update persian_nlp_model_sqlite --> fastapi

check_tokenizers.py

@@ -1,28 +1,32 @@
 from transformers import AutoTokenizer
 import json
 
-file = open('./data/models_info.json', 'r')
+file = open('./data/models_ner_info.txt', 'r')
-models = json.load(file)
+models = file.readlines()
+file.close()
 
 # Strips the newline character
 text = 'جمهوری موافقت‌نامه معاملات قانون بودجه اساسی قضائی بین‌المللی تأسیس منطقه‌ای لازم‌الاجراء دامپروری راه‌آهن کمیسیون‌های جدیدالاحداث مسئول فرآورده زائد اسقاط پنجساله'
 results = []
 for line in models:
-    model_checkpoint = line['model_name']
+    model_checkpoint = line.split(' -- ')[1].rstrip('\n')
+    # model_checkpoint = line['model_name']
     try:
         tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
         print(model_checkpoint)
         tokens = tokenizer.tokenize(text)
         print(tokens)
-        results.append({ 'model': model_checkpoint, 'tokens': tokens})
+        print(f'len(tokens): {len(tokens)}')
-        if len(tokens) == 2 : 
+        results.append({ 'model': model_checkpoint, 'len': len(tokens), 'tokens': tokens})
-            file.write( '{'+model_checkpoint + " : [ " + ','.join(tokens)  + ' ] }\n' )
+      
-        #result = tokenizer(text)
-        #print(result)
-        #print(tokenizer.decode(result['input_ids']))
     except:
         error = "An exception occurred in tokenizer : " + model_checkpoint
         #file.write( error + '\n' )
         print(error)
-    #tokenizer.save_pretrained(model_checkpoint+'-tokenizer')
+
-file.close()
+with open('./data/models_tokenizer_info.json', 'w', encoding='utf-8') as file:
+    jsondata = json.dumps(results, ensure_ascii=False, indent=2)
+    file.write(jsondata)
+    
+print('finished!')
+

data/final_ner_selected.txt

@@ -0,0 +1,8 @@
+HooshvareLab/bert-base-parsbert-ner-uncased
+HooshvareLab/bert-fa-base-uncased-ner-peyma
+HooshvareLab/bert-base-parsbert-armanner-uncased
+HooshvareLab/bert-fa-base-uncased-ner-arman
+HooshvareLab/bert-base-parsbert-peymaner-uncased
+nicolauduran45/affilgood-ner-multilingual-v2
+Amirmerfan/bert-base-uncased-persian-ner-50k-base
+AliFartout/Roberta-fa-en-ner

data/models_ner_info.json

@@ -0,0 +1,258 @@
+[
+  {
+    "model_name": "HooshvareLab/bert-base-parsbert-ner-uncased",
+    "task": "token-classification",
+    "last_modified": "2021-5-18",
+    "downloads": 4060,
+    "likes": 5,
+    "language": "fa"
+  },
+  {
+    "model_name": "jplu/tf-xlm-r-ner-40-lang",
+    "task": "token-classification",
+    "last_modified": "2022-10-6",
+    "downloads": 874,
+    "likes": 27,
+    "language": "fa"
+  },
+  {
+    "model_name": "nicolauduran45/affilgood-ner-multilingual-v2",
+    "task": "token-classification",
+    "last_modified": "2025-4-16",
+    "downloads": 668,
+    "likes": 0,
+    "language": "fa"
+  },
+  {
+    "model_name": "HooshvareLab/bert-fa-zwnj-base-ner",
+    "task": "token-classification",
+    "last_modified": "2021-5-18",
+    "downloads": 407,
+    "likes": 4,
+    "language": "fa"
+  },
+  {
+    "model_name": "igorsterner/xlmr-multilingual-sentence-segmentation",
+    "task": "token-classification",
+    "last_modified": "2024-10-5",
+    "downloads": 256,
+    "likes": 4,
+    "language": "fa"
+  },
+  {
+    "model_name": "hamedkhaledi/persian-flair-ner",
+    "task": "token-classification",
+    "last_modified": "2022-4-3",
+    "downloads": 230,
+    "likes": 1,
+    "language": "fa"
+  },
+  {
+    "model_name": "HooshvareLab/bert-fa-base-uncased-ner-peyma",
+    "task": "token-classification",
+    "last_modified": "2021-5-18",
+    "downloads": 138,
+    "likes": 8,
+    "language": "fa"
+  },
+  {
+    "model_name": "mradermacher/persian-question-generator-GGUF",
+    "task": "Unknown",
+    "last_modified": "2024-12-8",
+    "downloads": 110,
+    "likes": 0,
+    "language": "fa"
+  },
+  {
+    "model_name": "HooshvareLab/bert-base-parsbert-armanner-uncased",
+    "task": "token-classification",
+    "last_modified": "2021-5-18",
+    "downloads": 103,
+    "likes": 3,
+    "language": "fa"
+  },
+  {
+    "model_name": "HooshvareLab/distilbert-fa-zwnj-base-ner",
+    "task": "token-classification",
+    "last_modified": "2021-3-21",
+    "downloads": 101,
+    "likes": 4,
+    "language": "fa"
+  },
+  {
+    "model_name": "HooshvareLab/roberta-fa-zwnj-base-ner",
+    "task": "token-classification",
+    "last_modified": "2021-5-20",
+    "downloads": 92,
+    "likes": 1,
+    "language": "fa"
+  },
+  {
+    "model_name": "HooshvareLab/bert-fa-base-uncased-ner-arman",
+    "task": "token-classification",
+    "last_modified": "2021-5-18",
+    "downloads": 71,
+    "likes": 0,
+    "language": "fa"
+  },
+  {
+    "model_name": "HooshvareLab/albert-fa-zwnj-base-v2-ner",
+    "task": "token-classification",
+    "last_modified": "2021-3-21",
+    "downloads": 58,
+    "likes": 0,
+    "language": "fa"
+  },
+  {
+    "model_name": "artificial-nerds/pmnet",
+    "task": "sentence-similarity",
+    "last_modified": "2024-10-29",
+    "downloads": 33,
+    "likes": 0,
+    "language": "fa"
+  },
+  {
+    "model_name": "HooshvareLab/bert-base-parsbert-peymaner-uncased",
+    "task": "token-classification",
+    "last_modified": "2021-5-18",
+    "downloads": 28,
+    "likes": 0,
+    "language": "fa"
+  },
+  {
+    "model_name": "Erfan/mT5-base_Farsi_Title_Generator",
+    "task": "text-generation",
+    "last_modified": "2022-1-30",
+    "downloads": 24,
+    "likes": 2,
+    "language": "fa"
+  },
+  {
+    "model_name": "myrkur/persian-question-generator",
+    "task": "summarization",
+    "last_modified": "2024-12-10",
+    "downloads": 24,
+    "likes": 2,
+    "language": "fa"
+  },
+  {
+    "model_name": "m3hrdadfi/albert-fa-base-v2-ner-arman",
+    "task": "token-classification",
+    "last_modified": "2020-12-26",
+    "downloads": 21,
+    "likes": 3,
+    "language": "fa"
+  },
+  {
+    "model_name": "hezarai/bert-fa-ner-arman",
+    "task": "token-classification",
+    "last_modified": "2024-11-14",
+    "downloads": 17,
+    "likes": 0,
+    "language": "fa"
+  },
+  {
+    "model_name": "m3hrdadfi/albert-fa-base-v2-ner-peyma",
+    "task": "token-classification",
+    "last_modified": "2020-12-26",
+    "downloads": 16,
+    "likes": 1,
+    "language": "fa"
+  },
+  {
+    "model_name": "Amirmerfan/bert-base-uncased-persian-ner-50k-base",
+    "task": "token-classification",
+    "last_modified": "2025-3-18",
+    "downloads": 16,
+    "likes": 0,
+    "language": "fa"
+  },
+  {
+    "model_name": "tum-nlp/neural-news-generator-bloomz-7b1-fa",
+    "task": "text-generation",
+    "last_modified": "2024-8-15",
+    "downloads": 14,
+    "likes": 0,
+    "language": "fa"
+  },
+  {
+    "model_name": "dadashzadeh/test-trainer-persian",
+    "task": "text-classification",
+    "last_modified": "2023-11-13",
+    "downloads": 11,
+    "likes": 0,
+    "language": "fa"
+  },
+  {
+    "model_name": "tum-nlp/neural-news-generator-llama-7b-fa",
+    "task": "text-generation",
+    "last_modified": "2024-8-15",
+    "downloads": 11,
+    "likes": 0,
+    "language": "fa"
+  },
+  {
+    "model_name": "AliFartout/Roberta-fa-en-ner",
+    "task": "token-classification",
+    "last_modified": "2023-9-1",
+    "downloads": 9,
+    "likes": 0,
+    "language": "fa"
+  },
+  {
+    "model_name": "myrkur/persian-title-generator",
+    "task": "summarization",
+    "last_modified": "2024-6-13",
+    "downloads": 9,
+    "likes": 2,
+    "language": "fa"
+  },
+  {
+    "model_name": "NLPclass/mt5-title-generation",
+    "task": "text-generation",
+    "last_modified": "2024-7-24",
+    "downloads": 9,
+    "likes": 0,
+    "language": "fa"
+  },
+  {
+    "model_name": "SinaRp/Question_generator_persian",
+    "task": "text-generation",
+    "last_modified": "2024-11-8",
+    "downloads": 9,
+    "likes": 1,
+    "language": "fa"
+  },
+  {
+    "model_name": "mansoorhamidzadeh/mt5-title-generation",
+    "task": "text-generation",
+    "last_modified": "2024-7-24",
+    "downloads": 8,
+    "likes": 0,
+    "language": "fa"
+  },
+  {
+    "model_name": "dz5035/bicleaner-ai-full-large-de-xx",
+    "task": "Unknown",
+    "last_modified": "2024-6-3",
+    "downloads": 4,
+    "likes": 0,
+    "language": "fa"
+  },
+  {
+    "model_name": "m0javad/conversational_Persian_paraphrase_generation",
+    "task": "Unknown",
+    "last_modified": "2024-2-17",
+    "downloads": 0,
+    "likes": 0,
+    "language": "fa"
+  },
+  {
+    "model_name": "PranavKeshav/event-planner-gemma-4bit",
+    "task": "text-generation",
+    "last_modified": "2025-5-17",
+    "downloads": 0,
+    "likes": 0,
+    "language": "fa"
+  }
+]

