Upload 21 files

.gitattributes

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+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
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+*.pb filter=lfs diff=lfs merge=lfs -text
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+*.pkl filter=lfs diff=lfs merge=lfs -text
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+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
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+*.zip filter=lfs diff=lfs merge=lfs -text
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+custom_ner_model/ner/model filter=lfs diff=lfs merge=lfs -text

Dockerfile

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+FROM python:3.9
+
+WORKDIR /app
+
+# Copy and install Python requirements
+COPY ./requirements.txt /app/requirements.txt
+RUN pip install --no-cache-dir --upgrade -r /app/requirements.txt
+RUN python -m spacy download en_core_web_sm
+
+# Copy application files
+COPY . .
+
+
+# Add a non-root user and adjust permissions
+RUN adduser --disabled-password --gecos '' --shell /bin/bash user \
+    && chown -R user:user /app
+
+# Ensure the sudo package is installed
+RUN apt-get update && apt-get install -y sudo \
+    && echo "user ALL=(ALL) NOPASSWD:ALL" > /etc/sudoers.d/90-user \
+    && chmod 440 /etc/sudoers.d/90-user
+
+# Set up cache directory for the non-root user
+RUN mkdir /.cache && chown user:user /.cache
+
+# Switch to the non-root user
+USER user
+
+# Default command
+CMD ["python", "main.py"]

README.md

@@ -1,11 +1,11 @@
----
-title: Resume Title Predictor
-emoji: 🐨
-colorFrom: gray
-colorTo: purple
-sdk: docker
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: Resume Title Predictor
+emoji: 🏆
+colorFrom: indigo
+colorTo: purple
+sdk: docker
+pinned: false
+license: apache-2.0
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

