OpenAI/compatible models
+      - CLAUDE_API_KEY for Claude models
+      - GROK_API_KEY for Grok models
+      - QWEN_API_KEY for Qwen models
+      - INTERN_API_KEY for InternVL models
+    """
+    api_key = os.getenv("OPENAI_API_KEY")
+    base_url = os.getenv("OPENAI_BASE_URL")
+
+    if "intern" in model.lower():
+        api_key = os.getenv("INTERN_API_KEY")
+        base_url = os.getenv("INTERN_BASE_URL")
+    elif "qwen" in model.lower():
+        api_key = os.getenv("QWEN_API_KEY")
+        base_url = os.getenv("QWEN_BASE_URL")
+    elif "grok" in model.lower():
+        api_key = os.getenv("GROK_API_KEY")
+    elif "claude" in model.lower():
+        api_key = os.getenv("CLAUDE_API_KEY")
+
+    return OpenAI(api_key=api_key, base_url=base_url)
+
+
+# =========================
+# IMAGE UTILS
+# =========================
+
+def encode_image(path: pathlib.Path) -> str:
+    with open(path, "rb") as f:
+        return base64.b64encode(f.read()).decode("utf-8")
+
+
+# =========================
+# PROMPTS
+# =========================
+
+SYSTEM_PROMPT_GUIDED = """**Task:** Classify this image as either AGN or Galaxy.
+
+**Instructions:**
+Both categories are massive galaxies consisting of billions of stars spread over thousands of light-years. The distinction lies entirely in the physical mechanism dominating the very core:
+- Normal Galaxy: The light emission from the central region is purely the collective glow of the inner stellar populations, typically forming a central "bulge." A bulge is a massive, spheroidal concentration of older stars. While the stellar density naturally increases and peaks at the very center, the emission remains fundamentally rooted in a thermal population distributed across a spatial volume spanning hundreds to thousands of light-years.
+- Type-1 AGN: In addition to the host galaxy's extended stellar components (like the disk and the central bulge), the exact geometric center hosts a supermassive black hole with an active accretion disk. This disk emits a staggering amount of radiative energy from a physically microscopic volume (often just light-days across). This results in a hyper-luminous, practically dimensionless energy source sitting directly on top of the extended stellar bulge.
+
+Examine the image, focusing on the interplay between the central emission and the extended host structure. Rely on your understanding of how these underlying physical realities would naturally manifest visually:
+- If your analysis indicates the *presence* of an active accretion disk governing the core's emission, respond: AGN
+- If your analysis indicates the core is purely governed by distributed stellar emission (e.g., the bulge) without an overpowering active black hole component, respond: Galaxy
+
+**Output requirements:**
+- Respond with a JSON object in the following format: {"answer": "", "reason": ""}
+- The "answer" field must be either: AGN or Galaxy
+- The "reason" field should contain a brief explanation of your classification decision, explicitly linking the visual manifestation to the physical presence or absence of the central accretion disk versus a purely stellar bulge.
+- Do not include any text outside the JSON object"""
+
+SYSTEM_PROMPT_WOGUIDE = """Classify this galaxy image as either AGN or Galaxy.
+Output requirements:
+- Respond with a JSON object in the following format: {"answer": "", "reason": ""}
+- The "answer" field must be either: AGN or Galaxy
+- The "reason" field should contain a brief explanation of your classification decision
+- Do not include any text outside the JSON object"""
+
+SYSTEM_PROMPT_PHENOMENOLOGICAL = """**Task:** Classify this image as either AGN or Galaxy.
+
+**Instructions:**
+Examine the central region of this galaxy image in high detail. Follow these steps internally:
+1. Analyze core sharpness: determine whether the core is an unresolved point source (PSF-like) or a resolved, extended structure.
+2. Check for optical artifacts such as diffraction spikes or saturation bleeding.
+3. Evaluate the transition from the center to the disk: smooth and gradual (bulge-like) versus a sharp point superposed on extended light (AGN-like).
+- If the center is dominated by an unresolved point source consistent with a Type-1 AGN, respond: AGN
+- If the center is dominated by a resolved stellar bulge, respond: Galaxy
+
+**Output requirements:**
+- Respond with a JSON object in the following format: {"answer": "", "reason": ""}
+- The "answer" field must be either: AGN or Galaxy
+- The "reason" field should contain a brief explanation of your classification decision
+- Do not include any text outside the JSON object"""
+
+USER_TEXT = "Label this image based on the underlying physical emission mechanisms present. Respond with JSON format."
+
+
+# =========================
+# MODEL CALL
+# =========================
+
+def classify_image(client: OpenAI, image_path: pathlib.Path, model: str, system_prompt: str, max_completion_tokens: int):
+    img_b64 = encode_image(image_path)
+
+    messages = [
+        {"role": "system", "content": system_prompt},
+        {
+            "role": "user",
+            "content": [
+                {"type": "text", "text": USER_TEXT},
+                {
+                    "type": "image_url",
+                    "image_url": {
+                        "url": f"data:image/jpeg;base64,{img_b64}",
+                        "detail": "high",
+                    },
+                },
+            ],
+        },
+    ]
+
+    extra = {"enable_thinking": False} if "qwen" in model.lower() else {}
+    for attempt in range(5):
+        try:
+            response = client.chat.completions.create(
+                model=model,
+                messages=messages,
+                temperature=0,
+                max_completion_tokens=max_completion_tokens,
+                extra_body=extra if extra else None,
+            )
+            return response
+        except Exception as e:
+            if attempt < 4:
+                wait = 2 ** attempt * 5
+                print(f"  Attempt {attempt+1} failed ({e}), retrying in {wait}s...")
+                time.sleep(wait)
+            else:
+                raise
+
+
+# =========================
+# PARSE PREDICTION
+# =========================
+
+def parse_prediction(raw: str) -> dict:
+    cleaned = re.sub(r"```json\s*", "", raw)
+    cleaned = re.sub(r"```\s*", "", cleaned)
+    cleaned = cleaned.strip()
+    try:
+        return json.loads(cleaned)
+    except json.JSONDecodeError:
+        return {"answer": raw, "reason": ""}
+
+
+# =========================
+# MAIN PIPELINE
+# =========================
+
+def run(
+    csv_path: pathlib.Path,
+    root: pathlib.Path,
+    model: str,
+    limit: Optional[int],
+    results_dir: pathlib.Path,
+    prompt_type: str,
+    max_completion_tokens: int,
+    resume: bool,
+) -> pathlib.Path:
+
+    client = get_client(model)
+
+    if prompt_type == "guided":
+        system_prompt = SYSTEM_PROMPT_GUIDED
+    elif prompt_type == "woguide":
+        system_prompt = SYSTEM_PROMPT_WOGUIDE
+    elif prompt_type == "phenomenological":
+        system_prompt = SYSTEM_PROMPT_PHENOMENOLOGICAL
+    else:
+        raise ValueError(f"Unknown prompt type: {prompt_type}")
+
+    rows = list(csv.DictReader(csv_path.open()))
+    results_dir.mkdir(parents=True, exist_ok=True)
+
+    out_path = results_dir / f"predictions-{prompt_type}-{model}.json"
+
+    results = []
+    processed_images = set()
+    if resume and out_path.exists():
+        with out_path.open("r") as f:
+            results = json.load(f)
+            processed_images = {r["image"] for r in results}
+        print(f"Resuming from {len(results)} existing predictions")
+
+    correct = sum(r["correct"] for r in results)
+    total = len(results)
+
+    for i, row in enumerate(rows):
+        if limit is not None and i >= limit:
+            break
+
+        image_path = (root / row["image"]).resolve()
+        if str(image_path) in processed_images:
+            continue
+
+        label = row["label"].strip()
+
+        response = classify_image(client, image_path, model, system_prompt, max_completion_tokens)
+        content = response.choices[0].message.content
+        pred = parse_prediction(content)
+        answer = pred.get("answer", "")
+        is_correct = answer.strip().lower() == label.lower()
+
+        total += 1
+        correct += int(is_correct)
+
+        results.append({
+            "image": str(image_path),
+            "label": label,
+            "prediction": pred,
+            "correct": int(is_correct),
+            "raw_response": response.model_dump(),
+        })
+        print(f"{image_path.name}: pred={answer} label={label} {'✓' if is_correct else '✗'}")
+
+        with out_path.open("w") as f:
+            json.dump(results, f, indent=2)
+
+    if total > 0:
+        print(f"Accuracy on {total} checked: {correct}/{total} = {correct/total:.2%}")
+
+    print(f"Saved predictions to {out_path}")
+    return out_path
+
+
+# =========================
+# ARGPARSE
+# =========================
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description="Task1: QSO Host Galaxy Classification")
+    parser.add_argument("--csv", type=pathlib.Path, default=DATA_DIR / "image_labels.csv")
+    parser.add_argument("--root", type=pathlib.Path, default=DATA_DIR)
+    parser.add_argument("--model", default="gpt-4o")
+    parser.add_argument("--prompt-type", choices=["guided", "woguide", "phenomenological"], default="guided")
+    parser.add_argument("--limit", type=int, default=None)
+    parser.add_argument("--results-dir", type=pathlib.Path, default=pathlib.Path("./results"))
+    parser.add_argument("--max-completion-tokens", type=int, default=16384)
+    parser.add_argument("--resume", action="store_true")
+    return parser.parse_args()
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    run(
+        csv_path=args.csv,
+        root=args.root,
+        model=args.model,
+        limit=args.limit,
+        results_dir=args.results_dir,
+        prompt_type=args.prompt_type,
+        max_completion_tokens=args.max_completion_tokens,
+        resume=args.resume,
+    )