data/models_ner_info.txt

@@ -0,0 +1,17 @@
+4060 -- HooshvareLab/bert-base-parsbert-ner-uncased
+874 -- jplu/tf-xlm-r-ner-40-lang
+668 -- nicolauduran45/affilgood-ner-multilingual-v2
+407 -- HooshvareLab/bert-fa-zwnj-base-ner
+230 -- hamedkhaledi/persian-flair-ner
+138 -- HooshvareLab/bert-fa-base-uncased-ner-peyma
+103 -- HooshvareLab/bert-base-parsbert-armanner-uncased
+101 -- HooshvareLab/distilbert-fa-zwnj-base-ner
+92 -- HooshvareLab/roberta-fa-zwnj-base-ner
+71 -- HooshvareLab/bert-fa-base-uncased-ner-arman
+58 -- HooshvareLab/albert-fa-zwnj-base-v2-ner
+28 -- HooshvareLab/bert-base-parsbert-peymaner-uncased
+21 -- m3hrdadfi/albert-fa-base-v2-ner-arman
+17 -- hezarai/bert-fa-ner-arman
+16 -- m3hrdadfi/albert-fa-base-v2-ner-peyma
+16 -- Amirmerfan/bert-base-uncased-persian-ner-50k-base
+9 -- AliFartout/Roberta-fa-en-ner