ResumeAnalyzer.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "iibGqquzYEIG",
+    "outputId": "e56b8860-eafd-4ba6-e9cb-46080c6557f8"
+   },
+   "outputs": [],
+   "source": [
+    "%pip install pypdf\n",
+    "%pip install spacy\n",
+    "%pip install PyMuPDF\n",
+    "%pip install numpy --upgrade"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install --force-reinstall spacy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "id": "DuuOgi6hyclw"
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "numpy.dtype size changed, may indicate binary incompatibility. Expected 96 from C header, got 88 from PyObject",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[1], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01msys\u001b[39;00m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mfitz\u001b[39;00m\n\u001b[1;32m----> 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mspacy\u001b[39;00m\n",
+      "File \u001b[1;32mc:\\Users\\niram\\anaconda3\\Lib\\site-packages\\spacy\\__init__.py:6\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtyping\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Any, Dict, Iterable, Union\n\u001b[0;32m      5\u001b[0m \u001b[38;5;66;03m# set library-specific custom warning handling before doing anything else\u001b[39;00m\n\u001b[1;32m----> 6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01merrors\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m setup_default_warnings\n\u001b[0;32m      8\u001b[0m setup_default_warnings()  \u001b[38;5;66;03m# noqa: E402\u001b[39;00m\n\u001b[0;32m     10\u001b[0m \u001b[38;5;66;03m# These are imported as part of the API\u001b[39;00m\n",
+      "File \u001b[1;32mc:\\Users\\niram\\anaconda3\\Lib\\site-packages\\spacy\\errors.py:3\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mwarnings\u001b[39;00m\n\u001b[1;32m----> 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcompat\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Literal\n\u001b[0;32m      6\u001b[0m \u001b[38;5;28;01mclass\u001b[39;00m \u001b[38;5;21;01mErrorsWithCodes\u001b[39;00m(\u001b[38;5;28mtype\u001b[39m):\n\u001b[0;32m      7\u001b[0m     \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__getattribute__\u001b[39m(\u001b[38;5;28mself\u001b[39m, code):\n",
+      "File \u001b[1;32mc:\\Users\\niram\\anaconda3\\Lib\\site-packages\\spacy\\compat.py:4\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;124;03m\"\"\"Helpers for Python and platform compatibility.\"\"\"\u001b[39;00m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01msys\u001b[39;00m\n\u001b[1;32m----> 4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mthinc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutil\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m copy_array\n\u001b[0;32m      6\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m      7\u001b[0m     \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mcPickle\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpickle\u001b[39;00m\n",
+      "File \u001b[1;32mc:\\Users\\niram\\anaconda3\\Lib\\site-packages\\thinc\\__init__.py:5\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m\n\u001b[0;32m      4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mabout\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m __version__\n\u001b[1;32m----> 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mconfig\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m registry\n\u001b[0;32m      7\u001b[0m \u001b[38;5;66;03m# fmt: off\u001b[39;00m\n\u001b[0;32m      8\u001b[0m __all__ \u001b[38;5;241m=\u001b[39m [\n\u001b[0;32m      9\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mregistry\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m     10\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__version__\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m     11\u001b[0m ]\n",
+      "File \u001b[1;32mc:\\Users\\niram\\anaconda3\\Lib\\site-packages\\thinc\\config.py:5\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mconfection\u001b[39;00m\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mconfection\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m VARIABLE_RE, Config, ConfigValidationError, Promise\n\u001b[1;32m----> 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtypes\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Decorator\n\u001b[0;32m      8\u001b[0m \u001b[38;5;28;01mclass\u001b[39;00m \u001b[38;5;21;01mregistry\u001b[39;00m(confection\u001b[38;5;241m.\u001b[39mregistry):\n\u001b[0;32m      9\u001b[0m     \u001b[38;5;66;03m# fmt: off\u001b[39;00m\n\u001b[0;32m     10\u001b[0m     optimizers: Decorator \u001b[38;5;241m=\u001b[39m catalogue\u001b[38;5;241m.\u001b[39mcreate(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mthinc\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moptimizers\u001b[39m\u001b[38;5;124m\"\u001b[39m, entry_points\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
+      "File \u001b[1;32mc:\\Users\\niram\\anaconda3\\Lib\\site-packages\\thinc\\types.py:25\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtyping\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[0;32m      5\u001b[0m     Any,\n\u001b[0;32m      6\u001b[0m     Callable,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     20\u001b[0m     overload,\n\u001b[0;32m     21\u001b[0m )\n\u001b[0;32m     23\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m\n\u001b[1;32m---> 25\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcompat\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m cupy, has_cupy\n\u001b[0;32m     27\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m has_cupy:\n\u001b[0;32m     28\u001b[0m     get_array_module \u001b[38;5;241m=\u001b[39m cupy\u001b[38;5;241m.\u001b[39mget_array_module\n",
+      "File \u001b[1;32mc:\\Users\\niram\\anaconda3\\Lib\\site-packages\\thinc\\compat.py:99\u001b[0m\n\u001b[0;32m     95\u001b[0m has_mxnet \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m     98\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m---> 99\u001b[0m     \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mh5py\u001b[39;00m\n\u001b[0;32m    100\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m:  \u001b[38;5;66;03m# pragma: no cover\u001b[39;00m\n\u001b[0;32m    101\u001b[0m     h5py \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