code/task2/llm.py

@@ -0,0 +1,295 @@
+#!/usr/bin/env python3
+"""
+Task 2: Radio Galaxy Morphology Classification (FRI vs FRII)
+
+Classifies radio galaxy images using the Fanaroff-Riley classification scheme.
+Supports MiraBest_F (FIRST survey) and MiraBest_N (NVSS survey) datasets.
+"""
+
+import argparse
+import base64
+import json
+import os
+import pathlib
+import re
+import time
+from typing import Optional
+
+from dotenv import load_dotenv
+load_dotenv(override=True)
+
+from openai import OpenAI
+
+
+# =========================
+# CONFIGURATION
+# =========================
+
+DATA_DIR = pathlib.Path(__file__).resolve().parent.parent.parent / "data" / "Task2_RadioMorph"
+
+
+# =========================
+# CLIENT
+# =========================
+
+def get_client(model: str) -> OpenAI:
+    """Create OpenAI-compatible client based on model name.
+
+    Requires environment variables:
+      - OPENAI_API_KEY / OPENAI_BASE_URL for OpenAI/compatible models
+      - CLAUDE_API_KEY for Claude models
+      - GROK_API_KEY for Grok models
+      - QWEN_API_KEY for Qwen models
+      - INTERN_API_KEY for InternVL models
+    """
+    api_key = os.getenv("OPENAI_API_KEY")
+    base_url = os.getenv("OPENAI_BASE_URL")
+
+    if "intern" in model.lower():
+        api_key = os.getenv("INTERN_API_KEY")
+        base_url = os.getenv("INTERN_BASE_URL")
+    elif "qwen" in model.lower():
+        api_key = os.getenv("QWEN_API_KEY")
+        base_url = os.getenv("QWEN_BASE_URL")
+    elif "grok" in model.lower():
+        api_key = os.getenv("GROK_API_KEY")
+    elif "claude" in model.lower():
+        api_key = os.getenv("CLAUDE_API_KEY")
+
+    return OpenAI(api_key=api_key, base_url=base_url)
+
+
+# =========================
+# IMAGE UTILS
+# =========================
+
+def encode_image(path: pathlib.Path) -> str:
+    with open(path, "rb") as f:
+        return base64.b64encode(f.read()).decode("utf-8")
+
+
+# =========================
+# DATA LOADING
+# =========================
+
+def load_metadata(dataset: str, data_dir: pathlib.Path = DATA_DIR) -> list:
+    metadata_path = data_dir / dataset / "metadata.jsonl"
+    samples = []
+    with open(metadata_path, 'r') as f:
+        for line in f:
+            data = json.loads(line.strip())
+            samples.append(data)
+    return samples
+
+
+def get_survey_name(dataset: str) -> str:
+    if dataset == "MiraBest_F":
+        return "FIRST"
+    elif dataset == "MiraBest_N":
+        return "NVSS"
+    return "radio"
+
+
+# =========================
+# PROMPTS
+# =========================
+
+def build_prompt_guided(survey: str) -> str:
+    return f"""**Task:** Classify the radio galaxy image from {survey} survey according to the Fanaroff-Riley classification scheme (Class I or Class II).
+
+**Instructions:**
+- FRI (Fanaroff-Riley Type I): Edge-darkened sources where the radio emission is brightest near the core and fades toward the edges. The jets are typically less collimated and more turbulent.
+- FRII (Fanaroff-Riley Type II): Edge-brightened sources with prominent hotspots at the outer edges of the radio lobes. The jets remain well-collimated until they reach the hotspots.
+
+**Output requirements:**
+- Respond with a JSON object in the following format: {{"answer": "", "reason": ""}}
+- The "answer" field must be either: FRI or FRII
+- The "reason" field should contain a brief explanation of your classification decision
+- Do not include any text outside the JSON object
+"""
+
+
+def build_prompt_woguide(survey: str) -> str:
+    return f"""Classify this radio galaxy image from {survey} survey as FRI or FRII.
+
+Output requirements:
+- Respond with a JSON object in the following format: {{"answer": "", "reason": ""}}
+- The "answer" field must be either: FRI or FRII
+- The "reason" field should contain a brief explanation of your classification decision
+- Do not include any text outside the JSON object
+"""
+
+
+USER_TEXT = "Classify this radio galaxy image: FRI or FRII. Respond with JSON format."
+
+
+# =========================
+# MODEL CALL
+# =========================
+
+def classify_image(client: OpenAI, image_path: pathlib.Path, system_prompt: str, model: str, max_completion_tokens: int):
+    img_b64 = encode_image(image_path)
+
+    messages = [
+        {"role": "system", "content": system_prompt},
+        {
+            "role": "user",
+            "content": [
+                {"type": "text", "text": USER_TEXT},
+                {
+                    "type": "image_url",
+                    "image_url": {
+                        "url": f"data:image/png;base64,{img_b64}",
+                        "detail": "high",
+                    },
+                },
+            ],
+        },
+    ]
+
+    extra = {"enable_thinking": False} if "qwen" in model.lower() else {}
+    for attempt in range(5):
+        try:
+            response = client.chat.completions.create(
+                model=model,
+                messages=messages,
+                temperature=0,
+                max_completion_tokens=max_completion_tokens,
+                extra_body=extra if extra else None,
+            )
+            return response
+        except Exception as e:
+            if attempt < 4:
+                wait = 2 ** attempt * 5
+                print(f"  Attempt {attempt+1} failed ({e}), retrying in {wait}s...")
+                time.sleep(wait)
+            else:
+                raise
+
+
+# =========================
+# PARSE PREDICTION
+# =========================
+
+def parse_prediction(raw: str) -> dict:
+    cleaned = re.sub(r"```json\s*", "", raw)
+    cleaned = re.sub(r"```\s*", "", cleaned)
+    cleaned = cleaned.strip()
+    try:
+        return json.loads(cleaned)
+    except json.JSONDecodeError:
+        return {"answer": raw, "reason": ""}
+
+
+def canonicalize_label(value: str) -> str:
+    val = (value or "").strip().upper()
+    if "FRII" in val or "FR2" in val or "II" in val:
+        return "FRII"
+    if "FRI" in val or "FR1" in val:
+        return "FRI"
+    return "Unknown"
+
+
+# =========================
+# MAIN PIPELINE
+# =========================
+
+def run(
+    dataset: str,
+    model: str,
+    limit: Optional[int],
+    results_dir: pathlib.Path,
+    prompt_type: str,
+    max_completion_tokens: int,
+    resume: bool,
+    data_dir: pathlib.Path = DATA_DIR,
+) -> pathlib.Path:
+
+    client = get_client(model)
+    samples = load_metadata(dataset, data_dir)
+    results_dir.mkdir(parents=True, exist_ok=True)
+
+    out_path = results_dir / f"predictions-{dataset}-{prompt_type}-{model}.json"
+
+    results = []
+    processed_images = set()
+    if resume and out_path.exists():
+        with out_path.open("r") as f:
+            results = json.load(f)
+            processed_images = {r["image"] for r in results}
+        print(f"Resuming from {len(results)} existing predictions")
+
+    correct = sum(r["correct"] for r in results)
+    total = len(results)
+
+    survey = get_survey_name(dataset)
+    if prompt_type == "guided":
+        system_prompt = build_prompt_guided(survey)
+    else:
+        system_prompt = build_prompt_woguide(survey)
+
+    for i, sample in enumerate(samples):
+        if limit is not None and i >= limit:
+            break
+
+        image_path = data_dir / dataset / sample["filename"]
+        if str(image_path) in processed_images:
+            continue
+
+        label = sample["label"]
+
+        response = classify_image(client, image_path, system_prompt, model, max_completion_tokens)
+        content = response.choices[0].message.content
+        pred = parse_prediction(content)
+        answer = canonicalize_label(pred.get("answer", ""))
+        is_correct = answer == label
+
+        total += 1
+        correct += int(is_correct)
+
+        results.append({
+            "image": str(image_path),
+            "label": label,
+            "prediction": pred,
+            "correct": int(is_correct),
+            "raw_response": response.model_dump(),
+        })
+        print(f"{sample['filename']}: pred={answer} label={label} {'✓' if is_correct else '✗'}")
+
+        with out_path.open("w") as f:
+            json.dump(results, f, indent=2)
+
+    if total > 0:
+        print(f"Accuracy on {total} checked: {correct}/{total} = {correct/total:.2%}")
+
+    print(f"Saved predictions to {out_path}")
+    return out_path
+
+
+# =========================
+# ARGPARSE
+# =========================
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description="Task2: Radio Galaxy Morphology Classification")
+    parser.add_argument("--dataset", choices=["MiraBest_F", "MiraBest_N"], default="MiraBest_F")
+    parser.add_argument("--model", default="gpt-4o")
+    parser.add_argument("--prompt-type", choices=["guided", "woguide"], default="guided")
+    parser.add_argument("--limit", type=int, default=None)
+    parser.add_argument("--results-dir", type=pathlib.Path, default=pathlib.Path("./results"))
+    parser.add_argument("--max-completion-tokens", type=int, default=16384)
+    parser.add_argument("--resume", action="store_true")
+    return parser.parse_args()
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    run(
+        dataset=args.dataset,
+        model=args.model,
+        limit=args.limit,
+        results_dir=args.results_dir,
+        prompt_type=args.prompt_type,
+        max_completion_tokens=args.max_completion_tokens,
+        resume=args.resume,
+    )