data/models_tokenizer_info.json

@@ -0,0 +1,465 @@
+[
+  {
+    "model": "HooshvareLab/bert-base-parsbert-ner-uncased",
+    "len": 20,
+    "tokens": [
+      "جمهوری",
+      "موافقتنامه",
+      "معاملات",
+      "قانون",
+      "بودجه",
+      "اساسی",
+      "قضايی",
+      "بینالمللی",
+      "تاسیس",
+      "منطقهای",
+      "لازمالاجراء",
+      "دامپروری",
+      "راهاهن",
+      "کمیسیونهای",
+      "جدیدالاحداث",
+      "مسيول",
+      "فراورده",
+      "زايد",
+      "اسقاط",
+      "پنجساله"
+    ]
+  },
+  {
+    "model": "nicolauduran45/affilgood-ner-multilingual-v2",
+    "len": 39,
+    "tokens": [
+      "▁جمهوری",
+      "▁موافقت",
+      "▁نامه",
+      "▁معاملات",
+      "▁قانون",
+      "▁بودجه",
+      "▁اساسی",
+      "▁قضا",
+      "ئی",
+      "▁بین",
+      "▁المللی",
+      "▁تأسیس",
+      "▁منطقه",
+      "▁ای",
+      "▁لازم",
+      "▁الاج",
+      "راء",
+      "▁دام",
+      "پر",
+      "وری",
+      "▁راه",
+      "▁آهن",
+      "▁کمیسیون",
+      "▁های",
+      "▁جدید",
+      "الا",
+      "حد",
+      "اث",
+      "▁مسئول",
+      "▁فر",
+      "آور",
+      "ده",
+      "▁زائد",
+      "▁اس",
+      "ق",
+      "اط",
+      "▁پنج",
+      "سال",
+      "ه"
+    ]
+  },
+  {
+    "model": "HooshvareLab/bert-fa-zwnj-base-ner",
+    "len": 37,
+    "tokens": [
+      "جمهوری",
+      "موافقت",
+      "[ZWNJ]",
+      "نامه",
+      "معاملات",
+      "قانون",
+      "بودجه",
+      "اساسی",
+      "[UNK]",
+      "بین",
+      "[ZWNJ]",
+      "المللی",
+      "[UNK]",
+      "منطقه",
+      "[ZWNJ]",
+      "ای",
+      "لازم",
+      "[ZWNJ]",
+      "الاجرا",
+      "##ء",
+      "دامپروری",
+      "راه",
+      "[ZWNJ]",
+      "آ",
+      "##هن",
+      "کمیسیون",
+      "[ZWNJ]",
+      "های",
+      "جدیدا",
+      "##لاح",
+      "##داث",
+      "[UNK]",
+      "[UNK]",
+      "[UNK]",
+      "اسقاط",
+      "پنج",
+      "##ساله"
+    ]
+  },
+  {
+    "model": "HooshvareLab/bert-fa-base-uncased-ner-peyma",
+    "len": 20,
+    "tokens": [
+      "جمهوری",
+      "موافقتنامه",
+      "معاملات",
+      "قانون",
+      "بودجه",
+      "اساسی",
+      "قضايی",
+      "بینالمللی",
+      "تاسیس",
+      "منطقهای",
+      "لازمالاجراء",
+      "دامپروری",
+      "راهاهن",
+      "کمیسیونهای",
+      "جدیدالاحداث",
+      "مسيول",
+      "فراورده",
+      "زايد",
+      "اسقاط",
+      "پنجساله"
+    ]
+  },
+  {
+    "model": "HooshvareLab/bert-base-parsbert-armanner-uncased",
+    "len": 20,
+    "tokens": [
+      "جمهوری",
+      "موافقتنامه",
+      "معاملات",
+      "قانون",
+      "بودجه",
+      "اساسی",
+      "قضايی",
+      "بینالمللی",
+      "تاسیس",
+      "منطقهای",
+      "لازمالاجراء",
+      "دامپروری",
+      "راهاهن",
+      "کمیسیونهای",
+      "جدیدالاحداث",
+      "مسيول",
+      "فراورده",
+      "زايد",
+      "اسقاط",
+      "پنجساله"
+    ]
+  },
+  {
+    "model": "HooshvareLab/distilbert-fa-zwnj-base-ner",
+    "len": 37,
+    "tokens": [
+      "جمهوری",
+      "موافقت",
+      "[ZWNJ]",
+      "نامه",
+      "معاملات",
+      "قانون",
+      "بودجه",
+      "اساسی",
+      "[UNK]",
+      "بین",
+      "[ZWNJ]",
+      "المللی",
+      "[UNK]",
+      "منطقه",
+      "[ZWNJ]",
+      "ای",
+      "لازم",
+      "[ZWNJ]",
+      "الاجرا",
+      "##ء",
+      "دامپروری",
+      "راه",
+      "[ZWNJ]",
+      "آ",
+      "##هن",
+      "کمیسیون",
+      "[ZWNJ]",
+      "های",
+      "جدیدا",
+      "##لاح",
+      "##داث",
+      "[UNK]",
+      "[UNK]",
+      "[UNK]",
+      "اسقاط",
+      "پنج",
+      "##ساله"
+    ]
+  },
+  {
+    "model": "HooshvareLab/roberta-fa-zwnj-base-ner",
+    "len": 37,
+    "tokens": [
+      "ĠجÙħÙĩÙĪرÛĮ",
+      "ĠÙħÙĪاÙģÙĤت",
+      "âĢĮ",
+      "ÙĨاÙħÙĩ",
+      "ĠÙħعاÙħÙĦات",
+      "ĠÙĤاÙĨÙĪÙĨ",
+      "ĠبÙĪدجÙĩ",
+      "ĠاساسÛĮ",
+      "ĠÙĤضائÛĮ",
+      "ĠبÛĮÙĨ",
+      "âĢĮ",
+      "اÙĦÙħÙĦÙĦÛĮ",
+      "ĠتأسÛĮس",
+      "ĠÙħÙĨØ·ÙĤÙĩ",
+      "âĢĮ",
+      "اÛĮ",
+      "ĠÙĦازÙħ",
+      "âĢĮ",
+      "اÙĦاج",
+      "راء",
+      "ĠداÙħپرÙĪرÛĮ",
+      "ĠراÙĩ",
+      "âĢĮ",
+      "Ø¢ÙĩÙĨ",
+      "ĠÚ©ÙħÛĮسÛĮÙĪÙĨ",
+      "âĢĮ",
+      "ÙĩاÛĮ",
+      "ĠجدÛĮد",
+      "اÙĦ",
+      "اØŃ",
+      "داث",
+      "ĠÙħسئÙĪÙĦ",
+      "ĠÙģرآÙĪردÙĩ",
+      "Ġزائد",
+      "ĠاسÙĤاط",
+      "ĠÙ¾ÙĨج",
+      "ساÙĦÙĩ"
+    ]
+  },
+  {
+    "model": "HooshvareLab/bert-fa-base-uncased-ner-arman",
+    "len": 20,
+    "tokens": [
+      "جمهوری",
+      "موافقتنامه",
+      "معاملات",
+      "قانون",
+      "بودجه",
+      "اساسی",
+      "قضايی",
+      "بینالمللی",
+      "تاسیس",
+      "منطقهای",
+      "لازمالاجراء",
+      "دامپروری",
+      "راهاهن",
+      "کمیسیونهای",
+      "جدیدالاحداث",
+      "مسيول",
+      "فراورده",
+      "زايد",
+      "اسقاط",
+      "پنجساله"
+    ]
+  },
+  {
+    "model": "HooshvareLab/albert-fa-zwnj-base-v2-ner",
+    "len": 46,
+    "tokens": [
+      "▁جمهوری",
+      "▁موافقت",
+      "[ZWNJ]",
+      "نامه",
+      "▁معاملات",
+      "▁قانون",
+      "▁بودجه",
+      "▁اساسی",
+      "▁قضا",
+      "ي",
+      "ی",
+      "▁بین",
+      "[ZWNJ]",
+      "الم",
+      "لل",
+      "ی",
+      "▁تاسیس",
+      "▁منطقه",
+      "[ZWNJ]",
+      "ای",
+      "▁لازم",
+      "[ZWNJ]",
+      "ال",
+      "اجرا",
+      "ء",
+      "▁دامپروری",
+      "▁راه",
+      "[ZWNJ]",
+      "اهن",
+      "▁کمیسیون",
+      "[ZWNJ]",
+      "های",
+      "▁جدید",
+      "الا",
+      "حد",
+      "اث",
+      "▁مس",
+      "ي",
+      "ول",
+      "▁فراورده",
+      "▁زا",
+      "ي",
+      "د",
+      "▁اسقاط",
+      "▁پنج",
+      "ساله"
+    ]
+  },
+  {
+    "model": "HooshvareLab/bert-base-parsbert-peymaner-uncased",
+    "len": 20,
+    "tokens": [
+      "جمهوری",
+      "موافقتنامه",
+      "معاملات",
+      "قانون",
+      "بودجه",
+      "اساسی",
+      "قضايی",
+      "بینالمللی",
+      "تاسیس",
+      "منطقهای",
+      "لازمالاجراء",
+      "دامپروری",
+      "راهاهن",
+      "کمیسیونهای",
+      "جدیدالاحداث",
+      "مسيول",
+      "فراورده",
+      "زايد",
+      "اسقاط",
+      "پنجساله"
+    ]
+  },
+  {
+    "model": "Amirmerfan/bert-base-uncased-persian-ner-50k-base",
+    "len": 56,
+    "tokens": [
+      "جمهوری",
+      "م",
+      "##وا",
+      "##فق",
+      "##تن",
+      "##ام",
+      "##ه",
+      "مع",
+      "##امل",
+      "##ات",
+      "قانون",
+      "بود",
+      "##جه",
+      "اساسی",
+      "ق",
+      "##ضا",
+      "##يی",
+      "بینالمللی",
+      "تاسیس",
+      "منطقه",
+      "##ای",
+      "لازم",
+      "##ال",
+      "##اج",
+      "##راء",
+      "دا",
+      "##م",
+      "##پر",
+      "##وری",
+      "راه",
+      "##اه",
+      "##ن",
+      "کمی",
+      "##سی",
+      "##ون",
+      "##های",
+      "جدید",
+      "##ال",
+      "##اح",
+      "##دا",
+      "##ث",
+      "م",
+      "##سي",
+      "##ول",
+      "ف",
+      "##را",
+      "##ورد",
+      "##ه",
+      "ز",
+      "##ايد",
+      "اس",
+      "##قا",
+      "##ط",
+      "پنج",
+      "##سال",
+      "##ه"
+    ]
+  },
+  {
+    "model": "AliFartout/Roberta-fa-en-ner",
+    "len": 39,
+    "tokens": [
+      "▁جمهوری",
+      "▁موافقت",
+      "▁نامه",
+      "▁معاملات",
+      "▁قانون",
+      "▁بودجه",
+      "▁اساسی",
+      "▁قضا",
+      "ئی",
+      "▁بین",
+      "▁المللی",
+      "▁تأسیس",
+      "▁منطقه",
+      "▁ای",
+      "▁لازم",
+      "▁الاج",
+      "راء",
+      "▁دام",
+      "پر",
+      "وری",
+      "▁راه",
+      "▁آهن",
+      "▁کمیسیون",
+      "▁های",
+      "▁جدید",
+      "الا",
+      "حد",
+      "اث",
+      "▁مسئول",
+      "▁فر",
+      "آور",
+      "ده",
+      "▁زائد",
+      "▁اس",
+      "ق",
+      "اط",
+      "▁پنج",
+      "سال",
+      "ه"
+    ]
+  }
+]

get_persian_nlp_models.py

@@ -1,7 +1,11 @@
-from huggingface_hub import HfApi, ModelFilter
+from huggingface_hub import HfApi
+from huggingface_hub import ModelCard
+from huggingface_hub import ModelFilter
 import json
 