+      "File \u001b[1;32mc:\\Users\\niram\\anaconda3\\Lib\\site-packages\\h5py\\__init__.py:45\u001b[0m\n\u001b[0;32m     36\u001b[0m     _warn((\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mh5py is running against HDF5 \u001b[39m\u001b[38;5;132;01m{0}\u001b[39;00m\u001b[38;5;124m when it was built against \u001b[39m\u001b[38;5;132;01m{1}\u001b[39;00m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m     37\u001b[0m            \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mthis may cause problems\u001b[39m\u001b[38;5;124m\"\u001b[39m)\u001b[38;5;241m.\u001b[39mformat(\n\u001b[0;32m     38\u001b[0m             \u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{0}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;132;01m{1}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;132;01m{2}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;241m*\u001b[39mversion\u001b[38;5;241m.\u001b[39mhdf5_version_tuple),\n\u001b[0;32m     39\u001b[0m             \u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{0}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;132;01m{1}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;132;01m{2}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;241m*\u001b[39mversion\u001b[38;5;241m.\u001b[39mhdf5_built_version_tuple)\n\u001b[0;32m     40\u001b[0m     ))\n\u001b[0;32m     43\u001b[0m _errors\u001b[38;5;241m.\u001b[39msilence_errors()\n\u001b[1;32m---> 45\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_conv\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m register_converters \u001b[38;5;28;01mas\u001b[39;00m _register_converters, \\\n\u001b[0;32m     46\u001b[0m                    unregister_converters \u001b[38;5;28;01mas\u001b[39;00m _unregister_converters\n\u001b[0;32m     47\u001b[0m _register_converters()\n\u001b[0;32m     48\u001b[0m atexit\u001b[38;5;241m.\u001b[39mregister(_unregister_converters)\n",
+      "File \u001b[1;32mh5py\\\\_conv.pyx:1\u001b[0m, in \u001b[0;36minit h5py._conv\u001b[1;34m()\u001b[0m\n",
+      "File \u001b[1;32mh5py\\\\h5r.pyx:1\u001b[0m, in \u001b[0;36minit h5py.h5r\u001b[1;34m()\u001b[0m\n",
+      "File \u001b[1;32mh5py\\\\h5p.pyx:1\u001b[0m, in \u001b[0;36minit h5py.h5p\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;31mValueError\u001b[0m: numpy.dtype size changed, may indicate binary incompatibility. Expected 96 from C header, got 88 from PyObject"
+     ]
+    }
+   ],
+   "source": [
+    "import sys\n",
+    "import fitz\n",
+    "import spacy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "K-Qk_E1jyUvg",
+    "outputId": "ae117ef8-99f2-44d2-be74-69a419b81596"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DIVYARAJSINH RANA \n",
+      "            +1 (732) 522 6418 | [email protected] | LinkedIn | Github | LeetCode | GeeksForGeeks \n",
+      "OBJECTIVE \n",
+      "Passionate Computer Science Master's student at NJIT seeking summer intern role as software developer. With excellent \n",
+      "problem-solving skills and two internships, I'm eager to apply my knowledge and skills in real-world. \n",
+      "EDUCATION \n",
+      "Master of Science in Computer Science,                                                                                                  01/2024 - 12/2025 (Expected) \n",
+      "New Jersey Institute of Technology                                                                                                                                     GPA: - 4.0 / 4.0 \n",
+      " \n",
+      "Bachelor of Technology in Information Technology,                                                                                               06/2019 - 05/2023 \n",
+      "Birla Vishwakarma Mahavidyalaya                                                                                                                                       GPA: - 3.5 / 4.0 \n",
+      "EXPERIENCE \n",
+      "Full Stack Developer Trainee \n",
+      "Jun 2023 - Dec 2023 \n",
+      "Capgemini \n",
+      " \n",
+      "• Gained expertise in Python and ReactJS, equipping me with skills to build scalable, interactive applications. \n",
+      "• Created a website using ReactJS to detect AI-generated text, identify hate speech, and analyze content. TextUtils serves 500+ \n",
+      "users with 85% accuracy and on average 2-second query times. \n",
+      "• Delivered an effective content analysis tool that aids organizations in moderating content, boosting user engagement, and \n",
+      "enhancing compliance. \n",
+      "Android Developer Intern \n",
+      "Aug 2021 - Dec 2021 \n",
+      "Crown Software \n",
+      "• Built a scalable backend with Java and SQL, ensuring data integrity and optimizing queries for large datasets, leading to \n",
+      "faster response times and improved app performance. \n",
+      "• Leveraged data structure concepts to optimize the performance of an Android application, resulting in 25% faster \n",
+      "response time and refactored code to improve response times and reduce memory usage. \n",
+      "PROJECTS \n",
+      "     Resume Analyzer. Analyzes resumes using Natural Language Processing to extract skills and suggest advanced skillsets based \n",
+      "on skills user possess. Allowed users to create resumes from templates by simply entering basic details. Compares resumes \n",
+      "with job descriptions, displaying match percentages with 85-90% accuracy.  \n",
+      "JustPics. Developed an interactive product recommendation application that allows users to search for attire based on visual \n",
+      "similarity using the reference image uploaded by user with 95% accuracy. \n",
+      "Covid-19 Tracker App. Developed an Android application to provide real-time statistical data on COVID-19 cases globally. \n",
+      "Fetches latest and detailed statistics for every country and their states giving comprehensive information to the user. \n",
+      "AWARDS AND ACHIEVEMENTS \n",
+      "• LeetCode: Solved over 700 problems (Top 12% of all LeetCode users). \n",
+      "• GeeksForGeeks: Currently ranked 3rd in institute. \n",
