small fix

app.py

@@ -15,6 +15,7 @@ dotenv.load_dotenv()
 # Constants
 DEFAULT_TEMPLATE_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), "templates", "Arbab - Resume.tex")
 OUTPUT_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "outputs")
+DEFAULT_JOBS_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), "jobs-applied-for.txt")
 
 # Ensure output directory exists
 os.makedirs(OUTPUT_DIR, exist_ok=True)
@@ -80,6 +81,7 @@ Basically we are preparing this resume for the person who will be hiring. So the
 If the role is more applied and industry focused and does not mention about publications, you can skip publications and instead focus in why hire me section that i have multiple industry internships And most of my PhD projects have applied applications and give short and concise and crucial justification for that (you can remove the publications section).  But if the role is more research focused you can focus on the research publication and mention that I have published in NeurIPS and WACV and keep the final publication section as well . 
 Importantly, You can completely get rid of few things which are not directly related. For example, you can completely get rid of your research papers or a few bullet points completely but if you are writing something then write that completely. You can skip about 20% of the things in the resume. Be strategic about where to reduce text from. Do not just text a little little from all the places. Instead, be strategic and cut brutally from some places which are not contributing as much to the final resume for this job. 
 FORMAT YOUR RESPONSE AS CLEAN LATEX CODE WITH NO EXPLANATIONS OR CODE BLOCKS.
+Keep all the projects in the projects section, do not remove any of them.
 """
     
     try:
@@ -110,6 +112,64 @@ FORMAT YOUR RESPONSE AS CLEAN LATEX CODE WITH NO EXPLANATIONS OR CODE BLOCKS.
         # Return error message instead of original content
         raise ValueError(f"Error occurred: {str(e)}")
 
+def find_relevant_jobs(api_key, job_description, jobs_list=None):
+    """Find the most relevant jobs from a list based on the job description."""
+    if not api_key.strip():
+        return "Please provide an OpenAI API key"
+    
+    try:
+        client = get_openai_client(api_key)
+        
+        # Use default jobs list if none provided
+        if not jobs_list or jobs_list.strip() == "":
+            try:
+                with open(DEFAULT_JOBS_PATH, "r") as f:
+                    jobs_list = f.read()
+            except Exception as e:
+                return f"Error reading default jobs list: {str(e)}"
+        
+        # LinkedIn jobs link to include in the search
+        linkedin_jobs_link = "https://www.linkedin.com/jobs/search/?currentJobId=4212868961&f_E=2%2C3%2C4&f_TPR=r86400&geoId=102095887&keywords=(machine%20learning)%20OR%20(software%20engineer)&origin=JOB_SEARCH_PAGE_JOB_FILTER&refresh=true&sortBy=R&spellCorrectionEnabled=true"
+        
+        # Create the prompt for OpenAI
+        prompt = f"""
+Given the following job description and a list of job positions, identify the top 5 most relevant jobs from the list.
+
+Job Description:
+{job_description}
+
+Available Jobs List:
+{jobs_list}
+
+Additionally, consider checking recent job postings from LinkedIn:
+{linkedin_jobs_link}
+
+Return only the top 5 most relevant jobs in this format:
+1. [Company Name] - [Position Title] (Relevance Score: X/10)
+2. [Company Name] - [Position Title] (Relevance Score: X/10)
+3. [Company Name] - [Position Title] (Relevance Score: X/10)
+4. [Company Name] - [Position Title] (Relevance Score: X/10)
+5. [Company Name] - [Position Title] (Relevance Score: X/10)
+
+For each job, briefly explain in 1-2 sentences why it's relevant to the job description.
+"""
+        
+        response = client.chat.completions.create(
+            model="gpt-4.1",
+            messages=[
+                {"role": "system", "content": "You are a job matching assistant that identifies the most relevant jobs based on a job description."},
+                {"role": "user", "content": prompt}
+            ],
+            temperature=0.3,
+            max_tokens=1000
+        )
+        
+        # Return the response
+        return response.choices[0].message.content.strip()
+    
+    except Exception as e:
+        return f"Error finding relevant jobs: {str(e)}"
+
 def create_customized_resume(api_key, job_description):
     """Create a customized resume based on the job description."""
     if not api_key.strip():
@@ -166,43 +226,93 @@ def convert_to_pdf(tex_path):
 # Define the Gradio interface
 def create_interface():
     with gr.Blocks(title="Resume Customizer") as app:
-        gr.Markdown("# Resume Customizer")
-        gr.Markdown("Enter a job description to generate a fully customized resume tailored to the position.")
-        
-        with gr.Row():
-            with gr.Column():
-                api_key = gr.Textbox(
-                    label="OpenAI API Key",
-                    placeholder="Enter your OpenAI API key",
-                    type="password"
-                )
+        with gr.Tabs():
+            with gr.Tab("Resume Customizer"):
+                gr.Markdown("# Resume Customizer")
+                gr.Markdown("Enter a job description to generate a fully customized resume tailored to the position.")
                 