+
 api = HfApi()
+# card = ModelCard.load('model-name')
 
 # persian languages tags
 languages = ['pes', 'fas', 'fa']  # Language codes for Persian, Pashto, and Turkish

hugingFace_persianModelList.py

@@ -1,173 +0,0 @@
-from huggingface_hub import HfApi
-import json
-import datetime
-
-
-
-# تعریف تسک‌های رایج NLP
-nlp_task_list = [
-    "text-classification",
-    "token-classification",
-    "question-answering",
-    "summarization",
-    "translation",
-    "text-generation",
-    "fill-mask",
-    "zero-shot-classification",
-    "feature-extraction",
-    "sentence-similarity",
-    "text2text-generation",
-    "conversational"
-]
-
-def persian_model_finder(nlp_tasks,json_file_name):
-    
-    api = HfApi()
-    all_persian_nlp_models_data = []
-    seen_model_ids = set() # برای جلوگیری از اضافه شدن مدل‌های تکراری
-
-    print("در حال جستجو و استخراج اطلاعات مدل‌های NLP فارسی...")
-
-    #  فیلتر کردن و پیمایش روی مدل‌ها
-    # برای هر تسک NLP، مدل‌های فارسی را جستجو می‌کنیم.
-    # محدودیت 500 مدل برای هر تسک در نظر گرفته شده است تا از دانلود بیش از حد جلوگیری شود.
-    # اگر می‌خواهید همه مدل‌ها را استخراج کنید، ممکن است نیاز به پیجینیشن (pagination) باشد.
-    for task in nlp_tasks:
-        print(f"  جستجو برای تسک: {task} (زبان: فارسی)...")
-        try:
-            models_for_task = api.list_models(
-                language="fa",
-                task=task,
-                sort="downloads",
-                direction=-1, # نزولی (از بیشترین دانلود به کمترین)
-                limit=None # می‌توانید این عدد را تغییر دهید
-            )
-
-            for model_info in models_for_task:
-                if model_info.id not in seen_model_ids:
-                    try : # اگر از کارت مدل توانست اطلاعات بیشتری به دست بیاورد :
-                        model_ = api.model_info(model_info.id) # به دست آوردن شناسه مدل
-                        card_data_dict = model_.card_data.to_dict() # از روی کارت مدل که شامل اطلاعات مدل میباشد یک دیکشنری میسازیم
-                        model_data = {
-                            "model_id": model_info.id,
-                            "url": f"https://huggingface.co/{model_info.id}",
-                            "downloads": model_info.downloads,
-                            "private": model_info.private,
-                            "author": model_info.author,
-                            "tags": model_info.tags, # شامل زبان‌ها، تسک‌ها، لایبرری‌ها و...
-                            "tag_dataset":"-",
-                            "tag_base_model":"-",
-                            "tag_license":"-",
-                            "tag_region":"-",
-                            "pipeline_tag": model_info.pipeline_tag, # تسک اصلی مدل که توسط هاب تعیین شده
-                            "Likes":model_info.likes,
-                            # چهار مورد پایینی از روی دیکشنری کارت مدل خوانده میشود
-                            "languages":card_data_dict.get('language', 'N/A'), # زبان هایی که پشتیبانی میشود
-                            "library":card_data_dict.get('library', 'N/A'), # کتابخانه های  مورد استفاده
-                            "datasets":card_data_dict.get('datasets', 'N/A'), # دیتابیس های مورد استفاده
-                            "license":card_data_dict.get('license', 'N/A'),
-                            "just_persian" : False
-                            
-                        }
-                        
-                        if model_data["library"] == 'N/A': # در بعضی موارد کتابخانه به این نام ('library_name') در دیکشنری کارت مدل ذخیره شده
-                            model_data["library"] = card_data_dict.get('library_name', 'N/A')
-                        # شرط پایینی ، مواردی که فقط مختص زبان فارسی هستند را در دیکشنری مشخص میکند
-                        if len(model_data["languages"]) == 2 and "multilingual" in model_data["languages"] or\
-                            len(model_data["languages"]) == 2 and "persian" in model_data["languages"] or\
-                            len(model_data["languages"]) == 2 and "farsi" in model_data["languages"] or\
-                            len(model_data["languages"]) == 2 and "fas" in model_data["languages"] or\
-                            len(model_data["languages"]) == 2 and model_data["languages"]=="fa" or\
-                            model_data["languages"] == "persian"  or\
-                            model_data["languages"] == "farsi"  or\
-                            model_data["languages"] == "fas"  or\
-                            model_data["languages"] == "pes"  or\
-                            len(model_data["languages"]) == 1 :
-                            model_data["just_persian"] = True
-                        
-                        for value in model_data["tags"]:
-                            
-                            if "dataset:" in value :
-                                if type(model_data["tag_dataset"]) == type(""):
-                                    model_data["tag_dataset"] = list(model_data["tag_dataset"])
-                                    model_data["tag_dataset"].pop(0)
-                                model_data["tag_dataset"].append(f"{str(value).replace("dataset:","")}")
-                            
-                            if "base_model:" in value :
-                                if type(model_data["tag_base_model"]) == type(""):
-                                    model_data["tag_base_model"] = list(model_data["tag_base_model"])
-                                    model_data["tag_base_model"].pop(0)
-                                model_data["tag_base_model"].append(f"{str(value).replace("base_model:","")}")
-                            
-                            if "region:" in value :
-                                model_data["tag_region"]=f"{str(value).replace("region:","")}"
-                                
-                            if "license:" in value :
-                                model_data["tag_license"]=f"{str(value).replace("license:","")}"
-                            
-                                
-                        all_persian_nlp_models_data.append(model_data)
-                        seen_model_ids.add(model_info.id)
-                        
-                    except : # اگر استفاده از کارت مدل با مشکل مواجه شد :
-                        model_data = {
-                            "model_id": model_info.id,
-                            "url": f"https://huggingface.co/{model_info.id}",
-                            "downloads": model_info.downloads,
-                            "private": model_info.private,
-                            "author": model_info.author,
-                            "tags": model_info.tags, # شامل زبان‌ها، تسک‌ها، لایبرری‌ها و...
-                            "pipeline_tag": model_info.pipeline_tag, # تسک اصلی مدل که توسط هاب تعیین شده
-                            "Likes":model_info.likes,
-                            "library":model_info.library_name,
-                            # افزودن لایسنس اگر موجود باشد
-                            "license": model_info.card_data.license if model_info.card_data and model_info.card_data.license else "N/A"
-                        }
-                        
-                        for value in model_data["tags"]:
-                            
-                            if "dataset:" in value :
-                                if type(model_data["tag_dataset"]) == type(""):
-                                    model_data["tag_dataset"] = list(model_data["tag_dataset"])
-                                    model_data["tag_dataset"].pop(0)
-                                model_data["tag_dataset"].append(f"{str(value).replace("dataset:","")}")
-                            
-                            if "base_model:" in value :
-                                if type(model_data["tag_base_model"]) == type(""):
-                                    model_data["tag_base_model"] = list(model_data["tag_base_model"])
-                                    model_data["tag_base_model"].pop(0)
-                                model_data["tag_base_model"].append(f"{str(value).replace("base_model:","")}")
-                            
-                            if "region:" in value :
-                                model_data["tag_region"]=f"{str(value).replace("region:","")}"
-                                
-                            if "license:" in value :
-                                model_data["tag_license"]=f"{str(value).replace("license:","")}"
-                                
-                        all_persian_nlp_models_data.append(model_data)
-                        seen_model_ids.add(model_info.id)
-                        
-            print(f"    تعداد مدل‌های یافت شده برای تسک '{task}': {len(models_for_task)}")
-
-        except Exception as e:
-            print(f"  خطا در جستجو برای تسک {task}: {e}")
-
-    # مرتب‌سازی نهایی مدل‌ها بر اساس تعداد دانلود (کل لیست)
-    # این مرحله اطمینان می‌دهد که حتی اگر مدل‌ها از تسک‌های مختلف جمع‌آوری شده باشند،
-    # در نهایت بر اساس دانلود مرتب شده باشند.
-    all_persian_nlp_models_data_sorted = sorted(all_persian_nlp_models_data, key=lambda x: x['downloads'], reverse=True)
-
-    print(f"\nتعداد کل مدل‌های NLP فارسی منحصربه‌فرد یافت شده: {len(all_persian_nlp_models_data_sorted)}")
-
-    #  ذخیره اطلاعات در یک فایل JSON
-    output_json_file = f"{json_file_name}"
-    with open(output_json_file, "w", encoding="utf-8") as f:
-        json.dump(all_persian_nlp_models_data_sorted, f, ensure_ascii=False, indent=4)
-
-    print(f"اطلاعات {len(all_persian_nlp_models_data_sorted)} مدل در فایل '{output_json_file}' ذخیره شد.")
-    
-    
-    
-persian_model_finder(nlp_task_list,"persian_nlp_models_info.json")
-
-