code/task3/llm.py

@@ -0,0 +1,430 @@
+#!/usr/bin/env python3
+"""
+Task 3: SED Classification (Type-1 AGN, Type-2 AGN, or Galaxy)
+
+Classifies astronomical sources from SED plots or text-based magnitude data.
+Supports both image-based and text-based modalities, with/without redshift.
+"""
+
+import argparse
+import base64
+import csv
+import json
+import os
+import pathlib
+import re
+import time
+from typing import Optional
+
+from dotenv import load_dotenv
+load_dotenv(override=True)
+
+from openai import OpenAI
+
+
+# =========================
+# CONFIGURATION
+# =========================
+
+DATA_DIR = pathlib.Path(__file__).resolve().parent.parent.parent / "data" / "Task3_SED"
+
+# Magnitude columns: (band name, mag column, err column, wavelength in microns)
+MAG_COLUMNS = [
+    ("HSC-g",      "HSC_G_MAG",      "HSC_G_MAG_ERR",      0.48),
+    ("HSC-r",      "HSC_R_MAG",      "HSC_R_MAG_ERR",      0.62),
+    ("HSC-i",      "HSC_I_MAG",      "HSC_I_MAG_ERR",      0.77),
+    ("HSC-z",      "HSC_Z_MAG",      "HSC_Z_MAG_ERR",      0.91),
+    ("HSC-Y",      "HSC_Y_MAG",      "HSC_Y_MAG_ERR",      1.00),
+    ("Euclid-Y",   "EUCLID_Y_MAG",   "EUCLID_Y_MAG_ERR",   1.08),
+    ("Euclid-J",   "EUCLID_J_MAG",   "EUCLID_J_MAG_ERR",   1.25),
+    ("Euclid-H",   "EUCLID_H_MAG",   "EUCLID_H_MAG_ERR",   1.65),
+    ("AKARI-N2",   "AKARI_N2_MAG",   "AKARI_N2_MAG_ERR",   2.4),
+    ("AKARI-N3",   "AKARI_N3_MAG",   "AKARI_N3_MAG_ERR",   3.2),
+    ("AKARI-N4",   "AKARI_N4_MAG",   "AKARI_N4_MAG_ERR",   4.1),
+    ("AKARI-S7",   "AKARI_S7_MAG",   "AKARI_S7_MAG_ERR",   7.0),
+    ("AKARI-S9W",  "AKARI_S9W_MAG",  "AKARI_S9W_MAG_ERR",  9.0),
+    ("AKARI-S11",  "AKARI_S11_MAG",  "AKARI_S11_MAG_ERR",  11.0),
+    ("AKARI-L15",  "AKARI_L15_MAG",  "AKARI_L15_MAG_ERR",  15.0),
+    ("AKARI-L18",  "AKARI_L18_MAG",  "AKARI_L18_MAG_ERR",  18.0),
+    ("AKARI-L24",  "AKARI_L24_MAG",  "AKARI_L24_MAG_ERR",  24.0),
+    ("WISE-W1",    "WISE_W1_MAG",    "WISE_W1_MAG_ERR",    3.4),
+    ("WISE-W2",    "WISE_W2_MAG",    "WISE_W2_MAG_ERR",    4.6),
+    ("WISE-W3",    "WISE_W3_MAG",    "WISE_W3_MAG_ERR",    12.0),
+    ("WISE-W4",    "WISE_W4_MAG",    "WISE_W4_MAG_ERR",    22.0),
+]
+
+
+# =========================
+# CLIENT
+# =========================
+
+def get_client(model: str) -> OpenAI:
+    """Create OpenAI-compatible client based on model name.
+
+    Requires environment variables:
+      - OPENAI_API_KEY / OPENAI_BASE_URL for OpenAI/compatible models
+      - CLAUDE_API_KEY for Claude models
+      - GROK_API_KEY for Grok models
+      - QWEN_API_KEY for Qwen models
+      - INTERN_API_KEY for InternVL models
+    """
+    api_key = os.getenv("OPENAI_API_KEY")
+    base_url = os.getenv("OPENAI_BASE_URL")
+
+    if "intern" in model.lower():
+        api_key = os.getenv("INTERN_API_KEY")
+        base_url = os.getenv("INTERN_BASE_URL")
+    elif "qwen" in model.lower():
+        api_key = os.getenv("QWEN_API_KEY")
+        base_url = os.getenv("QWEN_BASE_URL")
+    elif "grok" in model.lower():
+        api_key = os.getenv("GROK_API_KEY")
+    elif "claude" in model.lower():
+        api_key = os.getenv("CLAUDE_API_KEY")
+
+    return OpenAI(api_key=api_key, base_url=base_url)
+
+
+# =========================
+# IMAGE UTILS
+# =========================
+
+def encode_image(path: pathlib.Path) -> str:
+    with open(path, "rb") as f:
+        return base64.b64encode(f.read()).decode("utf-8")
+
+
+def get_image_media_type(path: pathlib.Path) -> str:
+    suffix = path.suffix.lower()
+    if suffix in [".jpg", ".jpeg"]:
+        return "image/jpeg"
+    elif suffix == ".png":
+        return "image/png"
+    return "image/png"
+
+
+# =========================
+# PROMPTS
+# =========================
+
+SYSTEM_PROMPT_IMAGE = """**Task:** Classify the astronomical source shown in the attached SED plot as a 'Type-1 AGN', 'Type-2 AGN', or 'Galaxy'.
+
+**Context:**
+- The plot shows nu_fnu (y-axis, log scale) vs. Wavelength (x-axis, log scale).
+- The upper x-axis shows rest-frame wavelength and the lower x-axis shows observed wavelength.
+- The HSC g, r, i, z, y bands, the Euclid Y, J, H bands, the AKARI N2, N3, N4, S7, S9W, S11, L15, L18, L24 bands, and the WISE W1, W2, W3, W4 bands are marked on the plot (if available).
+- {REDSHIFT_BLOCK}
+
+**Instructions:**
+Normal galaxies are entirely driven by stellar processes, peaking with starlight in the optical/NIR and star-heated cold dust in the MIR. Type 1 AGNs outshine their host galaxies across the board, dominated by the naked accretion disk in the UV/optical and the intensely heated inner dust torus in the MIR. Type 2 AGNs have their central engines hidden behind thick dust, leaving their UV/optical to appear as normal star-dominated host galaxies, while their MIR reveals the hidden monster via the glowing, re-radiating dust torus.
+{REDSHIFT_INSTRUCTION}
+
+**Output requirements:**
+- Respond with a JSON object in the following format: {{"answer": "", "reason": ""}}
+- The "answer" field must be either: Type-1 AGN, Type-2 AGN, or Galaxy
+- The "reason" field should contain a brief explanation of your classification decision
+- Do not include any text outside the JSON object
+"""
+
+SYSTEM_PROMPT_WOGUIDE = """Classify the astronomical source shown in the attached SED plot as a 'Type-1 AGN', 'Type-2 AGN', or 'Galaxy'.
+
+- {REDSHIFT_BLOCK}
+{REDSHIFT_INSTRUCTION}
+
+Output requirements:
+- Respond with a JSON object in the following format: {{"answer": "", "reason": ""}}
+- The "answer" field must be either: Type-1 AGN, Type-2 AGN, or Galaxy
+- The "reason" field should contain a brief explanation of your classification decision
+- Do not include any text outside the JSON object
+"""
+
+
+def format_photometry(row: dict) -> str:
+    lines = []
+    for band_name, mag_col, err_col, wavelength in MAG_COLUMNS:
+        mag = row.get(mag_col, "")
+        err = row.get(err_col, "")
+        if mag and mag.strip() and mag.strip() not in ["", "nan", "NaN"]:
+            try:
+                mag_val = float(mag)
+                if err and err.strip() and err.strip() not in ["", "nan", "NaN"]:
+                    err_val = float(err)
+                    lines.append(f"  {band_name} ({wavelength:.2f} um): {mag_val:.3f} +/- {err_val:.3f} mag")
+                else:
+                    lines.append(f"  {band_name} ({wavelength:.2f} um): {mag_val:.3f} mag")
+            except ValueError:
+                pass
+    return "\n".join(lines) if lines else "  No photometric data available."
+
+
+def build_text_prompt(photometry: str, redshift_mode: str, redshift: float, redshift_err: float) -> str:
+    if redshift_mode == "with":
+        if redshift_err and redshift_err > 0:
+            redshift_block = f"Redshift (z) = {redshift:.4f} +/- {redshift_err:.4f}"
+        else:
+            redshift_block = f"Redshift (z) = {redshift:.4f}"
+        redshift_instruction = "Use the redshift information to interpret rest-frame wavelengths where helpful."
+    else:
+        redshift_block = "Redshift: not provided."
+        redshift_instruction = "Do not assume redshift; base your reasoning on the observer-frame SED only."
+
+    return f"""**Task:** Classify the astronomical source based on the following photometric magnitude data as a 'Type-1 AGN', 'Type-2 AGN', or 'Galaxy'.
+
+**Photometric Data (observed-frame magnitudes):**
+{photometry}
+
+**Context:**
+- {redshift_block}
+- Lower magnitude values indicate brighter flux at that wavelength.
+
+**Instructions:**
+Normal galaxies are entirely driven by stellar processes, peaking with starlight in the optical/NIR and star-heated cold dust in the MIR. Type 1 AGNs outshine their host galaxies across the board, dominated by the naked accretion disk in the UV/optical and the intensely heated inner dust torus in the MIR. Type 2 AGNs have their central engines hidden behind thick dust, leaving their UV/optical to appear as normal star-dominated host galaxies, while their MIR reveals the hidden monster via the glowing, re-radiating dust torus.
+{redshift_instruction}
+
+**Output requirements:**
+- Respond with a JSON object in the following format: {{"answer": "", "reason": ""}}
+- The "answer" field must be either: Type-1 AGN, Type-2 AGN, or Galaxy
+- The "reason" field should contain a brief explanation of your classification decision
+- Do not include any text outside the JSON object
+"""
+
+
+def build_image_prompt(redshift_mode: str, redshift: float, redshift_err: float, prompt_type: str = "guided") -> str:
