code/support_check/dataset_process.py

import json
from pathlib import Path

# Input file (synthetic subclaims dataset)
DATA_PATH = Path(
    "/home/mshahidul/readctrl/data/extracting_subclaim/synthetic_subclaims_first200.json"
)
OUTPUT_PATH = Path(
    "/home/mshahidul/readctrl/data/finetuning_data/dataset_for_sft_support_check_list_new.json"
)


def training_prompt(medical_text, subclaims, labels):
    numbered_subclaims = "\n".join(
        [f"{idx + 1}. {claim}" for idx, claim in enumerate(subclaims)]
    )
    
    system_prompt = f"""
You are an expert medical adjudicator. Determine if the 'Medical Passage' contains the core factual information of each 'Subclaim', even if the passage uses simpler language or layperson terms.
Rules:
- Label 'supported' if the essential meaning is present.
- Label 'not_supported' only if the information is missing or contradicted.
Output: JSON array of strings ['supported', 'not_supported', ...]

Medical text:
{medical_text}

Subclaims:
{numbered_subclaims}
"""

    conversation = {}
    conversation["conversations"] = (
        {"from": "user", "content": system_prompt},
        {"from": "assistant", "content": json.dumps(labels, ensure_ascii=False)},
    )
    return conversation


def load_conversation_dataset(data_path=DATA_PATH):
    with Path(data_path).open("r", encoding="utf-8") as f:
        raw_data = json.load(f)

    formatted_data = []
    for record in raw_data:
        generated = record.get("generated", {})
        medical_text = generated.get("passage", "")
        raw_subclaims = generated.get("subclaims", [])

        subclaims = []
        labels = []
        for subclaim in raw_subclaims:
            claim_text = subclaim.get("claim_text", "").strip()
            if not claim_text:
                continue
            subclaims.append(claim_text)
            labels.append(subclaim.get("label", "not_supported"))

        if not medical_text or not subclaims:
            continue

        formatted_data.append(training_prompt(medical_text, subclaims, labels))

    return formatted_data


# Example usage:
dataset_for_sft = load_conversation_dataset()

with OUTPUT_PATH.open("w", encoding="utf-8") as f:
    json.dump(dataset_for_sft, f, ensure_ascii=False, indent=2)

print(len(dataset_for_sft))
print(dataset_for_sft[0])