important_tokenizers.txt

@@ -0,0 +1,3 @@
+facebook/xlm-v-base
+sharif-dal/dal-bert
+lifeweb-ai/shiraz

model_filter.py

@@ -0,0 +1,30 @@
+import json
+
+with open('./data/models_info.json', 'r', encoding='utf-8') as file:
+    models = json.load(file)
+    
+ner_models = []
+for mdl in models:
+    if mdl['model_name'].__contains__('ner') or mdl['model_name'].__contains__('Ner') or mdl['model_name'].__contains__('Ner'):
+        ner_models.append(mdl)
+        # [mdl['model_name']]= {
+        #     "task": mdl['task'],
+        #     "last_modified": mdl['last_modified'],
+        #     "downloads": int(mdl['downloads']),
+        #     "likes": mdl['likes'],
+        #     "language": mdl['language']
+        # }
+sorted_ner_models = sorted(ner_models, key=lambda x: x["downloads"], reverse=True)      
+
+with open('./data/models_ner_info.json', 'w', encoding='utf-8') as file:
+    jsondata = json.dumps(sorted_ner_models, ensure_ascii=False, indent=2)
+    file.write(jsondata)
+    
+ner_models_text = ''
+for mdl in sorted_ner_models:
+    ner_models_text += ''.join(f"{mdl['downloads']} -- {mdl['model_name']}\n")
+    
+with open('./data/models_ner_info.txt', 'w', encoding='utf-8') as file:
+    file.write(ner_models_text)
+    
+print(len(ner_models))

persian_nlp_model_sqlite.py

@@ -1,58 +1,83 @@
 #بسم الله 
+
+
+from apscheduler.schedulers.background import BackgroundScheduler
+from transformers import AutoTokenizer
+from bidi.algorithm import get_display
 from huggingface_hub import HfApi
+import matplotlib.pyplot as plt
+from fastapi import FastAPI
 from datetime import date
+import arabic_reshaper
 from fpdf import FPDF
+import threading
 import random
+import logging
 import sqlite3
 import string
+import os
 
 
 
+# تنظیم لاگ‌دهی برای دیدن خروجی زمان‌بندی
+logging.basicConfig(level=logging.INFO)
+logging.getLogger('apscheduler').setLevel(logging.INFO)
 
+
+first_id = 6600
+text = 'جمهوری موافقت‌نامه معاملات قانون بودجه اساسی قضائی بین‌المللی تأسیس منطقه‌ای لازم‌الاجراء دامپروری راه‌آهن کمیسیون‌های جدیدالاحداث مسئول فرآورده زائد اسقاط پنجساله'
 list1 = ["ID","model_id","url","downloads","private","author","tags","tag_dataset",\
         "tag_base_model","tag_license","tag_region","pipeline_tag","Likes","languages",\
-            "library","datasets","license","just_persian","deleted","date_added"]
+            "library","datasets","license","just_persian","deleted","date_added","last_modified"]
-cnt = sqlite3.connect("persian_nlp_model.db")
+cnt = sqlite3.connect(".\\db\\persian_nlp_model.db", check_same_thread=False)
 c = cnt.cursor()
 today = date.today()
 d1 = today.strftime("%d-%m-%Y")
 
 
-
+create_tables=True
-
+if create_tables == True :
+    try:
 # فقط برای اولین بار که جدول قرار است ساخته شود از این کد ها استفاده شود
-# c.execute("""CREATE TABLE PersianNlp(
+        c.execute("""CREATE TABLE PersianNlp(
-#    ID INT PRIMARY KEY     ,
+        ID INT PRIMARY KEY     ,
-#    model_id           TEXT    ,
+        model_id           TEXT    ,
-#    url            TEXT     ,
+        url            TEXT     ,
-#    downloads        INT,
+        downloads        INT,
-#    private         TEXT,
+        private         TEXT,
-#    author         TEXT,
+        author         TEXT,
-#    tags         TEXT,
+        tags         TEXT,
-#    tag_dataset         TEXT,
+        tag_dataset         TEXT,
-#    tag_base_model         TEXT,
+        tag_base_model         TEXT,
-#    tag_license         TEXT,
+        tag_license         TEXT,
-#    tag_region         TEXT,
+        tag_region         TEXT,
-#    pipeline_tag         TEXT,
+        pipeline_tag         TEXT,
-#    Likes         INT,
+        Likes         INT,
-#    languages         TEXT,
+        languages         TEXT,
-#    library         TEXT,
+        library         TEXT,
-#    datasets         TEXT,
+        datasets         TEXT,
-#    license         TEXT,
+        license         TEXT,
-#    just_persian         TEXT,
+        just_persian         TEXT,
-#    deleted         TEXT,
+        deleted         TEXT,
-#    date_added         TEXT
+        date_added         TEXT,
-# );""")
+        last_modified         TEXT
+        );""")
 
 
 
 # برای ساخت جدول میزان دانلود ها از این کد استفاده شود
-# c.execute("""CREATE TABLE downloadCountHistory(
+        c.execute("""CREATE TABLE downloadCountHistory(
-#    ID INT PRIMARY KEY     ,
+        ID INT PRIMARY KEY     ,
-#    key_id           INT    ,
+        key_id           INT    ,
-#    downloads        INT,
+        downloads        INT,
-#    date         TEXT
+        date         TEXT
-# );""")
+        );""")
+        create_tables = False
+        
+    except Exception as e:
+
+        print("--- یک خطای غیرمنتظره در ساخت تیبل رخ داد ---")
+        print(f"متن خطا: {e}")
 
 
 
@@ -89,14 +114,358 @@ def generate_random_id(length=10, chars=string.ascii_letters + string.digits):
 
 
 
