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{
"cells": [
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd \n",
"import requests \n",
"import datetime as dt\n",
"import re\n",
"import json\n",
"from tqdm import tqdm\n",
"import os\n",
"import time\n",
"from openai import OpenAI"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Calculate"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"if \"OPENAI_API_KEY\" not in os.environ:\n",
" with open('/home/sagemaker-user/Sciences-POC/config/secrets/keys.txt', 'r') as f:\n",
" keys = json.loads(f.read())\n",
"else : \n",
" keys=os.environ"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"save_path = 'data/outputs'\n",
"content_path = 'data/extract_sciences_po'\n",
"\n",
"\n",
"def retrieve_classifications(name, mapping_prompt):\n",
"\n",
" df = pd.read_csv('data/extract_sciences_po.csv')\n",
"\n",
" if os.path.exists(f\"{save_path}/output_{name}.txt\"):\n",
" with open(f\"{save_path}/output_{name}.txt\", 'r') as f : \n",
" out_dict = json.loads(f.read())\n",
" out_df = pd.DataFrame.from_dict(out_dict)\n",
" out = out_dict\n",
" else : \n",
" out_df = pd.DataFrame(columns = ['item_id', 'categorie_principale', 'categorie_secondaire'])\n",
" out = []\n",
"\n",
" df_to_process = df.loc[~df.item_id.isin(out_df.item_id)]\n",
"\n",
" if mapping_prompt[name]['client']=='deepseek':\n",
" client = OpenAI(api_key=keys[\"DEEPSEEK_API_KEY\"], base_url=\"https://api.deepseek.com\")\n",
" model=\"deepseek-chat\"\n",
" else:\n",
" client=OpenAI(api_key=\"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\")\n",
" model=\"gpt-4o-mini\"\n",
"\n",
" with open(mapping_prompt[name]['path_prompt'], 'r') as f:\n",
" prompt = f.read()\n",
"\n",
" if mapping_prompt[name]['client']=='openai-assistant':\n",
" \n",
" assistant = client.beta.assistants.create(\n",
" name=\"News classifier\",\n",
" instructions=prompt,\n",
" response_format={ \"type\": \"json_object\"},\n",
" model=\"gpt-4o-mini\",\n",
" )\n",
"\n",
" assistant_id = assistant.id #mapping_prompt[name]['assistant_id']\n",
"\n",
" with tqdm(total=df_to_process.shape[0]) as pbar:\n",
"\n",
" for i, row in df_to_process.iterrows():\n",
" titre_brut = f\"{row.item_id}_\"+row.titre.lower().strip().replace(f\"\\xa0\", ' ').replace(' : ', ':').replace(' ', '_').replace('/', '')\n",
" \n",
" with open(f'{content_path}/{titre_brut}.txt', 'r') as f:\n",
" text = f.read()\n",
"\n",
" if mapping_prompt[name]['client']=='openai-assistant':\n",
" \n",
" # Step 1: Create a thread\n",
" thread = client.beta.threads.create()\n",
"\n",
" # Step 2: Add a user message\n",
" client.beta.threads.messages.create(\n",
" thread_id=thread.id,\n",
" role=\"user\",\n",
" content=text\n",
" )\n",
"\n",
" # Step 3: Run the assistant\n",
" run = client.beta.threads.runs.create(\n",
" thread_id=thread.id,\n",
" assistant_id=assistant_id,\n",
" )\n",
"\n",
" # Step 4: Wait for completion\n",
" while True:\n",
" run = client.beta.threads.runs.retrieve(\n",
" thread_id=thread.id,\n",
" run_id=run.id,\n",
" )\n",
" if run.status == \"completed\":\n",
" break\n",
" elif run.status in [\"failed\", \"cancelled\", \"expired\"]:\n",
" raise Exception(f\"\"\"Run failed with status: {run.status}\\n\n",
" {run}\"\"\")\n",
" time.sleep(1)\n",
"\n",
" # Step 5: Get last assistant message only\n",
" messages = client.beta.threads.messages.list(thread_id=thread.id)\n",
" assistant_messages = [m for m in messages.data if m.role == \"assistant\"]\n",
"\n",
" if assistant_messages:\n",
" # Get the most recent assistant message\n",
" latest = assistant_messages[0]\n",
" content = latest.content[0].text.value \n",
" \n",
" else:\n",
" messages = [{\"role\": \"system\", \"content\": prompt},\n",
" {\"role\": \"user\", \"content\": text}]\n",
"\n",
" response = client.chat.completions.create(\n",
" model=model,\n",
" messages=messages,\n",
" response_format={\n",
" 'type': 'json_object'\n",
" }\n",
" )\n",
" content = response.choices[0].message.content\n",
" try : \n",
" cat_json = json.loads(content)\n",
"\n",
" out.append({\n",
" 'item_id':row.item_id, \n",
" 'categorie_principale': cat_json['categorie_principale'],\n",
" 'categorie_secondaire': cat_json['categorie_secondaire'],\n",
" })\n",
" \n",
" with open(f'{save_path}/output_{name}.txt', 'w+') as f : \n",
" f.write(json.dumps(out))\n",
"\n",
" except Exception as e : \n",
" print(f'Error with article {row.item_id}')\n",
" pass\n",
"\n",
" \n",
" pbar.update(1)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"dimanov_et_al\n"
]
},
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]
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" 21%|██ | 106/509 [15:06<58:56, 8.78s/it] "
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"Error with article UBNN4MNV35BCVICDFZVGQEAPYU\n"
]
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" 21%|██ | 108/509 [15:21<53:29, 8.00s/it] "
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"Error with article VWT2DL4B2RESPHBN5GLSRSKCXA\n"