+      "• 16-week DSA course: Earned a certificate after solving enough problems and participating in contests. \n",
+      "• 3rd Place Certificate of Achievement for Resume Analyzer Project. \n",
+      "SKILLS \n",
+      "Technical skills. \n",
+      "Web Dev, Android Dev, Machine Learning, Database Management, Object-oriented Programming. \n",
+      "Programming Langs. \n",
+      "C, C++, Java, Python, SQL, JavaScript. \n",
+      "Problem Solving. \n",
+      "Data Structures, Algorithms, LeetCode, GeeksForGeeks. \n",
+      "LEADERSHIP \n",
+      "• Led the Resume Analyzer project team. Our project received third place honors in the class for its innovative approach \n",
+      "to extracting skills and matching resumes with job descriptions. \n"
+     ]
+    }
+   ],
+   "source": [
+    "with open(\"demo.txt\",\"r\") as f:\n",
+    "    resumeText = f.read()\n",
+    "print(resumeText)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "id": "mC0VxwX3r9tj"
+   },
+   "outputs": [],
+   "source": [
+    "skill_pattern = \"jz_skill_patterns.jsonl\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "0dntqz47sHOh",
+    "outputId": "b83e4534-8ff2-43d6-93b4-b9c776ee150f"
+   },
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'spacy' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[13], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m nlp2 \u001b[38;5;241m=\u001b[39m spacy\u001b[38;5;241m.\u001b[39mload(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124men_core_web_sm\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m      2\u001b[0m ruler2 \u001b[38;5;241m=\u001b[39m nlp2\u001b[38;5;241m.\u001b[39madd_pipe(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mentity_ruler\u001b[39m\u001b[38;5;124m\"\u001b[39m, before\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mner\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m      3\u001b[0m ruler2\u001b[38;5;241m.\u001b[39mfrom_disk(skill_pattern)\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'spacy' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "nlp2 = spacy.load(\"en_core_web_sm\")\n",
+    "ruler2 = nlp2.add_pipe(\"entity_ruler\", before=\"ner\")\n",
+    "ruler2.from_disk(skill_pattern)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 160,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "N97FcBrSslhc",
+    "outputId": "91383c4b-82ea-4c0b-f0a2-fecd245f36de"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'summary': {'tok2vec': {'assigns': ['doc.tensor'],\n",
+       "   'requires': [],\n",
+       "   'scores': [],\n",
+       "   'retokenizes': False},\n",
+       "  'tagger': {'assigns': ['token.tag'],\n",
+       "   'requires': [],\n",
+       "   'scores': ['tag_acc'],\n",
+       "   'retokenizes': False},\n",
+       "  'parser': {'assigns': ['token.dep',\n",
+       "    'token.head',\n",
+       "    'token.is_sent_start',\n",
+       "    'doc.sents'],\n",
+       "   'requires': [],\n",
+       "   'scores': ['dep_uas',\n",
+       "    'dep_las',\n",
+       "    'dep_las_per_type',\n",
+       "    'sents_p',\n",
+       "    'sents_r',\n",
+       "    'sents_f'],\n",
+       "   'retokenizes': False},\n",
+       "  'attribute_ruler': {'assigns': [],\n",
+       "   'requires': [],\n",
+       "   'scores': [],\n",
+       "   'retokenizes': False},\n",
+       "  'lemmatizer': {'assigns': ['token.lemma'],\n",
+       "   'requires': [],\n",
+       "   'scores': ['lemma_acc'],\n",
+       "   'retokenizes': False},\n",
+       "  'entity_ruler': {'assigns': ['doc.ents', 'token.ent_type', 'token.ent_iob'],\n",
+       "   'requires': [],\n",
+       "   'scores': ['ents_f', 'ents_p', 'ents_r', 'ents_per_type'],\n",
+       "   'retokenizes': False},\n",
+       "  'ner': {'assigns': ['doc.ents', 'token.ent_iob', 'token.ent_type'],\n",
+       "   'requires': [],\n",
+       "   'scores': ['ents_f', 'ents_p', 'ents_r', 'ents_per_type'],\n",
+       "   'retokenizes': False}},\n",
+       " 'problems': {'tok2vec': [],\n",
+       "  'tagger': [],\n",
+       "  'parser': [],\n",
+       "  'attribute_ruler': [],\n",
+       "  'lemmatizer': [],\n",
+       "  'entity_ruler': [],\n",
+       "  'ner': []},\n",
+       " 'attrs': {'token.is_sent_start': {'assigns': ['parser'], 'requires': []},\n",
+       "  'token.tag': {'assigns': ['tagger'], 'requires': []},\n",
+       "  'doc.tensor': {'assigns': ['tok2vec'], 'requires': []},\n",
+       "  'doc.ents': {'assigns': ['entity_ruler', 'ner'], 'requires': []},\n",
+       "  'token.ent_iob': {'assigns': ['entity_ruler', 'ner'], 'requires': []},\n",
+       "  'token.dep': {'assigns': ['parser'], 'requires': []},\n",
+       "  'token.head': {'assigns': ['parser'], 'requires': []},\n",
+       "  'doc.sents': {'assigns': ['parser'], 'requires': []},\n",
+       "  'token.lemma': {'assigns': ['lemmatizer'], 'requires': []},\n",
+       "  'token.ent_type': {'assigns': ['entity_ruler', 'ner'], 'requires': []}}}"
+      ]
+     },
+     "execution_count": 160,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nlp2.analyze_pipes()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 163,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "5DwqiEiCsnI8",
+    "outputId": "18fcd9f8-e132-4e52-a161-0d606a93fce1"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Github\n",
+      "Computer Science\n",
+      "software\n",
+      "Computer Science\n",
+      "Python\n",
+      "AI\n",
+      "Android\n",
+      "Software\n",
+      "Java\n",
+      "SQL\n",
+      "data integrity\n",
+      "data structure\n",
+      "Android\n",
+      "response time\n",
+      "Natural Language Processing\n",
+      "Android\n",
+      "certificate\n",
+      "Certificate\n",
+      "Android\n",
+      "Machine Learning\n",
+      "Database\n",
+      "C\n",
+      "C++\n",
+      "Java\n",
+      "Python\n",
+      "SQL\n",
+      "JavaScript\n",
+      "Data Structures\n",
+      "Algorithms\n"
+     ]
+    }
+   ],
+   "source": [
+    "doc2=nlp2(resumeText)\n",
+    "for ent in doc2.ents:\n",
+    "  if(ent.label_ == \"SKILL\"):\n",
+    "    print(ent.text)"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "base",
+   "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": "3.12.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}