+    if redshift_mode == "with":
+        if redshift_err and redshift_err > 0:
+            redshift_block = f"Redshift (z) = {redshift:.4f} ± {redshift_err:.4f}"
+        else:
+            redshift_block = f"Redshift (z) = {redshift:.4f}"
+        redshift_instruction = "\nUse the redshift information to interpret rest-frame wavelengths where helpful."
+    else:
+        redshift_block = "Redshift: not provided."
+        redshift_instruction = "\nDo not assume redshift; base your reasoning on the observer-frame SED only."
+
+    template = SYSTEM_PROMPT_IMAGE if prompt_type == "guided" else SYSTEM_PROMPT_WOGUIDE
+    return template.format(
+        REDSHIFT_BLOCK=redshift_block,
+        REDSHIFT_INSTRUCTION=redshift_instruction,
+    )
+
+
+USER_TEXT = "Label this image: Type-1 AGN, Type-2 AGN, or Galaxy. Respond with JSON format."
+
+
+# =========================
+# MODEL CALL
+# =========================
+
+def classify_image(client: OpenAI, image_path: pathlib.Path, system_prompt: str, model: str, max_completion_tokens: int):
+    img_b64 = encode_image(image_path)
+    media_type = get_image_media_type(image_path)
+
+    messages = [
+        {"role": "system", "content": system_prompt},
+        {
+            "role": "user",
+            "content": [
+                {"type": "text", "text": USER_TEXT},
+                {
+                    "type": "image_url",
+                    "image_url": {
+                        "url": f"data:{media_type};base64,{img_b64}",
+                        "detail": "high",
+                    },
+                },
+            ],
+        },
+    ]
+
+    extra = {"enable_thinking": False} if "qwen" in model.lower() else {}
+    for attempt in range(5):
+        try:
+            response = client.chat.completions.create(
+                model=model,
+                messages=messages,
+                temperature=0,
+                max_completion_tokens=max_completion_tokens,
+                extra_body=extra if extra else None,
+            )
+            return response
+        except Exception as e:
+            if attempt < 4:
+                wait = 2 ** attempt * 5
+                print(f"  Attempt {attempt+1} failed ({e}), retrying in {wait}s...")
+                time.sleep(wait)
+            else:
+                raise
+
+
+def classify_text(client: OpenAI, system_prompt: str, model: str, max_completion_tokens: int):
+    messages = [
+        {"role": "system", "content": system_prompt},
+        {"role": "user", "content": "Label this source: Type-1 AGN, Type-2 AGN, or Galaxy. Respond with JSON format."},
+    ]
+
+    extra = {"enable_thinking": False} if "qwen" in model.lower() else {}
+    for attempt in range(5):
+        try:
+            response = client.chat.completions.create(
+                model=model,
+                messages=messages,
+                temperature=0,
+                max_completion_tokens=max_completion_tokens,
+                extra_body=extra if extra else None,
+            )
+            return response
+        except Exception as e:
+            if attempt < 4:
+                wait = 2 ** attempt * 5
+                print(f"  Attempt {attempt+1} failed ({e}), retrying in {wait}s...")
+                time.sleep(wait)
+            else:
+                raise
+
+
+# =========================
+# PARSE PREDICTION
+# =========================
+
+def parse_prediction(raw: str) -> dict:
+    cleaned = re.sub(r"```json\s*", "", raw)
+    cleaned = re.sub(r"```\s*", "", cleaned)
+    cleaned = cleaned.strip()
+    try:
+        return json.loads(cleaned)
+    except json.JSONDecodeError:
+        return {"answer": raw, "reason": ""}
+
+
+def canonicalize_label(value: str) -> str:
+    val = (value or "").strip().lower()
+    if "type-1" in val or "type 1" in val:
+        return "Type-1 AGN"
+    if "type-2" in val or "type 2" in val:
+        return "Type-2 AGN"
+    if "galaxy" in val or "sfg" in val or "star" in val:
+        return "Galaxy"
+    return "Unknown"
+
+
+# =========================
+# MAIN PIPELINE
+# =========================
+
+def run(
+    catalog_path: pathlib.Path,
+    images_dir: pathlib.Path,
+    model: str,
+    limit: Optional[int],
+    results_dir: pathlib.Path,
+    redshift_mode: str,
+    modality: str,
+    prompt_type: str,
+    max_completion_tokens: int,
+    resume: bool,
+) -> pathlib.Path:
+
+    client = get_client(model)
+
+    rows = list(csv.DictReader(catalog_path.open()))
+    results_dir.mkdir(parents=True, exist_ok=True)
+
+    out_path = results_dir / f"predictions-{modality}-{prompt_type}-{model}-{redshift_mode}.json"
+
+    results = []
+    processed_targets = set()
+    if resume and out_path.exists():
+        with out_path.open("r") as f:
+            results = json.load(f)
+            processed_targets = {r["targetid"] for r in results if "prediction" in r}
+        print(f"Resuming from {len(results)} existing predictions")
+
+    correct = sum(r.get("correct", False) for r in results)
+    total = len(results)
+
+    for i, row in enumerate(rows):
+        if limit is not None and i >= limit:
+            break
+
+        targetid = str(row.get("targetid", "")).strip()
+        if not targetid:
+            continue
+        if targetid in processed_targets:
+            continue
+
+        label = (row.get("class") or "").strip()
+        redshift = float(row.get("z", 0.0) or 0.0)
+        redshift_err = float(row.get("zerr", 0.0) or 0.0)
+
+        if modality == "image":
+            matches = list(images_dir.glob(f"*_{targetid}.png"))
+            if not matches:
+                print(f"Warning: image not found for {targetid}")
+                continue
+            image_path = matches[0].resolve()
+            system_prompt = build_image_prompt(redshift_mode, redshift, redshift_err, prompt_type)
+            response = classify_image(client, image_path, system_prompt, model, max_completion_tokens)
+        else:
+            photometry = format_photometry(row)
+            system_prompt = build_text_prompt(photometry, redshift_mode, redshift, redshift_err)
+            response = classify_text(client, system_prompt, model, max_completion_tokens)
+
+        content = response.choices[0].message.content
+        pred = parse_prediction(content)
+        answer = canonicalize_label(pred.get("answer", ""))
+        is_correct = answer.lower() == label.lower()
+
+        total += 1
+        correct += int(is_correct)
+
+        results.append({
+            "targetid": targetid,
+            "label": label,
+            "prediction": pred,
+            "correct": int(is_correct),
+            "raw_response": response.model_dump(),
+        })
+        print(f"{targetid}: pred={answer} label={label} {'✓' if is_correct else '✗'}")
+
+        with out_path.open("w") as f:
+            json.dump(results, f, indent=2)
+
+    if total > 0:
+        print(f"Accuracy on {total} checked: {correct}/{total} = {correct/total:.2%}")
+
+    print(f"Saved predictions to {out_path}")
+    return out_path
+
+
+# =========================
+# ARGPARSE
+# =========================
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description="Task3: SED Classification")
+    parser.add_argument("--catalog", type=pathlib.Path, default=DATA_DIR / "nirsed_v2_catalog.csv")
+    parser.add_argument("--images-dir", type=pathlib.Path, default=DATA_DIR / "images")
+    parser.add_argument("--model", default="gpt-4o")
+    parser.add_argument("--modality", choices=["image", "text"], default="image")
+    parser.add_argument("--prompt-type", choices=["guided", "woguide"], default="guided")
+    parser.add_argument("--redshift-mode", choices=["with", "without"], default="with")
+    parser.add_argument("--limit", type=int, default=None)
+    parser.add_argument("--results-dir", type=pathlib.Path, default=pathlib.Path("./results"))
+    parser.add_argument("--max-completion-tokens", type=int, default=16384)
+    parser.add_argument("--resume", action="store_true")
+    return parser.parse_args()
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    run(
+        catalog_path=args.catalog,
+        images_dir=args.images_dir,
+        model=args.model,
+        limit=args.limit,
+        results_dir=args.results_dir,
+        redshift_mode=args.redshift_mode,
+        modality=args.modality,
+        prompt_type=args.prompt_type,
+        max_completion_tokens=args.max_completion_tokens,
+        resume=args.resume,
+    )

