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return f'''
### **SYSTEM / ROLE INSTRUCTION**
You are a **medical text rewriting assistant** that improves summaries while maintaining the intended readability level (*easy / intermediate / hard*).
You will receive:
* The **original reference summary** (the factual source)
* The **current generated summary**
* A list of **important missing subclaims** to be reintroduced
* The **target readability level**
Your task:
Revise the generated summary so that it **adds the missing information** naturally, while keeping:
* The same **tone, vocabulary, and sentence simplicity** of the given readability level.
* Logical **flow and coherence**.
* No extra, invented information beyond what’s in the reference summary.
---
### **INPUT FIELDS**
**Reference summary:**
{reference_summary}
**Current generated summary ({difficulty_level}):**
{generated_summary}
**Missing important subclaims to add back:**
{list_of_missing_subclaims}
**Target readability level:**
{difficulty_level}
---
### **TASK INSTRUCTIONS**
1. Integrate the missing subclaims **smoothly** into the generated summary.
2. Do **not** add any new facts beyond those listed.
3. Maintain the **same readability level**:
* **Easy:** conversational, short sentences, no jargon.
* **Intermediate:** light medical terms, brief explanations.
* **Hard:** concise clinical tone with correct terminology.
4. Keep the summary approximately the same length; avoid redundancy.
5. Ensure the resulting text remains **fluent, coherent, and faithful** to the reference summary.
---
### **OUTPUT FORMAT**
```json
{{
"revised_summary": "<the new version of the summary, rewritten with the added subclaims>",
"explanation": "<brief note explaining how the missing subclaims were added while preserving readability>"
}}
```
'''
from openai import OpenAI
import json
file_path = "/home/mshahidul/api_new.json"
with open(file_path, "r") as file:
api_keys = json.load(file)
openai_api_key = api_keys.get("openai")
client = OpenAI(api_key=openai_api_key)
def openai_return(prompt):
response = client.chat.completions.create(
model="gpt-5-mini",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": prompt}
]
)
cleaned_response = response.choices[0].message.content.strip().replace("```json", "").replace("```", "")
return json.loads(cleaned_response)
import json
file_path = "/home/mshahidul/readctrl/data/training_data_subclaim_verifier/synthetic_data_es_subclaims_100.json"
with open(file_path, 'r') as f:
synthetic_data = json.load(f)
# /home/mshahidul/readctrl/results/dataset_quality_check/resonability_check_100_gpt5_completeness.json
with open("/home/mshahidul/readctrl/results/dataset_quality_check/resonability_check_100_gpt5_completeness.json", 'r') as f:
readability_reasoning = json.load(f)
# readability_reasoning[0].keys() # dict_keys(['id', 'difficulty_level', 'prompt'])
# readability_reasoning[0]['prompt'].keys() # dict_keys(['evaluation_table', 'reasonableness_score', 'overall_explanation'])
reason_info={}
for item in readability_reasoning:
id=item['id']
difficulty_level=item['difficulty_level']
data_temp=item['prompt']
for _data in data_temp['evaluation_table']:
if _data['reasonable_omission'] == "no":
key=(id, difficulty_level)
if key not in reason_info:
reason_info[key]=[]
reason_info[key].append(_data['subclaim'])
file_path_qwen3_32B = "/home/mshahidul/readctrl/results/dataset_quality_check/subclaim_verifier_results_100_qwen3-32B.json"
with open(file_path_qwen3_32B, 'r') as f:
qwen3_32B_results = json.load(f)
# dict_keys(['id', 'full_text', 'ref_summary', 'readability_versions'])
# print(f"Full text: {synthetic_data[0]['full_text']}")
import os
# def revised_results(reference_summary, generated_summary, list_of_missing_subclaims, difficulty_level):
res=[]
temp=""
save_path = "/home/mshahidul/readctrl/results/dataset_quality_check/results_revised_100_gpt5.json"
if os.path.exists(save_path):
with open(save_path, 'r') as f:
res = json.load(f)
existing_check=set((entry['id'], entry['difficulty_level']) for entry in res)
print(f"Resuming from {len(res)} entries")
import tqdm
for ind in tqdm.tqdm(range(0,100)):
for version in ["easy", "intermediate", "hard"]:
reference_summary = (f"{synthetic_data[ind]['ref_summary']['text']}")
generated_summary = (f"{synthetic_data[ind]['readability_versions'][version]['text']}")
if (synthetic_data[ind]['id'],version) in existing_check:
continue
if (synthetic_data[ind]['id'],version) not in reason_info:
continue
subclaims_results = reason_info[(synthetic_data[ind]['id'],version)]
prompt = revised_results(reference_summary, generated_summary, subclaims_results, version)
try:
ans=openai_return(prompt)
res.append({
"id": synthetic_data[ind]['id'],
"difficulty_level": version,
"prompt": prompt,
"response": ans
})
if len(res)%2==0:
print(f"Completed {len(res)} out of 300")
with open(save_path, 'w') as outfile:
json.dump(res, outfile, indent=2)
except Exception as e:
print(f"Error at index {ind}, version {version}: {e}")
with open(save_path, 'w') as outfile:
json.dump(res, outfile, indent=2)
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