best.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4449c7f0c68d28163a78e66a04eded95bedac02aef908d3c4ea60e8acd837dc7
+size 891716257

custom_ner_model/config.cfg

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+[paths]
+train = null
+dev = null
+vectors = null
+init_tok2vec = null
+
+[system]
+seed = 0
+gpu_allocator = null
+
+[nlp]
+lang = "en"
+pipeline = ["ner"]
+disabled = []
+before_creation = null
+after_creation = null
+after_pipeline_creation = null
+batch_size = 1000
+tokenizer = {"@tokenizers":"spacy.Tokenizer.v1"}
+vectors = {"@vectors":"spacy.Vectors.v1"}
+
+[components]
+
+[components.ner]
+factory = "ner"
+incorrect_spans_key = null
+moves = null
+scorer = {"@scorers":"spacy.ner_scorer.v1"}
+update_with_oracle_cut_size = 100
+
+[components.ner.model]
+@architectures = "spacy.TransitionBasedParser.v2"
+state_type = "ner"
+extra_state_tokens = false
+hidden_width = 64
+maxout_pieces = 2
+use_upper = true
+nO = null
+
+[components.ner.model.tok2vec]
+@architectures = "spacy.HashEmbedCNN.v2"
+pretrained_vectors = null
+width = 96
+depth = 4
+embed_size = 2000
+window_size = 1
+maxout_pieces = 3
+subword_features = true
+
+[corpora]
+
+[corpora.dev]
+@readers = "spacy.Corpus.v1"
+path = ${paths.dev}
+gold_preproc = false
+max_length = 0
+limit = 0
+augmenter = null
+
+[corpora.train]
+@readers = "spacy.Corpus.v1"
+path = ${paths.train}
+gold_preproc = false
+max_length = 0
+limit = 0
+augmenter = null
+
+[training]
+seed = ${system.seed}
+gpu_allocator = ${system.gpu_allocator}
+dropout = 0.1
+accumulate_gradient = 1
+patience = 1600
+max_epochs = 0
+max_steps = 20000
+eval_frequency = 200
+frozen_components = []
+annotating_components = []
+dev_corpus = "corpora.dev"
+train_corpus = "corpora.train"
+before_to_disk = null
+before_update = null
+
+[training.batcher]
+@batchers = "spacy.batch_by_words.v1"
+discard_oversize = false
+tolerance = 0.2
+get_length = null
+
+[training.batcher.size]
+@schedules = "compounding.v1"
+start = 100
+stop = 1000
+compound = 1.001
+t = 0.0
+
+[training.logger]
+@loggers = "spacy.ConsoleLogger.v1"
+progress_bar = false
+
+[training.optimizer]
+@optimizers = "Adam.v1"
+beta1 = 0.9
+beta2 = 0.999
+L2_is_weight_decay = true
+L2 = 0.01
+grad_clip = 1.0
+use_averages = false
+eps = 0.00000001
+learn_rate = 0.001
+
+[training.score_weights]
+ents_f = 1.0
+ents_p = 0.0
+ents_r = 0.0
+ents_per_type = null
+
+[pretraining]
+
+[initialize]
+vectors = ${paths.vectors}
+init_tok2vec = ${paths.init_tok2vec}
+vocab_data = null
+lookups = null
+before_init = null
+after_init = null
+
+[initialize.components]
+
+[initialize.tokenizer]

custom_ner_model/meta.json

@@ -0,0 +1,37 @@
+{
+  "lang":"en",
+  "name":"pipeline",
+  "version":"0.0.0",
+  "spacy_version":">=3.8.2,<3.9.0",
+  "description":"",
+  "author":"",
+  "email":"",
+  "url":"",
+  "license":"",
+  "spacy_git_version":"63f1b53",
+  "vectors":{
+    "width":0,
+    "vectors":0,
+    "keys":0,
+    "name":null,
+    "mode":"default"
+  },
+  "labels":{
+    "ner":[
+      "AREA",
+      "INDUSTRY",
+      "LOCATION",
+      "ROLE",
+      "SKILL"
+    ]
+  },
+  "pipeline":[
+    "ner"
+  ],
+  "components":[
+    "ner"
+  ],
+  "disabled":[
+
+  ]
+}