code/task4/llm.py

@@ -0,0 +1,426 @@
+#!/usr/bin/env python3
+"""
+Task 4: Light Curve Classification (AGN / SNIa / TDE / RRL / Mira)
+
+Classifies astronomical transients and variables from light curve images
+or text-based multi-band flux data.
+"""
+
+import argparse
+import base64
+import csv
+import json
+import os
+import pathlib
+import re
+import time
+from typing import Optional
+
+from dotenv import load_dotenv
+load_dotenv(override=True)
+
+from openai import OpenAI
+
+
+# =========================
+# CONFIGURATION
+# =========================
+
+DATA_DIR = pathlib.Path(__file__).resolve().parent.parent.parent / "data" / "Task4_LightCurve"
+CLASSES = ["AGN", "SNIa", "TDE", "RRL", "Mira"]
+
+# Passband index -> LSST filter name and central wavelength (nm)
+PASSBAND_MAP = {
+    0: ("u", 365),
+    1: ("g", 480),
+    2: ("r", 620),
+    3: ("i", 750),
+    4: ("z", 870),
+    5: ("y", 1000),
+}
+
+
+# =========================
+# CLIENT
+# =========================
+
+def get_client(model: str) -> OpenAI:
+    """Create OpenAI-compatible client based on model name.
+
+    Requires environment variables:
+      - OPENAI_API_KEY / OPENAI_BASE_URL for OpenAI/compatible models
+      - CLAUDE_API_KEY for Claude models
+      - GROK_API_KEY for Grok models
+      - QWEN_API_KEY for Qwen models
+      - INTERN_API_KEY for InternVL models
+    """
+    api_key = os.getenv("OPENAI_API_KEY")
+    base_url = os.getenv("OPENAI_BASE_URL")
+
+    if "intern" in model.lower():
+        api_key = os.getenv("INTERN_API_KEY")
+        base_url = os.getenv("INTERN_BASE_URL")
+    elif "qwen" in model.lower():
+        api_key = os.getenv("QWEN_API_KEY")
+        base_url = os.getenv("QWEN_BASE_URL")
+    elif "grok" in model.lower():
+        api_key = os.getenv("GROK_API_KEY")
+    elif "claude" in model.lower():
+        api_key = os.getenv("CLAUDE_API_KEY")
+
+    return OpenAI(api_key=api_key, base_url=base_url)
+
+
+# =========================
+# IMAGE UTILS
+# =========================
+
+def encode_image(path: pathlib.Path) -> str:
+    with open(path, "rb") as f:
+        return base64.b64encode(f.read()).decode("utf-8")
+
+
+# =========================
+# DATA LOADING
+# =========================
+
+def load_catalog_image(data_dir: pathlib.Path = DATA_DIR):
+    samples = []
+    for cls in CLASSES:
+        fig_dir = data_dir / "figures" / cls
+        if not fig_dir.exists():
+            continue
+        for fig in sorted(fig_dir.glob("*.png")):
+            samples.append({
+                "oid": fig.stem,
+                "label": cls,
+                "image_path": fig,
+            })
+    return samples
+
+
+def load_catalog_text(data_dir: pathlib.Path = DATA_DIR):
+    samples = []
+    for cls in CLASSES:
+        csv_dir = data_dir / "csv" / cls
+        if not csv_dir.exists():
+            continue
+        for csv_file in sorted(csv_dir.glob("*.csv")):
+            samples.append({
+                "oid": csv_file.stem,
+                "label": cls,
+                "csv_path": csv_file,
+            })
+    return samples
+
+
+def format_lightcurve(csv_path: pathlib.Path) -> str:
+    rows = []
+    with csv_path.open(newline="") as f:
+        reader = csv.DictReader(f)
+        for row in reader:
+            rows.append(row)
+
+    if not rows:
+        return "No data available."
+
+    by_band: dict[int, list] = {}
+    for row in rows:
+        try:
+            pb = int(float(row["passband"]))
+            mjd = float(row["mjd"])
+            flux = float(row["flux"])
+            flux_err = float(row["flux_err"])
+            detected = int(float(row.get("detected_bool", 1)))
+            by_band.setdefault(pb, []).append((mjd, flux, flux_err, detected))
+        except (ValueError, KeyError):
+            continue
+
+    lines = []
+    for pb in sorted(by_band.keys()):
+        band_name, wave_nm = PASSBAND_MAP.get(pb, (f"band{pb}", 0))
+        obs = by_band[pb]
+        obs.sort(key=lambda x: x[0])
+        lines.append(f"  {band_name}-band ({wave_nm} nm), {len(obs)} observations:")
+        for mjd, flux, flux_err, detected in obs[:50]:
+            det_str = "" if detected else " [non-detection]"
+            lines.append(f"    MJD={mjd:.4f}  flux={flux:.3f} ± {flux_err:.3f}{det_str}")
+        if len(obs) > 50:
+            lines.append(f"    ... ({len(obs) - 50} more observations)")
+
+    return "\n".join(lines)
+
+
+# =========================
+# PROMPTS
+# =========================
+
+SYSTEM_PROMPT_IMAGE = """You are an expert astrophysicist classifying astronomical transients and variables based on their light curve morphology.
+
+Classify the source into exactly one of these 5 categories:
+
+**AGN** — Stochastic, red-noise variability driven by accretion disk fluctuations around a supermassive black hole. Continuous process with no defined periodicity, persisting over long epochs.
+
+**SNIa** — Thermonuclear explosion of a white dwarf. Fast rise to peak brightness followed by a characteristic exponential decline powered by radioactive decay (weeks timescale). A one-time event.
+
+**TDE** — Tidal Disruption Event: a star disrupted by a supermassive black hole. Rapid rise followed by a smooth power-law decline as stellar debris falls back. Sustained high-temperature emission.
+
+**RRL** — RR Lyrae: short-period pulsating star with a period of hours to ~1 day. Strictly periodic, sawtooth-like light curve with rapid rise and slow decline.
+
+**Mira** — Long-period pulsating AGB star. Periodic variability over months to years, with large amplitude and smooth sinusoidal-like variations.
+
+**Output requirements:**
+- Respond with a JSON object: {"answer": "", "reason": ""}
+- The "answer" field must be exactly one of: AGN, SNIa, TDE, RRL, Mira
+- The "reason" field should contain a brief explanation of your classification decision
+- Do not include any text outside the JSON object
+"""
+
+SYSTEM_PROMPT_IMAGE_WOGUIDE = """Classify this light curve into one of: AGN, SNIa, TDE, RRL, Mira.
+
+Output requirements:
+- Respond with a JSON object: {"answer": "", "reason": ""}
+- The "answer" field must be exactly one of: AGN, SNIa, TDE, RRL, Mira
+- The "reason" field should contain a brief explanation of your classification decision
+- Do not include any text outside the JSON object
+"""
+
+SYSTEM_PROMPT_TEXT = """You are an expert astrophysicist classifying astronomical transients and variables based solely on their underlying physical processes.
+
+You will be given multi-band observational flux data (flux in arbitrary units vs. Modified Julian Date). Classify the target into exactly one of the 5 categories below. Evaluate the implied timescales, periodicity, event continuity, and behavior across wavelengths. Base your answer on physical deduction alone.
+
+**AGN** — Stochastic, red-noise variability driven by accretion disk fluctuations around a supermassive black hole. Continuous process lacking any defined global periodicity, persisting over extremely long epochs.
+
+**SNIa** — Thermonuclear explosion of a white dwarf at the Chandrasekhar mass limit. Powered by radioactive decay (⁵⁶Ni → ⁵⁶Co → ⁵⁶Fe). Rapid rise to peak, then characteristic exponential decline over weeks. A single, non-repeating event.
+
+**TDE** — Tidal Disruption Event: a star disrupted by a dormant supermassive black hole. Steady fallback of stellar debris forms a temporary accretion disk. Rapid rise followed by a smooth power-law decline; sustained high-temperature emission.
+
+**RRL** — RR Lyrae: low-mass horizontal branch star with rapid radial pulsations. Strictly periodic, with period of hours to ~1 day. Sawtooth-like profile with fast rise and slow decline.
+