+# 3. تعریف تابع کمکی برای پردازش متن فارسی
+def process_text_for_fpdf(text):
+    # مرحله 1: تغییر شکل حروف (اتصال و شکل صحیح)
+    reshaped_text = arabic_reshaper.reshape(text)
+    # مرحله 2: بازآرایی برای نمایش راست به چپ
+    bidi_text = get_display(reshaped_text)
+    return bidi_text
+
+
+
+
+def add_download_count():
+    
+    count = 1
+    api = HfApi()
+    allModel = c.execute(f'''SELECT * FROM "PersianNlp"
+WHERE deleted != 'True' ;''')
+    all_model_id = []
+    for model in allModel:
+        all_model_id.append([model[0],model[1]])
+    
+    for id_ in all_model_id:
+            # try:
+                print(count)
+                count+=1
+                id_12_digits = generate_random_id(length=12, chars=string.digits)
+                model_details = api.model_info(repo_id=id_[1])
+                c.execute(f"""INSERT INTO downloadCountHistory(ID,key_id,downloads,date)
+    VALUES ({id_12_digits},"{int(id_[0])}","{int(model_details.downloads)}","{str(d1)}");""")
+    #             c.execute(f"""INSERT INTO downloadCountHistory(ID,key_id,downloads,date)
+    # VALUES ({id_12_digits},"{int(id_[0])}","{int(model_details.downloads)+1}","22-08-2025");""")
+                cnt.commit()
+            # except:
+            #     print("Error!!")
+                
+# add_download_count()
+
+
+
+def MultiModelInfo(limit_number=10):
+    
+    
+    today = date.today()
+    date_year = today.strftime("%Y")
+    date_month = today.strftime("%m")
+    month = int(date_month)
+    year = int(date_year)
+    Models_added_this_month=[]
+    Models_deleted=[]
+    all_id_download = []
+    all_download = []
+    growth_slope_list_info = []
+    growth_slope_list = []
+    model_info = c.execute(f'''SELECT *
+FROM PersianNlp''')
+    
+    for model in model_info:
+        
+        if int(model[19].split("/")[1]) == month and int(model[19].split("/")[2]) == year :
+            Models_added_this_month.append(model[1])
+        if str(model[18]) == "True":
+            Models_deleted.append(model[1])
+            
+        all_id_download.append([model[0],model[1],model[3],model[11],model[20]])
+        
+
+
+
+    listX=[]
+    for model in all_id_download:
+        
+        downloadCountHistory = c.execute(f'''SELECT *
+FROM downloadCountHistory
+WHERE key_id = {model[0]}''')
+        
+        for models in downloadCountHistory :
+            if int(models[3].split("-")[1]) == month and int(models[3].split("-")[2]) == year :
+                model[2]=models[2]
+        listX.append(model)
+    all_id_download = listX
+
+
+    for model in all_id_download:
+        all_download.append(model[2])
+
+    all_download.sort(reverse=True)
+    maximum_download_list = all_download[0:limit_number]
+    maximum_download_info_list = []
+    n=0
+    for DCount in maximum_download_list:
+
+            for  model in all_id_download:
+                if DCount == model[2]:
+                    if n < limit_number :
+                        maximum_download_info_list.append(model)
+                        n+=1
+     
+     
+     
+# پیدا کردن بیشترین شیب دانلود ها در چند ماه :
+
+    for model in all_id_download:
+        growth_slope = []
+        DHList =c.execute(f'''SELECT *
+FROM "downloadCountHistory"
+WHERE key_id = {model[0]}''')
+        
+        for data in DHList:
+            growth_slope.append(data[2])
+        
+        growth_slopee , lenM = find_growth_slope(growth_slope) # به دست آوردن درصد رشد هر مدل
+        growth_slope_list.append(growth_slopee)
+        growth_slope_list_info.append([model[1],growth_slopee,lenM,model[2],model[3]])
+        
+
+
+    growth_slope_list.sort(reverse=True)
+    maximum_growth_slope_list = growth_slope_list[0:limit_number]
+    maximum_growth_slope_info_list = []
+    n=0
+    for DCount in maximum_growth_slope_list:
+        
+            for  model in growth_slope_list_info:
+                if DCount == model[1]:
+                    if n < limit_number :
+                        maximum_growth_slope_info_list.append(model)
+                        n+=1
+
+# پایان پیدا کردن شیب
+     
+
+
+    model_id_list = []
+    model_download_count_list = []
+    for info in maximum_download_info_list:
+
+        model_download_count_list.append(info[2])
+        model_id_list.append(str(info[1]))
+
+    listA = []
+    for x in model_download_count_list:
+        listA.append(int(x)/1000000)
+    model_download_count_list = listA
+    
+    plt.plot(model_id_list,model_download_count_list,marker='o', linestyle='-')
+    plt.xticks(rotation=30, ha='right', fontsize=10)
+    plt.xlabel("Model Name", color='blue')
+    plt.ylabel("Download Count (milion)" ,color='red')
+    plt.tight_layout()
+    plt.savefig(f'Top_{limit_number}_download_rate.png', dpi=300)
+    pdf = FPDF()
+    pdf.add_page()
+    pdf.add_font('B Nazanin', '', '.\\fonts\\B Nazanin.ttf', uni=True)
+    pdf.set_font("Arial", size=16)
+    # اضافه کردن متن
+    pdf.multi_cell(0, 10, f"Top {limit_number}  Model  Information : \n ----------------------------------------------------------------")
+    pdf.set_font("Arial", size=12)
+    
+    pdf.ln()
+    pdf.multi_cell(0, 5, f" Download Rate Chart :")
+    pdf.ln()
+    
+    # اضافه کردن عکس
+    pdf.image(f'Top_{limit_number}_download_rate.png', x=10, y=pdf.get_y() + 5, w=70)
+    pdf.ln(20) # یک خط فاصله بعد از عکس
+    # اضافه کردن جدول
+    pdf.ln(50)
+    pdf.ln()
+    pdf.multi_cell(0, 5, f" Download Rate Table :")
+    pdf.ln()
+    # سربرگ جدول
+    for header in ['Count', 'Model_name','Download-rate','Task','Last_modified']:
+        if header == 'Model_name':
+            pdf.cell(80, 10, header, 1, 0, 'C')
+        else:
+            pdf.cell(40, 10, header, 1, 0, 'C')
+    pdf.ln()
+    
+    # ردیف‌های داده
+    x=1
+    for row in maximum_download_info_list:
+        n=0
+        row[0] = x
+        x+=1
+        for item in row:
+            if n == 1 :
+                pdf.set_font("Arial", size=6)
+                pdf.cell(80, 10, f"https://huggingface.co/{str(item)}", 1, 0, 'C')
+                n+=1
+            else:
+                pdf.set_font("Arial", size=10)
+                pdf.cell(40, 10, str(item), 1, 0, 'C')
+                n+=1
+        pdf.ln()
+        
+        
+        
+        
+    pdf.add_page()
+    model_name_list = []
+    model_growth  = []
+    for info in maximum_growth_slope_info_list:
+
+        model_name_list.append(info[0])
+        model_growth.append(round(info[1], 2) )
+
+    pdf.ln()
+    pdf.multi_cell(0, 5, f" Download Growth Chart :")
+    pdf.ln()
+    plt.figure(figsize=(8, 6)) # تنظیم اندازه کلی نمودار (عرض و ارتفاع بر حسب اینچ)
+    plt.bar(model_name_list,model_growth,color='lightgreen', width=0.4)
+    plt.xticks(rotation=30, ha='right', fontsize=10)
+    plt.xlabel("Model Name", color='blue')
+    plt.ylabel("Model Growth (%)" ,color='red')
+    plt.tight_layout()
+    plt.savefig(f'Top_{limit_number}_growth_rate.png', dpi=300)
+    pdf.image(f'Top_{limit_number}_growth_rate.png', x=10, y=pdf.get_y() + 5, w=70)
+    pdf.ln(80) # یک خط فاصله بعد از عکس
+        
+    pdf.ln()
+    pdf.multi_cell(0, 5, f" Download Growth Table :")
+    pdf.ln()
+    # سربرگ جدول
+    for header in [ 'Model_name','Growth-rate','Length-month','Download-rate','Task']:
+        if header == 'Model_name':
+            pdf.cell(80, 10, header, 1, 0, 'C')
+        else:
+            pdf.cell(30, 10, header, 1, 0, 'C')
+    pdf.ln()
+    
+    # ردیف‌های داده
+    x=1
+    for row in maximum_growth_slope_info_list:
+        n=0
+        x+=1
+        for item in row:
+            if n == 0 :
+                pdf.set_font("Arial", size=6)
+                pdf.cell(80, 10, f"https://huggingface.co/{str(item)}", 1, 0, 'C')
+                n+=1
+            else:
+                pdf.set_font("Arial", size=6)
+                pdf.cell(30, 10, str(item), 1, 0, 'C')
+                n+=1
+        pdf.ln()
+        
+        
+        
+    pdf.add_page()
+    pdf.ln()
+    pdf.set_font("Arial", size=14)
+    pdf.multi_cell(0, 5, f"Models added this month :")
+    pdf.set_font("Arial", size=6)
+    pdf.ln()
+    
+    txt=''
+    n=1
+    for model_name in Models_added_this_month:
+        txt +=f"{n} -->  {model_name}\n"
+        n+=1
+    pdf.multi_cell(0, 5, f"{txt}")
+    
+    pdf.ln()
+    pdf.set_font("Arial", size=14)
+    pdf.multi_cell(0, 5, f"Models deleted :")
+    pdf.set_font("Arial", size=6)
+    pdf.ln()
+    txt=''
+    n=1
+    for model_name in Models_deleted:
+        txt +=f"{n} -->  {model_name}\n"
+        n+=1
+    pdf.multi_cell(0, 5, f"{txt}")
+    
+    pdf.output(f"MultiModelInfo_{today}.pdf")
+    os.remove(f'Top_{limit_number}_download_rate.png')
+    os.remove(f'Top_{limit_number}_growth_rate.png')
+    
+# MultiModelInfo(5)
+
+
+
+
+
+def find_growth_slope(number_list):
+    
+    try:
+            n = -1
+            index = 0
+        # if n == -1:
+            last_num = number_list[-1]
+            first_num = number_list[-1]
+
+            if number_list[-1] <= number_list[-2]:
+                for num in range(len(number_list)-1):
+                    if number_list[n-num] <= number_list[n-num-1]:
+                        last_num = number_list[n-num-1]
+                        index = n-num-1 
+                    else:
+                        break
+            
+
+            if number_list[-1] >= number_list[-2] and last_num >= first_num:
+                for num in range(len(number_list)-1):
+                    if number_list[n-num] >= number_list[n-num-1]:
+                        last_num = number_list[n-num-1]
+                        index = n-num-1 
+                    else:
+                        break
+            percentage_growth = ((first_num - last_num) / last_num) * 100
+        
+    except:
+        percentage_growth = 0
+    
+
+    return percentage_growth ,abs(index)
+
+# find_growth_slope([15,16,17,16,10,8])
+# x = find_growth_slope([1,2,3,4,5,6,7,8,10])
+# c = find_growth_slope([8,8,9,10,8,8,7])
+# v = find_growth_slope([8,8,7,5,4,6,7,7])
+# print("finish!")
+
+
+
+
+
 def persian_model_finder(nlp_tasks,idx):
     today = date.today()
     download_date = today.strftime("%d/%m/%Y") 
     idX = idx # اخرین آیدی موجود در دیتابیس را وارد میکنیم تا موارد جدید با آیدی های قبلی تداخل نکند
     api = HfApi()
-    all_persian_nlp_models_data = []
+    # all_persian_nlp_models_data = []
     seen_model_ids = set() # برای جلوگیری از اضافه شدن مدل‌های تکراری
+    new_seen_ids = set()
+    
+    
+    for task in nlp_tasks:
+        
+            models_for_task = api.list_models(
+                language="fa",
+                task=task,
+                sort="downloads",
+                direction=-1, # نزولی (از بیشترین دانلود به کمترین)
+                limit=None # می‌توانید این عدد را تغییر دهید
+            )
 