custom_ner_model/ner/cfg

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+{
+  "moves":null,
+  "update_with_oracle_cut_size":100,
+  "multitasks":[
+
+  ],
+  "min_action_freq":1,
+  "learn_tokens":false,
+  "beam_width":1,
+  "beam_density":0.0,
+  "beam_update_prob":0.0,
+  "incorrect_spans_key":null
+}

custom_ner_model/ner/model

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+oid sha256:0c8340c548ae75ef0de971a146e75fdade795f810ba067416618410b9f18d796
+size 3837080

custom_ner_model/ner/moves

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custom_ner_model/tokenizer

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custom_ner_model/vocab/key2row

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+�

custom_ner_model/vocab/lookups.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:76be8b528d0075f7aae98d6fa57a6d3c83ae480a8469e668d7b0af968995ac71
+size 1

custom_ner_model/vocab/vectors.cfg

@@ -0,0 +1,3 @@
+{
+  "mode":"default"
+}

main.py

@@ -0,0 +1,125 @@
+from flask import Flask, render_template, request, jsonify
+from flask_cors import CORS
+import fitz  # PyMuPDF for PDF text extraction
+import spacy
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+import torch
+import os
+
+app = Flask(__name__)
+CORS(app)
+
+# ===== Load Custom NER Model =====
+try:
+    nlp = spacy.load("custom_ner_model")  # Load your custom-trained NER model
+    print("Custom NER model loaded successfully.")
+except Exception as e:
+    print(f"Error loading custom NER model: {e}")
+    exit()
+
+# ===== Load T5 Model for Job Title Prediction =====
+tokenizer = T5Tokenizer.from_pretrained("t5-small")
+model = T5ForConditionalGeneration.from_pretrained("t5-small")
+
+# Load model weights
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+model.load_state_dict(torch.load("best.pth", map_location=device))
+model.eval()
+model.to(device)
+
+print("T5 model for job title prediction loaded successfully.")
+
+# ===== Helper Functions =====
+
+# Extract text from PDF
+def extract_text_from_pdf(pdf_file):
+    doc = fitz.open(stream=pdf_file.read(), filetype="pdf")
+    text = ""
+    for page in doc:
+        text += page.get_text()
+    return text
+
+# Extract entities using Custom NER
+def extract_entities(text):
+    text=text.replace("\\n","\n")
+    doc = nlp(text)  # Process text with custom NER
+    extracted_data = {}
+    
+    for ent in doc.ents:
+        # Use only relevant labels
+        if ent.label_ in ["SKILL", "ROLE", "LOCATION", "AREA", "INDUSTRY"]:
+            if ent.label_ not in extracted_data:
+                extracted_data[ent.label_] = []
+            if ent.text not in extracted_data[ent.label_]:
+                extracted_data[ent.label_].append(ent.text)
+    
+    # Format results as comma-separated strings
+    for key in extracted_data:
+        extracted_data[key] = ", ".join(extracted_data[key])
+    return extracted_data
+
+# Predict job title using T5 model
+def predict_job_title(skills, area,roles,location,industry):
+    input_text = f"Skills: {skills}; \nRole: {roles}; \nLocation: {location}; \nArea: {area}; \nIndustry: {industry}"
+    inputs = tokenizer(input_text, return_tensors="pt", padding=True, truncation=True).to(device)
+    
+    with torch.no_grad():
+        outputs = model.generate(inputs["input_ids"], max_length=50, num_beams=4, early_stopping=True)
+    
+    predicted_job_title = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return predicted_job_title
+
+# ===== Flask Routes =====
+
+@app.route('/')
+def home():
+    return render_template('index.html')  # Default home page
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    if 'resume' not in request.files:
+        return jsonify({'error': 'No file uploaded'}), 400
+    
+    file = request.files['resume']
+    if file.filename == '':
+        return jsonify({'error': 'No file selected'}), 400
+    
+    if not file.filename.endswith('.pdf'):
+        return jsonify({'error': 'Please upload a PDF file'}), 400
+
+    try:
+        # Step 1: Extract text from PDF
+        resume_text = extract_text_from_pdf(file)
+        
+        # Step 2: Extract entities using Custom NER
+        extracted_data = extract_entities(resume_text)
+        
+        # Step 3: Prepare input for T5 prediction
+        skills = extracted_data.get("SKILL", "")
+        area = extracted_data.get("AREA", "")
+        roles = extracted_data.get("ROLE", "")
+        location = extracted_data.get("LOCATION", "")
+        industry = extracted_data.get("INDUSTRY", "")
+        # Step 4: Predict job title
+        predicted_title = predict_job_title(skills, area,roles,location,industry)
+        
+        # Step 5: Return response
+        response = {
+            'success': True,
+            'predicted_title': predicted_title.split(";")[0],
+            'extracted_skills': extracted_data.get("SKILL", ""),
+            'roles': extracted_data.get("ROLE", ""),
+            'locations': extracted_data.get("LOCATION", ""),
+            'area': extracted_data.get("AREA", ""),
+            'industry': extracted_data.get("INDUSTRY", "")
+        }
+        
+        return jsonify(response)
+    
+    except Exception as e:
+        return jsonify({'error': str(e)}), 500
+
+if __name__ == '__main__':
+    from waitress import serve
+    print("Starting Flask app...")
+    serve(app, host="0.0.0.0", port=7860)

