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2026-06-27 07:52:17 +03:30
{
"cells": [
{
"cell_type": "markdown",
"id": "1c94123f",
"metadata": {},
"source": [
"## Score estimation class and implementation"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "e1b8267f",
"metadata": {},
"outputs": [],
"source": [
"from openai import OpenAI\n",
"#from transformers import pipeline\n",
"import re\n",
"import joblib\n",
"import time\n",
"import numpy as np\n",
"\n",
"# positive\n",
"PERSONALITY = \"\"\"\n",
"\n",
"شما از یک مقیاس ده‌تایی برای اعلام میزان رعایت معیار استفاده می‌کنید.\n",
"سپس عدد انتخاب‌شده را به شکل خروجی پایتون بازمی‌گردانید.\n",
"\n",
"مقیاس رضایت شما به‌صورت زیر تعریف می‌شود:\n",
"9. رضایت کامل از رعایت معیار \n",
"8. رضایت بسیار زیاد \n",
"7. رضایت زیاد \n",
"6. نسبتاً راضی \n",
"5. رضایت متوسط \n",
"4. رضایت اندک \n",
"3. نارضایتی اندک \n",
"2. نسبتاً ناراضی \n",
"1. نارضایتی زیاد \n",
"0. عدم رضایت کامل از رعایت معیار\n",
"\"\"\"\n",
"\n",
"INTRO_QUARY = \"\"\"\n",
"اکنون لطفاً متن زیر را بررسی کن. سپس از مقیاس ده‌تایی برای اعلام میزان رعایت معیار استفاده کن و در نهایت، نتیجه را در قالب یک متغیر پایتونی به نام `satisfaction_index` ارائه بده.\n",
"متن نحلیل :\n",
"\n",
"\"\"\"\n",
" \n",
"QUESTION = \"\"\"\n",
"\n",
"در پایان، پاسخ خود را این‌گونه ارائه بده:\n",
"\n",
"```python\n",
"satisfaction_index = عدد انتخابی از 0 تا 9\n",
"```\n",
"\"\"\"\n",
"\n",
"def extract_senmtiment_score(output):\n",
" # If the output contains a code block, extract the list using regex\n",
" import re\n",
"\n",
" # Get only the code block (text between ```python and ```)\n",
" match = re.search(r\"```python\\n(.*?)```\", output, re.DOTALL)\n",
" if match:\n",
" code = match.group(1)\n",
" exec_globals = {}\n",
" try:\n",
" exec(code, exec_globals) # Be cautious with exec; only use with trusted content\n",
" return exec_globals[\"satisfaction_index\"]/9.\n",
" except: \n",
" print(\"No variables found.\")\n",
" print(code)\n",
" return 0\n",
"\n",
" else:\n",
" print(\"No code block or questions found.\")\n",
" print(match)\n",
" \n",
" return 0\n",
"\n",
"from LLMInterface import LLMInterface\n",
"class ScoreSentimentAnalyser(LLMInterface):\n",
" def __init__(self, personality=PERSONALITY, number_of_jobs=64, number_of_meaures_to_valuate=64):\n",
" super().__init__(personality=personality)\n",
" self.number_of_jobs = number_of_jobs\n",
"\n",
" def convert_to_message(self, query):\n",
" return INTRO_QUARY +'\\n'+ query +'\\n'+ QUESTION\n",
"\n",
" def get_text_analysis(self, list_of_analysis):\n",
" def func(i):\n",
" analysis = list_of_analysis[i]\n",
" query = self.convert_to_message(analysis)\n",
" output = self.get_reponse(query)\n",
" return extract_senmtiment_score(output)\n",
"\n",
" with joblib.parallel_backend(\"threading\"):\n",
" output_analysis = [joblib.Parallel(n_jobs=self.number_of_jobs)(joblib.delayed(func)(i) for i in range(len(list_of_analysis)))]\n",
" return output_analysis"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "f10c0a32",
"metadata": {},
"outputs": [],
"source": [
"analyzer = ScoreSentimentAnalyser()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "e3e683d5",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Resuming from existing output file: Step3_output_article_response_original_3.json\n",
"Resuming from index 290 (checkpoint found)\n"
]
}
],
"source": [
"import json\n",
"import os\n",
"\n",
"CHECKPOINT_FILE = 'checkpoint_original.txt'\n",
"OUTPUT_FILE = 'Step3_output_article_response_original_3.json'\n",
"\n",
"# 1. Load existing output if it exists (resume)\n",
"if os.path.exists(OUTPUT_FILE):\n",
" with open(OUTPUT_FILE, encoding='utf8') as f:\n",
" article_response = json.load(f)\n",