+            for model_info in models_for_task:
+                new_seen_ids.add(model_info.id)
+    
+    
+    
     print("در حال جستجو و استخراج اطلاعات مدل‌های NLP فارسی...")
 
     #  فیلتر کردن و پیمایش روی مدل‌ها
@@ -107,8 +476,12 @@ def persian_model_finder(nlp_tasks,idx):
     try:
         allModel = c.execute(f'''SELECT *
 FROM PersianNlp''')
+        
+        idX+=1        
+
         for model in allModel:
             seen_model_ids.add(model[1])
+            idX+=1
     except:
         print("database not find!")
         
@@ -129,6 +502,7 @@ FROM PersianNlp''')
                         idX+=1
                     # try : # اگر از کارت مدل توانست اطلاعات بیشتری به دست بیاورد :
                         model_ = api.model_info(model_info.id) # به دست آوردن شناسه مدل
+                        lastModified = api.model_info(repo_id=model_info.id).last_modified
                         card_data_dict = model_.card_data.to_dict() # از روی کارت مدل که شامل اطلاعات مدل میباشد یک دیکشنری میسازیم
                         model_data = {
                             "model_id": model_info.id,
@@ -150,7 +524,8 @@ FROM PersianNlp''')
                             "license":card_data_dict.get('license', 'N/A'),
                             "just_persian" : "False",
                             "deleted" : "False",
-                            "date_added" : f"{download_date}"
+                            "date_added" : f"{download_date}",
+                            "last_modified" : f"{str(lastModified.strftime("%d-%m-%Y"))}"
                             
                         }
                         
@@ -191,19 +566,32 @@ FROM PersianNlp''')
                             
                                 
                         # all_persian_nlp_models_data.append(model_data)
-                        c.execute(f"""INSERT INTO PersianNlp (ID,model_id,url,downloads,private,author,tags,tag_dataset,tag_base_model,tag_license,tag_region,pipeline_tag,Likes,languages,library,datasets,license,just_persian,deleted,date_added)  
+                        c.execute(f"""INSERT INTO PersianNlp (ID,model_id,url,downloads,private,author,tags,tag_dataset,tag_base_model,tag_license,tag_region,pipeline_tag,Likes,languages,library,datasets,license,just_persian,deleted,date_added,last_modified)  
-VALUES ({idX},"{model_data["model_id"]}","{model_data["url"]}",{model_data["downloads"]},"{model_data["private"]}","{model_data["author"]}","{model_data["tags"]}","{model_data["tag_dataset"]}","{model_data["tag_base_model"]}","{model_data["tag_license"]}","{model_data["tag_region"]}","{model_data["pipeline_tag"]}",{model_data["Likes"]},"{model_data["languages"]}","{model_data["library"]}","{model_data["datasets"]}","{model_data["license"]}","{model_data["just_persian"]}","{model_data["deleted"]}","{model_data["date_added"]}");""")
+VALUES ({idX},"{model_data["model_id"]}","{model_data["url"]}",{model_data["downloads"]},"{model_data["private"]}","{model_data["author"]}","{model_data["tags"]}","{model_data["tag_dataset"]}","{model_data["tag_base_model"]}","{model_data["tag_license"]}","{model_data["tag_region"]}","{model_data["pipeline_tag"]}",{model_data["Likes"]},"{model_data["languages"]}","{model_data["library"]}","{model_data["datasets"]}","{model_data["license"]}","{model_data["just_persian"]}","{model_data["deleted"]}","{model_data["date_added"]}","{model_data['last_modified']}");""")
                         cnt.commit()
                         seen_model_ids.add(model_info.id)
+
     
     print(f"\nتعداد کل مدل‌های NLP فارسی منحصربه‌فرد یافت شده: {len(seen_model_ids)}")
 
-#اول لیست تسک ها را میدهیم برای جست و جو ، و بعد آخرین آیدی که در تیبل مدلها در دیتابیس موجود است
+    for modelID in seen_model_ids:
-# persian_model_finder(nlp_task_list,8288)
+        if modelID not in new_seen_ids:
-
+            
-
+            c.execute(f'''UPDATE PersianNlp
+SET deleted = 'True'
+WHERE model_id = '{modelID}';''')
 
+            cnt.commit()
+            
+            
+    add_download_count()
+    MultiModelInfo()
 
+#اول لیست تسک ها را میدهیم برای جست و جو ، و بعد اولین آیدی که در تیبل مدلها در دیتابیس موجود است
+# persian_model_finder(nlp_task_list,6600)
+    
+    
+    
 def search(name,search_by):    
 