+**Mira** — Asymptotic Giant Branch star undergoing fundamental-mode radial pulsations. Large-amplitude, long-period variability spanning months to years. Smooth, quasi-sinusoidal variations.
+
+**Output requirements:**
+- Respond with a JSON object: {"answer": "", "reason": ""}
+- The "answer" field must be exactly one of: AGN, SNIa, TDE, RRL, Mira
+- The "reason" field should contain a brief explanation based on the light curve properties
+- Do not include any text outside the JSON object
+"""
+
+USER_TEXT_IMAGE = "Classify this light curve: AGN, SNIa, TDE, RRL, or Mira. Respond with JSON format."
+
+
+# =========================
+# MODEL CALLS
+# =========================
+
+def classify_image(client: OpenAI, image_path: pathlib.Path, model: str, system_prompt: str, max_completion_tokens: int):
+    img_b64 = encode_image(image_path)
+
+    messages = [
+        {"role": "system", "content": system_prompt},
+        {
+            "role": "user",
+            "content": [
+                {"type": "text", "text": USER_TEXT_IMAGE},
+                {
+                    "type": "image_url",
+                    "image_url": {
+                        "url": f"data:image/png;base64,{img_b64}",
+                        "detail": "high",
+                    },
+                },
+            ],
+        },
+    ]
+
+    extra = {"enable_thinking": False} if "qwen" in model.lower() else {}
+    for attempt in range(5):
+        try:
+            response = client.chat.completions.create(
+                model=model,
+                messages=messages,
+                temperature=0,
+                max_completion_tokens=max_completion_tokens,
+                extra_body=extra if extra else None,
+            )
+            return response
+        except Exception as e:
+            if attempt < 4:
+                wait = 2 ** attempt * 5
+                print(f"  Attempt {attempt+1} failed ({e}), retrying in {wait}s...")
+                time.sleep(wait)
+            else:
+                raise
+
+
+def classify_text(client: OpenAI, user_prompt: str, model: str, max_completion_tokens: int):
+    messages = [
+        {"role": "system", "content": SYSTEM_PROMPT_TEXT},
+        {"role": "user", "content": user_prompt},
+    ]
+
+    extra = {"enable_thinking": False} if "qwen" in model.lower() else {}
+    for attempt in range(5):
+        try:
+            response = client.chat.completions.create(
+                model=model,
+                messages=messages,
+                temperature=0,
+                max_completion_tokens=max_completion_tokens,
+                extra_body=extra if extra else None,
+            )
+            return response
+        except Exception as e:
+            if attempt < 4:
+                wait = 2 ** attempt * 5
+                print(f"  Attempt {attempt+1} failed ({e}), retrying in {wait}s...")
+                time.sleep(wait)
+            else:
+                raise
+
+
+# =========================
+# PARSE PREDICTION
+# =========================
+
+def parse_prediction(raw: str) -> dict:
+    cleaned = re.sub(r"```json\s*", "", raw)
+    cleaned = re.sub(r"```\s*", "", cleaned)
+    cleaned = cleaned.strip()
+    try:
+        return json.loads(cleaned)
+    except json.JSONDecodeError:
+        return {"answer": raw, "reason": ""}
+
+
+def canonicalize_label(value: str) -> str:
+    val = (value or "").strip()
+    val_up = val.upper()
+    if "AGN" in val_up:
+        return "AGN"
+    if "SNIA" in val_up or "SN IA" in val_up or "TYPE IA" in val_up or val_up == "SN":
+        return "SNIa"
+    if "TDE" in val_up or "TIDAL" in val_up:
+        return "TDE"
+    if "RRL" in val_up or "RR LYR" in val_up:
+        return "RRL"
+    if "MIRA" in val_up:
+        return "Mira"
+    return "Unknown"
+
+
+# =========================
+# MAIN PIPELINE
+# =========================
+
+def run(
+    model: str,
+    limit: Optional[int],
+    results_dir: pathlib.Path,
+    modality: str,
+    prompt_type: str,
+    max_completion_tokens: int,
+    resume: bool,
+    data_dir: pathlib.Path = DATA_DIR,
+) -> pathlib.Path:
+
+    client = get_client(model)
+
+    if modality == "image":
+        samples = load_catalog_image(data_dir)
+    else:
+        samples = load_catalog_text(data_dir)
+    print(f"Loaded {len(samples)} samples")
+
+    results_dir.mkdir(parents=True, exist_ok=True)
+    out_path = results_dir / f"predictions-{modality}-{prompt_type}-{model}.json"
+
+    results = []
+    processed_ids = set()
+    if resume and out_path.exists():
+        with out_path.open("r") as f:
+            results = json.load(f)
+            processed_ids = {r.get("oid") or r.get("image") for r in results}
+        print(f"Resuming from {len(results)} existing predictions")
+
+    correct = sum(r["correct"] for r in results)
+    total = len(results)
+
+    if prompt_type == "guided":
+        system_prompt = SYSTEM_PROMPT_IMAGE
+    else:
+        system_prompt = SYSTEM_PROMPT_IMAGE_WOGUIDE
+
+    for i, sample in enumerate(samples):
+        if limit is not None and i >= limit:
+            break
+
+        oid = sample["oid"]
+        if oid in processed_ids:
+            continue
+
+        label = sample["label"]
+
+        if modality == "image":
+            response = classify_image(client, sample["image_path"], model, system_prompt, max_completion_tokens)
+        else:
+            lightcurve_text = format_lightcurve(sample["csv_path"])
+            user_prompt = f"Multi-band light curve data:\n\n{lightcurve_text}\n\nBased on the physical processes described, classify this source. Respond with JSON format."
+            response = classify_text(client, user_prompt, model, max_completion_tokens)
+
+        content = response.choices[0].message.content
+        pred = parse_prediction(content)
+        answer = canonicalize_label(pred.get("answer", ""))
+        is_correct = answer == label
+
+        total += 1
+        correct += int(is_correct)
+
+        results.append({
+            "oid": oid,
+            "label": label,
+            "prediction": pred,
+            "correct": int(is_correct),
+            "raw_response": response.model_dump(),
+        })
+        print(f"{oid}: pred={answer} label={label} {'✓' if is_correct else '✗'}")
+
+        with out_path.open("w") as f:
+            json.dump(results, f, indent=2)
+
+    if total > 0:
+        print(f"Accuracy on {total} checked: {correct}/{total} = {correct/total:.2%}")
+
+    print(f"Saved predictions to {out_path}")
+    return out_path
+
+
+# =========================
+# ARGPARSE
+# =========================
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description="Task4: Light Curve Classification")
+    parser.add_argument("--model", default="gpt-4o")
+    parser.add_argument("--modality", choices=["image", "text"], default="image")
+    parser.add_argument("--prompt-type", choices=["guided", "woguide"], default="guided")
+    parser.add_argument("--limit", type=int, default=None)
+    parser.add_argument("--results-dir", type=pathlib.Path, default=pathlib.Path("./results"))
+    parser.add_argument("--max-completion-tokens", type=int, default=16384)
+    parser.add_argument("--resume", action="store_true")
+    parser.add_argument("--data-dir", type=pathlib.Path, default=DATA_DIR)
+    return parser.parse_args()
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    run(
+        model=args.model,
+        limit=args.limit,
+        results_dir=args.results_dir,
+        modality=args.modality,
+        prompt_type=args.prompt_type,
+        max_completion_tokens=args.max_completion_tokens,
+        resume=args.resume,
+        data_dir=args.data_dir,
+    )