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" 21%|██▏ | 109/509 [15:26<46:12, 6.93s/it]"
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"Error with article NE7X4E4S7BGCVN42KSQHYGPR5M\n"
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"Error with article 7MSP4B5UWFDURJY2ZA4E26D6Y4\n"
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"Error with article 5GQ4EE3DBZBJ3FOP2J3FHRAW6Y\n"
]
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"Error with article KXP2DEGANVHOXNRBFFU2X44KME\n"
]
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"Error with article GK5PBHMGJNGH7IC6TRV7BPISRI\n"
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{
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" 95%|█████████▌| 484/509 [1:17:49<04:09, 9.96s/it]"
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"Error with article BQ6E3KG74ZFQPEHRYVAUUDLTRY\n"
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" 97%|█████████▋| 492/509 [1:18:53<01:56, 6.83s/it]"
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"Error with article VC2YC2LPWRA2ZGM6DM3JWZKVHY\n"
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" 99%|█████████▉| 505/509 [1:21:06<00:41, 10.36s/it]"
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"100%|██████████| 509/509 [1:22:17<00:00, 9.70s/it]"
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"sans_titre_1\n"
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},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
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},
{
"ename": "KeyError",
"evalue": "'DEEPSEEK_API_KEY'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[28], line 6\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m mapping\u001b[38;5;241m.\u001b[39mkeys():\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28mprint\u001b[39m(name)\n\u001b[0;32m----> 6\u001b[0m \u001b[43mretrieve_classifications\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmapping\u001b[49m\u001b[43m)\u001b[49m\n",
"Cell \u001b[0;32mIn[27], line 21\u001b[0m, in \u001b[0;36mretrieve_classifications\u001b[0;34m(name, mapping_prompt)\u001b[0m\n\u001b[1;32m 18\u001b[0m df_to_process \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mloc[\u001b[38;5;241m~\u001b[39mdf\u001b[38;5;241m.\u001b[39mitem_id\u001b[38;5;241m.\u001b[39misin(out_df\u001b[38;5;241m.\u001b[39mitem_id)]\n\u001b[1;32m 20\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mapping_prompt[name][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mclient\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m==\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdeepseek\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[0;32m---> 21\u001b[0m client \u001b[38;5;241m=\u001b[39m OpenAI(api_key\u001b[38;5;241m=\u001b[39m\u001b[43mkeys\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mDEEPSEEK_API_KEY\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m, base_url\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhttps://api.deepseek.com\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 22\u001b[0m model\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdeepseek-chat\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 23\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
"File \u001b[0;32m<frozen os>:679\u001b[0m, in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n",
"\u001b[0;31mKeyError\u001b[0m: 'DEEPSEEK_API_KEY'"
]
}
],
"source": [
"with open('config/mapping_prompts.txt', 'r') as f : \n",
" mapping = json.loads(f.read())\n",
"\n",
"for name in mapping.keys():\n",
" print(name)\n",
" retrieve_classifications(name, mapping)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [],
"source": [
"articles = pd.read_csv('data/extract_sciences_po.csv')\n",
"\n",
"with open(\"data/outputs/output_favarel_et_al.txt\", 'r') as f : \n",
" out_dict = json.loads(f.read())\n",
"\n",
"\n",
"df = pd.DataFrame.from_dict(out_dict)\n",
"\n",
"articles = pd.merge(df, articles, on='item_id', how='left')\n",
"\n",
"count_principale = df.groupby('categorie_principale').item_id.count()\n",
"df['categorie_secondaire'] = df.apply(lambda x : x.categorie_secondaire.split(',')[0] if x.categorie_secondaire!=None else None, axis=1)\n",
"\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
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{
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{
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"3 4S4G6BKFRNER3LB22CLPAEWWKY GIVE ME PERSPECTIVE INSPIRE ME\n",
"4 ZAFHRNAHJVC6THXRSBMCB4A24I INSPIRE ME EDUCATE ME\n",
".. ... ... ...\n",
"511 AOT254SA2VDIDNF4YW7XPLWJ5E INSPIRE ME ENTERTAIN ME\n",
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"513 5HT6C24ZBVDOBFXPLA4HNVOTT4 EDUCATE ME UPDATE ME\n",
"514 VLV6RSQ6U5E6XJ6AIRV26AEKO4 UPDATE ME EDUCATE ME\n",
"515 FVCJ6DQ5HVDNDGC4F6F276NVFM UPDATE ME GIVE ME PERSPECTIVE\n",
"\n",
"[516 rows x 3 columns]"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Ajouter images"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
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