     X = "------------------------------------------------------------------------------------\n+-+-+-+-   FOUND MODEL   +-+-+-+-\n------------------------------------------------------------------------------------\n\n"
@@ -299,28 +687,165 @@ FROM PersianNlp''')
 
 
 
-def add_download_count():
+def singleModelInfo( model_id_ ,month_later = 6 , year_later = 0 ):
     
-    count = 1
-    api = HfApi()
-    allModel = c.execute(f'''SELECT *
-FROM PersianNlp''')
-    all_model_id = []
-    for model in allModel:
-        all_model_id.append([model[0],model[1]])
     
-    for id_ in all_model_id:
+    today = date.today()
-            # try:
+    date_year = today.strftime("%Y")
-                print(count)
+    date_month = today.strftime("%m")
-                count+=1
+    month = int(date_month)
-                id_12_digits = generate_random_id(length=12, chars=string.digits)
+    year = int(date_year)
-                model_details = api.model_info(repo_id=id_[1])
+    model_info = c.execute(f'''SELECT *
-                c.execute(f"""INSERT INTO downloadCountHistory(ID,key_id,downloads,date)
+FROM PersianNlp
-    VALUES ({id_12_digits},"{int(id_[0])}","{int(model_details.downloads)}","{str(d1)}");""")
+WHERE model_id = "{model_id_}"''')
-                cnt.commit()
+    
-            # except:
+    for model in model_info:
-            #     print("Error!!")
+        m = model
-                
+    model_id = m[0]
-# add_download_count()
+    last_modyfied = m[20]
+    Likes = m[12]
+    task = m[11]
+    
+    
+    downloadCountHistory = c.execute(f'''SELECT *
+FROM downloadCountHistory
+WHERE key_id = {model_id}''')
+
+    
+    n=0
+    downloads_list = []
+    download_count_list = []
+    download_date_list = []
+    for model in downloadCountHistory :
+        if int(model[3].split("-")[1]) >= month-month_later and int(model[3].split("-")[2]) >= year-year_later :
+
+            download_count_list.append(model[2])
+            download_date_list.append(model[3])
+            downloads_list.append([model[3],model[2]])
+
+    growth_slope  ,  lenM =  find_growth_slope(download_count_list)
+
+    plt.plot(download_date_list,download_count_list, marker='o', linestyle='-')
+    plt.savefig('Download_rate_chart.png', dpi=300)
+    
+    pdf = FPDF()
+    pdf.add_page()
+    pdf.add_font('B Nazanin', '', '.\\fonts\\B Nazanin.ttf', uni=True)
+    pdf.set_font("Arial", size=12)
+
+    # اضافه کردن متن
+    pdf.multi_cell(0, 10, f"Model  -<< https://huggingface.co/{model_id_} >>-  Information :")
+
+    pdf.ln()
+    pdf.multi_cell(0, 5, f"Download rate chart : ")
+    pdf.ln()
+    # اضافه کردن عکس
+    pdf.image("Download_rate_chart.png", x=10, y=pdf.get_y() + 5, w=70)
+    pdf.ln(20) # یک خط فاصله بعد از عکس
+    # pdf.cell(0, 10, " Download history chart ", ln=True, align='C')
+
+    # اضافه کردن جدول
+    pdf.ln(50)
+    
+    pdf.ln()
+    pdf.multi_cell(0, 5, f"Download rate table : ")
+    pdf.ln()
+    # سربرگ جدول
+    for header in ['Date', 'Download-rate']:
+        pdf.cell(40, 10, header, 1, 0, 'C')
+    pdf.ln()
+    pdf.set_font("Arial", size=10)
+    # ردیف‌های داده
+    for row in downloads_list:
+        for item in row:
+            pdf.cell(40, 10, str(item), 1, 0, 'C')
+        pdf.ln()
+        
+    pdf.ln()
+
+    tokenizer = AutoTokenizer.from_pretrained(model_id_)
+    tokens = tokenizer.tokenize(text)
+    print(tokens)
+    print(f'len(tokens): {len(tokens)}')
+    results = {'model': model_id_, 'len': len(tokens), 'tokens': tokens }
+    # results = str(results).encode('latin-1', 'replace').decode('latin-1')
+    # results = str(results).replace("▁","")
+    pdf.multi_cell(0, 3, f"Likes : {str(Likes)}")
+    pdf.ln()
+    pdf.multi_cell(0, 3, f"last_modyfied : {str(last_modyfied)}")
+    pdf.ln()
+    pdf.multi_cell(0, 3, f"Growth slope : {round(growth_slope, 2)} in {lenM} month .")
+    pdf.ln()
+    pdf.multi_cell(0, 7, f"task : {task}")
+    pdf.ln()
+    pdf.set_font("Arial", size=16)
+    pdf.multi_cell(0, 5, f"Tokenize info : ")
+    pdf.ln()
+    pdf.set_font("Arial", size=10)
+    pdf.multi_cell(0, 3, f"len : {str(results['len'])}")
+    pdf.ln()
+    pdf.multi_cell(0, 3, "tokenized list : ")
+    pdf.ln()
+    txt = ''
+    for token in results["tokens"]:
+        txt += f' [ {token} ] '
+    pdf.set_font("B Nazanin", size=10)
+    pdf.multi_cell(0, 5, f"{process_text_for_fpdf(txt)}",align='R')
+    pdf.output("singleModelInfo.pdf")
+    print("فایل PDF با FPDF ایجاد شد.")
+    os.remove("Download_rate_chart.png")
+    
+# singleModelInfo("amberoad/bert-multilingual-passage-reranking-msmarco")
+
+
+
+
+
+# --- بخش ادغام با FastAPI و APScheduler ---
+app = FastAPI()
+scheduler = BackgroundScheduler()
+
+# یک تابع برای دریافت آخرین ID قبل از اجرای job
+# def get_last_id():
+#     try:
+#         last_id = c.execute("SELECT MAX(ID) FROM PersianNlp").fetchone()[0]
+#         return last_id if last_id is not None else 0
+#     except sqlite3.OperationalError:
+#         return 0
+
+# تعریف کار زمان‌بندی شده: اجرای persian_model_finder در روز آخر هر ماه
+def scheduled_job():
+    idx = first_id
+    print(f"شروع اجرای کار زمان‌بندی شده. آخرین ID: {idx}")
+    persian_model_finder(nlp_task_list, idx)
+    print("کار زمان‌بندی شده با موفقیت به پایان رسید.")
+
+# scheduler.add_job(scheduled_job, 'cron', day='1', hour='0', minute='0')
+# scheduler.add_job(scheduled_job, 'interval', minutes=5)     # هر بیست دقیقه تابع رو اجرا میکنه که برای تست درست کار کردن هستش و الا کامنت بشه
+scheduler.add_job(scheduled_job, 'cron', day='last', hour='0', minute='0') # آخرین روز هرماه رو به عنوان زمان بندی قرار میده
+
+# رویداد startup: شروع زمان‌بندی هنگام روشن شدن سرور
+@app.on_event("startup")
+def startup_event():
+    print("رویداد startup: سرور در حال راه‌اندازی است و زمان‌بندی شروع می‌شود.")
+    scheduler.start()
+    print("زمان‌بندی فعال شد.")
+
+# رویداد shutdown: خاموش کردن زمان‌بندی هنگام خاموش شدن سرور
+@app.on_event("shutdown")
+def shutdown_event():
+    print("رویداد shutdown: سرور در حال خاموش شدن است و زمان‌بندی متوقف می‌شود.")
+    scheduler.shutdown()
+
+@app.get("/")
+def read_root():
+    return {"message": "FastAPI service is running and the scheduler is active."}
+
+@app.get("/run_monthly_job_now")
+def run_job_manually():
+    print("درخواست برای اجرای دستی کار ماهانه دریافت شد...")
+    # اجرای کار در یک ترد مجزا برای جلوگیری از مسدود شدن سرور
+    threading.Thread(target=scheduled_job).start()
+    return {"message": "Monthly job has been triggered manually."}