code/task5/llm.py

@@ -0,0 +1,430 @@
+#!/usr/bin/env python3
+"""
+Task 5: Spectral Type Classification (ASIB Benchmark)
+
+Three questions evaluated:
+  Q1: Does the spectrum contain H-alpha and H-beta emission lines? (all 700 samples)
+  Q2: Is this a Broad-Line AGN (BLAGN)? (500 samples: groups C1-C4, D)
+  Q3: What is the BPT classification? (400 samples: groups C1-C4)
+"""
+
+import argparse
+import base64
+import csv
+import json
+import os
+import pathlib
+import re
+import time
+from typing import Optional, List, Dict
+
+from dotenv import load_dotenv
+load_dotenv(override=True)
+
+from openai import OpenAI
+
+
+# =========================
+# CONFIGURATION
+# =========================
+
+DATA_DIR = pathlib.Path(__file__).resolve().parent.parent.parent / "data" / "Task5_SpecType"
+
+# Groups eligible for each question
+Q1_GROUPS = {"A", "B", "C1", "C2", "C3", "C4", "D"}
+Q2_GROUPS = {"C1", "C2", "C3", "C4", "D"}
+Q3_GROUPS = {"C1", "C2", "C3", "C4"}
+
+# Ground truth
+Q1_TRUTH = {"A": "False", "B": "False", "C1": "True", "C2": "True", "C3": "True", "C4": "True", "D": "True"}
+Q2_TRUTH = {"C1": "False", "C2": "False", "C3": "False", "C4": "False", "D": "True"}
+Q3_TRUTH = {"C1": "Star-Forming", "C2": "Composite", "C3": "Seyfert", "C4": "LINER"}
+
+GROUP_TO_DIR = {
+    "A": "Group_A_High_Z_Trap",
+    "B": "Group_B_Low_Z_Weak",
+    "C1": "Group_C1_BPT_SF",
+    "C2": "Group_C2_BPT_Composite",
+    "C3": "Group_C3_BPT_Seyfert",
+    "C4": "Group_C4_BPT_LINER",
+    "D": "Group_D_Broad_Line_AGN",
+}
+
+
+# =========================
+# CLIENT
+# =========================
+
+def get_client(model: str) -> OpenAI:
+    """Create OpenAI-compatible client based on model name.
+
+    Requires environment variables:
+      - OPENAI_API_KEY / OPENAI_BASE_URL for OpenAI/compatible models
+      - CLAUDE_API_KEY for Claude models
+      - GROK_API_KEY for Grok models
+      - QWEN_API_KEY for Qwen models
+      - INTERN_API_KEY for InternVL models
+    """
+    api_key = os.getenv("OPENAI_API_KEY")
+    base_url = os.getenv("OPENAI_BASE_URL")
+
+    if "intern" in model.lower():
+        api_key = os.getenv("INTERN_API_KEY")
+        base_url = os.getenv("INTERN_BASE_URL")
+    elif "qwen" in model.lower():
+        api_key = os.getenv("QWEN_API_KEY")
+        base_url = os.getenv("QWEN_BASE_URL")
+    elif "grok" in model.lower():
+        api_key = os.getenv("GROK_API_KEY")
+    elif "claude" in model.lower():
+        api_key = os.getenv("CLAUDE_API_KEY")
+
+    return OpenAI(api_key=api_key, base_url=base_url)
+
+
+# =========================
+# IMAGE UTILS
+# =========================
+
+def encode_image(path: pathlib.Path) -> str:
+    with open(path, "rb") as f:
+        return base64.b64encode(f.read()).decode("utf-8")
+
+
+# =========================
+# DATA LOADING
+# =========================
+
+def load_samples(question: str, data_dir: pathlib.Path = DATA_DIR) -> List[Dict]:
+    csv_path = data_dir / "ASIB_v1_selection_with_snr.csv"
+    figures_root = data_dir / "figures"
+
+    eligible_groups = {"Q1": Q1_GROUPS, "Q2": Q2_GROUPS, "Q3": Q3_GROUPS}[question]
+    truth_map = {"Q1": Q1_TRUTH, "Q2": Q2_TRUTH, "Q3": Q3_TRUTH}[question]
+
+    samples = []
+    with csv_path.open(newline="") as f:
+        reader = csv.DictReader(f)
+        for row in reader:
+            group = row["SUB_GROUP"].strip()
+            if group not in eligible_groups:
+                continue
+
+            target_id = row["TARGETID"].strip()
+            group_dir = GROUP_TO_DIR[group]
+            fig_path = figures_root / group_dir / f"spectrum_{target_id}.png"
+
+            if not fig_path.exists():
+                continue
+
+            samples.append({
+                "target_id": target_id,
+                "group": group,
+                "label": truth_map[group],
+                "image_path": fig_path,
+                "z_conf": float(row.get("Z_CONF", 0) or 0),
+                "class_label": row.get("CLASS_LABEL", "").strip(),
+            })
+
+    return samples
+
+
+# =========================
+# PROMPTS
+# =========================
+
+SYSTEM_PROMPT_Q1 = """**Task:** Analyze this optical spectrum of a galaxy to determine whether the spectrum contains BOTH H-alpha (rest wavelength 6563 Angstroms) and H-beta (rest wavelength 4861 Angstroms) emission lines.
+
+**Context:**
+- The spectrum is shown in the observed frame
+- H-alpha and H-beta may be redshifted out of the optical wavelength range or may be weak/non-detectable
+- Emission lines appear as peaks above the continuum
+
+**Output requirements:**
+- Respond with a JSON object in the following format: {"answer": "", "reason": ""}
+- The "answer" field must be either: True or False
+- Answer True if both lines are present, False otherwise
+- The "reason" field should contain a brief explanation
+- Do not include any text outside the JSON object
+"""
+
+SYSTEM_PROMPT_Q2 = """**Task:** Analyze this optical spectrum of a galaxy to determine whether this object is a Broad-Line AGN (BLAGN).
+
+**Context:**
+- A BLAGN is characterized by broad emission lines (FWHM > 1000 km/s), particularly in H-alpha
+- The broad line component is wider than typical narrow-line regions
+- Type-1 AGN / Seyfert 1 classification
+- The H-alpha feature could be blended with [N II] lines which may complicate the profile
+- Look for asymmetric or broadened emission line profiles in the Balmer lines (H-alpha, H-beta)
+
+**Output requirements:**
+- Respond with a JSON object in the following format: {"answer": "", "reason": ""}
+- The "answer" field must be either: True or False
+- Answer True if this is a BLAGN, False otherwise
+- The "reason" field should contain a brief explanation
+- Do not include any text outside the JSON object
+"""
+
+SYSTEM_PROMPT_Q3 = """**Task:** Analyze this optical spectrum of a galaxy and classify it using the BPT (Baldwin-Phillips-Terlevich) diagnostic diagram.
+
+**Context:**
+The BPT diagram uses the line ratios:
+- log([NII] 6584 / H-alpha) on the x-axis
+- log([OIII] 5007 / H-beta) on the y-axis
+
+The four classification regions are:
+1. Star-Forming: Low [NII]/H-alpha and low [OIII]/H-beta (ionization dominated by young stars)
+2. Composite: Intermediate region between SF and AGN (mixed ionization sources)
+3. Seyfert: High [OIII]/H-beta, high [NII]/H-alpha (AGN-dominated ionization)
+4. LINER: High [NII]/H-alpha, low [OIII]/H-beta (low-ionization nuclear emission region)
+
+**Output requirements:**
+- Respond with a JSON object in the following format: {"answer": "", "reason": ""}
+- The "answer" field must be one of: Star-Forming, Composite, Seyfert, or LINER
+- The "reason" field should contain a brief explanation
+- Do not include any text outside the JSON object
+"""
+
+SYSTEM_PROMPT_Q1_WOGUIDE = """Does this spectrum contain both H-alpha and H-beta emission lines?
+
+Output requirements:
+- Respond with a JSON object: {"answer": "", "reason": ""}
+- The "answer" field must be either: True or False
+- Do not include any text outside the JSON object
+"""
+
+SYSTEM_PROMPT_Q2_WOGUIDE = """Is this a Broad-Line AGN (BLAGN)?
+
+Output requirements:
+- Respond with a JSON object: {"answer": "", "reason": ""}
+- The "answer" field must be either: True or False
+- Do not include any text outside the JSON object
+"""
+
+SYSTEM_PROMPT_Q3_WOGUIDE = """Classify this spectrum using the BPT diagram: Star-Forming, Composite, Seyfert, or LINER.
+
+Output requirements:
+- Respond with a JSON object: {"answer": "", "reason": ""}
+- The "answer" field must be one of: Star-Forming, Composite, Seyfert, or LINER
+- Do not include any text outside the JSON object
+"""
+
+USER_TEXT_Q1 = "Does this spectrum contain both H-alpha and H-beta emission lines? Respond with JSON format."