requirements.txt

@@ -0,0 +1,8 @@
+waitress
+flask
+flask-cors
+torch
+transformers
+spacy
+PyMuPDF
+sentencepiece

templates/index.html

@@ -0,0 +1,185 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>AI Resume Analyzer</title>
+    <script src="https://cdn.tailwindcss.com"></script>
+    <link href="https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700&display=swap" rel="stylesheet">
+    <script src="https://unpkg.com/@lottiefiles/lottie-player@latest/dist/lottie-player.js"></script>
+    <style>
+        body {
+            font-family: 'Inter', sans-serif;
+        }
+    </style>
+</head>
+<body class="bg-gray-50">
+    <div class="min-h-screen">
+        <!-- Navbar -->
+        <nav class="bg-white shadow-sm">
+            <div class="max-w-7xl mx-auto px-4 sm:px-6 lg:px-8">
+                <div class="flex justify-between h-16 items-center">
+                    <div class="flex items-center">
+                        <svg class="h-8 w-8 text-indigo-600" fill="none" viewBox="0 0 24 24" stroke="currentColor">
+                            <path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M9 12h6m-6 4h6m2 5H7a2 2 0 01-2-2V5a2 2 0 012-2h5.586a1 1 0 01.707.293l5.414 5.414a1 1 0 01.293.707V19a2 2 0 01-2 2z" />
+                        </svg>
+                        <span class="ml-2 text-xl font-semibold text-gray-900">AI Resume Analyzer</span>
+                    </div>
+                </div>
+            </div>
+        </nav>
+
+        <!-- Main Content -->
+        <main class="max-w-7xl mx-auto py-12 sm:px-6 lg:px-8">
+            <div class="max-w-3xl mx-auto">
+                <!-- Hero Section -->
+                <div class="text-center mb-12">
+                    <h1 class="text-4xl font-bold text-gray-900 mb-4">
+                        Predict Your Ideal Job Title
+                    </h1>
+                    <p class="text-lg text-gray-600">
+                        Upload your resume and let AI analyze your skills and experience
+                    </p>
+                </div>
+
+                <!-- Upload Card -->
+                <div class="bg-white rounded-2xl shadow-xl p-8 mb-8">
+                    <form id="uploadForm" class="space-y-6">
+                        <div class="flex flex-col items-center justify-center border-2 border-dashed border-gray-300 rounded-lg p-6 hover:border-indigo-500 transition-colors">
+                            <div class="space-y-2 text-center">
+                                <svg class="mx-auto h-12 w-12 text-gray-400" stroke="currentColor" fill="none" viewBox="0 0 48 48">
+                                    <path d="M28 8H12a4 4 0 00-4 4v20m32-12v8m0 0v8a4 4 0 01-4 4H12a4 4 0 01-4-4v-4m32-4l-3.172-3.172a4 4 0 00-5.656 0L28 28M8 32l9.172-9.172a4 4 0 015.656 0L28 28m0 0l4 4m4-24h8m-4-4v8m-12 4h.02" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" />
+                                </svg>
+                                <div class="text-sm text-gray-600">
+                                    <label for="resume" class="relative cursor-pointer rounded-md font-medium text-indigo-600 hover:text-indigo-500 focus-within:outline-none focus-within:ring-2 focus-within:ring-offset-2 focus-within:ring-indigo-500">
+                                        <span>Upload a file</span>
+                                        <input id="resume" name="resume" type="file" class="sr-only" accept=".pdf">
+                                    </label>
+                                    <p class="pl-1">or drag and drop</p>
+                                </div>
+                                <p class="text-xs text-gray-500">PDF up to 10MB</p>
+                            </div>
+                        </div>
+                        <div class="text-center">
+                            <button type="submit" class="inline-flex items-center px-6 py-3 border border-transparent text-base font-medium rounded-full shadow-sm text-white bg-indigo-600 hover:bg-indigo-700 focus:outline-none focus:ring-2 focus:ring-offset-2 focus:ring-indigo-500">
+                                Analyze Resume
+                            </button>
+                        </div>
+                    </form>
+
+                    <!-- Loading State -->
+                    <div id="loading" class="hidden text-center py-8">
+                        <lottie-player src="https://assets2.lottiefiles.com/packages/lf20_poqmycwy.json" background="transparent" speed="1" style="width: 200px; height: 200px; margin: 0 auto;" loop autoplay></lottie-player>
+                        <p class="mt-4 text-gray-600">Analyzing your resume...</p>
+                    </div>
+
+                    <!-- Results Section -->
+                    <div id="result" class="hidden mt-8">
+                        <div class="space-y-6">