" print(f\"Resuming from existing output file: {OUTPUT_FILE}\")\n",
"else:\n",
" with open('step2-original.json', encoding='utf8') as f:\n",
" article_response = json.load(f)\n",
" print(\"Starting fresh from step2-original.json\")\n",
"\n",
"# 2. Read last processed index from checkpoint\n",
"start = 0\n",
"if os.path.exists(CHECKPOINT_FILE):\n",
" with open(CHECKPOINT_FILE, 'r') as f:\n",
" start = int(f.read().strip()) + 1\n",
" print(f\"Resuming from index {start} (checkpoint found)\")\n",
"\n",
"end = len(article_response)\n",
"\n",
"# 3. Process from start to end\n",
"for i in range(start, end):\n",
" try:\n",
" text = [l[2] for l in article_response[i]['response'][0]]\n",
" scores = [l[3] for l in article_response[i]['response'][0]] # not used later, but kept\n",
" res = analyzer.get_text_analysis(text)[0] # assumes res is list of same length as text\n",
" \n",
" # Update scores\n",
" for j in range(len(article_response[i]['response'][0])):\n",
" article_response[i]['response'][0][j][3] = res[j]\n",
" \n",
" # Save JSON after each successful update\n",
" with open(OUTPUT_FILE, 'w', encoding='utf8') as f:\n",
" json.dump(article_response, f, ensure_ascii=False)\n",
" \n",
" # Update checkpoint (last processed index)\n",
" with open(CHECKPOINT_FILE, 'w') as f:\n",
" f.write(str(i))\n",
" \n",
" print(f\"Processed and saved index {i}\")\n",
" \n",
" except Exception as e:\n",
" print(f\"Error at index {i}: {e}\")\n",
" # Do not update checkpoint – next run will retry this index\n",
" break # stop the loop so you can investigate the error"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "271a77e4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Resuming from existing output file: Step3_output_article_response_opposite_2.json\n",
"Processed and saved index 0\n",
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]
}
],
"source": [
"import json\n",
"import os\n",
"\n",
"CHECKPOINT_FILE = 'checkpoint_opposite.txt'\n",
"OUTPUT_FILE = 'Step3_output_article_response_opposite_2.json'\n",
"\n",
"# 1. Load existing output if it exists (resume)\n",
"if os.path.exists(OUTPUT_FILE):\n",
" with open(OUTPUT_FILE, encoding='utf8') as f:\n",
" article_response = json.load(f)\n",
" print(f\"Resuming from existing output file: {OUTPUT_FILE}\")\n",
"else:\n",
" with open('step2-opposite.json', encoding='utf8') as f:\n",
" article_response = json.load(f)\n",
" print(\"Starting fresh from step2-opposite.json\")\n",
"\n",
"# 2. Read last processed index from checkpoint\n",
"start = 0\n",
"if os.path.exists(CHECKPOINT_FILE):\n",
" with open(CHECKPOINT_FILE, 'r') as f:\n",
" start = int(f.read().strip()) + 1\n",
" print(f\"Resuming from index {start} (checkpoint found)\")\n",
"\n",
"end = len(article_response)\n",
"\n",
"# 3. Process from start to end\n",
"for i in range(start, end):\n",
" try:\n",
" text = [l[2] for l in article_response[i]['response'][0]]\n",
" scores = [l[3] for l in article_response[i]['response'][0]]\n",
" res = analyzer.get_text_analysis(text)[0]\n",
" \n",
" for j in range(len(article_response[i]['response'][0])):\n",
" article_response[i]['response'][0][j][3] = res[j]\n",
" \n",
" # Save JSON incrementally\n",
" with open(OUTPUT_FILE, 'w', encoding='utf8') as f:\n",
" json.dump(article_response, f, ensure_ascii=False)\n",
" \n",
" # Update checkpoint\n",
" with open(CHECKPOINT_FILE, 'w') as f:\n",
" f.write(str(i))\n",
" \n",
" print(f\"Processed and saved index {i}\")\n",
" \n",
" except Exception as e:\n",
" print(f\"Error at index {i}: {e}\")\n",
" break"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.13"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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