+USER_TEXT_Q2 = "Is this a Broad-Line AGN (BLAGN)? Respond with JSON format."
+USER_TEXT_Q3 = "Classify this spectrum: Star-Forming, Composite, Seyfert, or LINER. Respond with JSON format."
+
+
+def get_prompts(question: str, prompt_type: str = "guided"):
+    if prompt_type == "guided":
+        prompts = {"Q1": SYSTEM_PROMPT_Q1, "Q2": SYSTEM_PROMPT_Q2, "Q3": SYSTEM_PROMPT_Q3}
+    else:
+        prompts = {"Q1": SYSTEM_PROMPT_Q1_WOGUIDE, "Q2": SYSTEM_PROMPT_Q2_WOGUIDE, "Q3": SYSTEM_PROMPT_Q3_WOGUIDE}
+    user_texts = {"Q1": USER_TEXT_Q1, "Q2": USER_TEXT_Q2, "Q3": USER_TEXT_Q3}
+    return prompts[question], user_texts[question]
+
+
+# =========================
+# MODEL CALL
+# =========================
+
+def classify_image(
+    client: OpenAI,
+    image_path: pathlib.Path,
+    model: str,
+    system_prompt: str,
+    user_text: str,
+    max_completion_tokens: int,
+):
+    img_b64 = encode_image(image_path)
+
+    messages = [
+        {"role": "system", "content": system_prompt},
+        {
+            "role": "user",
+            "content": [
+                {"type": "text", "text": user_text},
+                {
+                    "type": "image_url",
+                    "image_url": {
+                        "url": f"data:image/png;base64,{img_b64}",
+                        "detail": "high",
+                    },
+                },
+            ],
+        },
+    ]
+
+    extra = {"enable_thinking": False} if "qwen" in model.lower() else {}
+    for attempt in range(5):
+        try:
+            response = client.chat.completions.create(
+                model=model,
+                messages=messages,
+                temperature=0,
+                max_completion_tokens=max_completion_tokens,
+                extra_body=extra if extra else None,
+            )
+            return response
+        except Exception as e:
+            if attempt < 4:
+                wait = 2 ** attempt * 5
+                print(f"  Attempt {attempt+1} failed ({e}), retrying in {wait}s...")
+                time.sleep(wait)
+            else:
+                raise
+
+
+# =========================
+# PARSE PREDICTION
+# =========================
+
+def parse_prediction(raw: str) -> dict:
+    cleaned = re.sub(r"```json\s*", "", raw)
+    cleaned = re.sub(r"```\s*", "", cleaned)
+    cleaned = cleaned.strip()
+    try:
+        return json.loads(cleaned)
+    except json.JSONDecodeError:
+        return {"answer": raw, "reason": ""}
+
+
+def canonicalize_binary(value) -> str:
+    if isinstance(value, bool):
+        return "True" if value else "False"
+    val = str(value or "").strip().upper()
+    if val.startswith("TRUE") or val == "T":
+        return "True"
+    if val.startswith("FALSE") or val == "F":
+        return "False"
+    if "TRUE" in val and "FALSE" not in val:
+        return "True"
+    if "FALSE" in val and "TRUE" not in val:
+        return "False"
+    return "Unknown"
+
+
+def canonicalize_bpt(value: str) -> str:
+    val = (value or "").strip().upper()
+    if "STAR-FORMING" in val or "STAR FORMING" in val or val == "SF":
+        return "Star-Forming"
+    if "COMPOSITE" in val:
+        return "Composite"
+    if "SEYFERT" in val:
+        return "Seyfert"
+    if "LINER" in val:
+        return "LINER"
+    return "Unknown"
+
+
+def canonicalize_answer(question: str, value: str) -> str:
+    if question in ("Q1", "Q2"):
+        return canonicalize_binary(value)
+    return canonicalize_bpt(value)
+
+
+# =========================
+# MAIN PIPELINE
+# =========================
+
+def run(
+    question: str,
+    model: str,
+    limit: Optional[int],
+    results_dir: pathlib.Path,
+    prompt_type: str,
+    max_completion_tokens: int,
+    resume: bool,
+    data_dir: pathlib.Path = DATA_DIR,
+) -> pathlib.Path:
+
+    client = get_client(model)
+    samples = load_samples(question, data_dir)
+    print(f"Loaded {len(samples)} samples for {question}")
+
+    results_dir.mkdir(parents=True, exist_ok=True)
+    out_path = results_dir / f"predictions-{question}-{prompt_type}-{model}.json"
+
+    system_prompt, user_text = get_prompts(question, prompt_type)
+
+    results = []
+    processed_images = set()
+    if resume and out_path.exists():
+        with out_path.open("r") as f:
+            results = json.load(f)
+            processed_images = {r["image"] for r in results}
+        print(f"Resuming from {len(results)} existing predictions")
+
+    correct = sum(r["correct"] for r in results)
+    total = len(results)
+
+    for i, sample in enumerate(samples):
+        if limit is not None and i >= limit:
+            break
+
+        image_path = sample["image_path"]
+        if str(image_path) in processed_images:
+            continue
+
+        label = sample["label"]
+
+        response = classify_image(client, image_path, model, system_prompt, user_text, max_completion_tokens)
+        content = response.choices[0].message.content
+        pred = parse_prediction(content)
+        answer = canonicalize_answer(question, pred.get("answer", ""))
+        is_correct = answer == label
+
+        total += 1
+        correct += int(is_correct)
+
+        results.append({
+            "image": str(image_path),
+            "target_id": sample["target_id"],
+            "group": sample["group"],
+            "label": label,
+            "prediction": pred,
+            "correct": int(is_correct),
+            "raw_response": response.model_dump(),
+        })
+        print(
+            f"{question} {sample['target_id']} (group={sample['group']}): "
+            f"pred={answer} label={label} "
+            f"{'✓' if is_correct else '✗'}"
+        )
+
+        with out_path.open("w") as f:
+            json.dump(results, f, indent=2)
+
+    if total > 0:
+        print(f"{question} Accuracy on {total} checked: {correct}/{total} = {correct/total:.2%}")
+
+    print(f"Saved predictions to {out_path}")
+    return out_path
+
+
+# =========================
+# ARGPARSE
+# =========================
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description="Task5: Spectral Type Classification (ASIB)")
+    parser.add_argument("--question", choices=["Q1", "Q2", "Q3"], required=True)
+    parser.add_argument("--model", default="gpt-4o")
+    parser.add_argument("--prompt-type", choices=["guided", "woguide"], default="guided")
+    parser.add_argument("--limit", type=int, default=None)
+    parser.add_argument("--results-dir", type=pathlib.Path, default=pathlib.Path("./results"))
+    parser.add_argument("--max-completion-tokens", type=int, default=16384)
+    parser.add_argument("--resume", action="store_true")
+    return parser.parse_args()
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    run(
+        question=args.question,
+        model=args.model,
+        limit=args.limit,
+        results_dir=args.results_dir,
+        prompt_type=args.prompt_type,
+        max_completion_tokens=args.max_completion_tokens,
+        resume=args.resume,
+    )

data/Task1_QSOHost/image_labels.csv

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data/Task4_LightCurve/csv/AGN/AGN_108057850.csv

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data/Task4_LightCurve/csv/AGN/AGN_115859.csv

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data/Task4_LightCurve/csv/AGN/AGN_175907.csv

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data/Task4_LightCurve/csv/AGN/AGN_246759.csv

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data/Task4_LightCurve/csv/AGN/AGN_278407.csv

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data/Task4_LightCurve/csv/AGN/AGN_34052183.csv

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data/Task4_LightCurve/csv/AGN/AGN_48749.csv

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