+                            <!-- Predicted Title -->
+                            <div class="bg-gradient-to-r from-indigo-50 to-blue-50 rounded-xl p-6">
+                                <h3 class="text-lg font-medium text-gray-900 mb-2">Predicted Job Title</h3>
+                                <p id="predictedTitle" class="text-2xl font-bold text-indigo-600"></p>
+                            </div>
+
+                            <!-- Skills -->
+                            <div class="bg-white rounded-xl border border-gray-200 p-6">
+                                <h3 class="text-lg font-medium text-gray-900 mb-4">Extracted Skills</h3>
+                                <div id="extractedSkills" class="flex flex-wrap gap-2">
+                                    <!-- Skills will be inserted here as tags -->
+                                </div>
+                            </div>
+
+                            <!-- Organization -->
+                            <div class="bg-white rounded-xl border border-gray-200 p-6">
+                                <h3 class="text-lg font-medium text-gray-900 mb-2">Functional Area</h3>
+                                <p id="organization" class="text-gray-700"></p>
+                            </div>
+                        </div>
+                    </div>
+                </div>
+            </div>
+        </main>
+    </div>
+
+    <script src="https://code.jquery.com/jquery-3.6.0.min.js"></script>
+    <script>
+        $(document).ready(function() {
+            // File input visual feedback
+            $('#resume').on('change', function(e) {
+                const fileName = e.target.files[0]?.name;
+                if (fileName) {
+                    $(this).closest('.border-dashed').find('p').text(fileName);
+                }
+            });
+            // Drag and drop functionality
+            const dropZone = $('.border-dashed');
+            
+            dropZone.on('dragover', function(e) {
+                e.preventDefault();
+                $(this).addClass('border-indigo-500');
+            });
+            dropZone.on('dragleave', function(e) {
+                e.preventDefault();
+                $(this).removeClass('border-indigo-500');
+            });
+            dropZone.on('drop', function(e) {
+                e.preventDefault();
+                $(this).removeClass('border-indigo-500');
+                const files = e.originalEvent.dataTransfer.files;
+                $('#resume')[0].files = files;
+                if (files[0]) {
+                    $(this).find('p').text(files[0].name);
+                }
+            });
+            // Form submission
+            $('#uploadForm').on('submit', function(e) {
+                e.preventDefault();
+                
+                const formData = new FormData();
+                const fileInput = $('#resume')[0];
+                
+                if (fileInput.files.length === 0) {
+                    alert('Please select a file');
+                    return;
+                }
+                
+                formData.append('resume', fileInput.files[0]);
+                
+                $('#loading').removeClass('hidden');
+                $('#result').addClass('hidden');
+                
+                $.ajax({
+                    url: '/predict',
+                    type: 'POST',
+                    data: formData,
+                    processData: false,
+                    contentType: false,
+                    success: function(response) {
+                        $('#loading').addClass('hidden');
+                        $('#result').removeClass('hidden');
+                        
+                        // Update results
+                        $('#predictedTitle').text(response.predicted_title);
+                        
+                        // Convert skills string to tags
+                        const skills = response.extracted_skills.split(', ');
+                        const skillsHtml = skills.map(skill => 
+                            `<span class="inline-flex items-center px-3 py-1 rounded-full text-sm font-medium bg-indigo-100 text-indigo-800">
+                                ${skill}
+                            </span>`
+                        ).join('');
+                        $('#extractedSkills').html(skillsHtml);
+                        
+                        $('#organization').html(response.area.replaceAll(", ","<br/>"));
+                    },
+                    error: function(xhr) {
+                        $('#loading').addClass('hidden');
+                        alert('Error: ' + (xhr.responseJSON?.error || 'Something went wrong'));
+                    }
+                });
+            });
+        });
+    </script>
+</body>
+</html>