-                job_description = gr.Textbox(
-                    label="Job Description",
-                    placeholder="Paste the job description here...",
-                    lines=10
-                )
+                with gr.Row():
+                    with gr.Column():
+                        api_key_resume = gr.Textbox(
+                            label="OpenAI API Key",
+                            placeholder="Enter your OpenAI API key",
+                            type="password"
+                        )
+                        
+                        job_description_resume = gr.Textbox(
+                            label="Job Description",
+                            placeholder="Paste the job description here...",
+                            lines=10
+                        )
+                        
+                        customize_btn = gr.Button("Customize Resume")
+                    
+                    with gr.Column():
+                        pdf_output = gr.File(label="Download Resume")
+                        status_text = gr.Textbox(label="Status", interactive=False)
                 
-                customize_btn = gr.Button("Customize Resume")
+                customize_btn.click(
+                    fn=create_customized_resume,
+                    inputs=[api_key_resume, job_description_resume],
+                    outputs=[pdf_output, status_text]
+                )
+
+                gr.Markdown("""
+                ## How to Use
+                1. Enter your OpenAI API key
+                2. Paste a job description in the text area
+                3. Click "Customize Resume"
+                4. Wait for the AI to tailor your entire resume to match the job requirements
+                5. Download the customized resume PDF
+                """)
             
-            with gr.Column():
-                pdf_output = gr.File(label="Download Resume")
-                status_text = gr.Textbox(label="Status", interactive=False)
-        
-        customize_btn.click(
-            fn=create_customized_resume,
-            inputs=[api_key, job_description],
-            outputs=[pdf_output, status_text]
-        )
+            with gr.Tab("Job Finder"):
+                gr.Markdown("# Job Finder")
+                gr.Markdown("Enter a job description to find the top 5 most relevant jobs from your list and LinkedIn recommendations.")
+                
+                with gr.Row():
+                    with gr.Column():
+                        api_key_job = gr.Textbox(
+                            label="OpenAI API Key",
+                            placeholder="Enter your OpenAI API key",
+                            type="password"
+                        )
+                        
+                        job_description_job = gr.Textbox(
+                            label="Job Description",
+                            placeholder="Paste the job description here...",
+                            lines=10
+                        )
+                        
+                        jobs_list = gr.Textbox(
+                            label="Jobs List (Optional)",
+                            placeholder="Paste your list of jobs here or leave empty to use the default list...",
+                            lines=10
+                        )
+                        
+                        find_jobs_btn = gr.Button("Find Relevant Jobs")
+                    
+                    with gr.Column():
+                        relevant_jobs_output = gr.Textbox(
+                            label="Top 5 Most Relevant Jobs",
+                            interactive=False,
+                            lines=15
+                        )
+                
+                find_jobs_btn.click(
+                    fn=find_relevant_jobs,
+                    inputs=[api_key_job, job_description_job, jobs_list],
+                    outputs=relevant_jobs_output
+                )
 
-        gr.Markdown("""
-        ## How to Use
-        1. Enter your OpenAI API key
-        2. Paste a job description in the text area
-        3. Click "Customize Resume"
-        4. Wait for the AI to tailor your entire resume to match the job requirements
-        5. Download the customized resume PDF
-        """)
+                gr.Markdown("""
+                ## How to Use
+                1. Enter your OpenAI API key
+                2. Paste a job description in the text area
+                3. Optionally, paste your own list of jobs (or leave empty to use the default list)
+                4. Click "Find Relevant Jobs"
+                5. View the top 5 most relevant jobs based on the job description (includes LinkedIn recommendations)
+                """)
     
     return app
 

jobs-applied-for.txt

@@ -0,0 +1,164 @@
+COMPANY NAME	POSITION TITLE
+Chalk	Machine Learning Engineer 
+Adobe	Software Development Engineer 
+Qualtrics	Machine Learning Engineer
+Salesforce	Software Engineer
+Laserfiche	Software Engineer I/II
+Quanterix	Software Engineer I 
+ServiceNow	Machine Learning Engineer 
+Red Hat	Machine Learning Engineer 
+Solventum	Software Developer
+Realtor.com	Machine Learning Engineer, Search 
+RoviSys	Software Engineers 
+	
+Anthropic	Research Engineer / Scientist, Alignment Science
+Applied Intuition	Research Scientist 
+Intuit	Machine Learning Engineer 2 
+Together AI	AI Researcher, Core ML
+Spotify	Research Scientist - Content Platform 
+Gradient	Applied AI Engineer
+Biostate AI	AI Research Scientist
+Instacart	Machine Learning Engineer II - Ads Response Prediction 
+Upstart	Machine Learning Engineer 
+xAI	Machine Learning Engineer - Frontier Data
+Epic	Software Engineer 
+C1	AI Developer - C1 Gov 
+PwC	AI & GenAI Data Scientist-Senior Associate 
+Otter.ai	Research Scientist 
+Snowflake	AI System Research and Development Engineer - Frameworks 
+Amazon Web Services (AWS)	Applied Scientist, ML_AI 
+Amazon	Applied Scientist (GenAI/LLM), Sandstone 
+NVIDIA	Research Scientist, Generalist Embodied Agent Research - New College Grad 2025 
+NVIDIA	System Software Engineer - GPU 
+Snap Inc.	Machine Learning Engineer, Generative AI 
+Chan Zuckerberg Biohub Network	Research Scientist, Machine Learning/A.I. (Biohub NY)
+Waymo	Machine Learning Engineer, Training 
+LatentView Analytict	MLOps Engineer 
+Google	Software Engineer, PhD, Early Career, Campus, 2025 Start 
+Caltrans	Research Data Specialist II (JC-474758) 
+Capital One	Senior Data Scientist - NLP 
+Amazon	Machine Learning Engineering (New Graduate), AGI Autonomy 
+X, The Moonshot Factory	Applied AI Research Engineer, Early Stage Project 
+Hasura	Applied AI Engineer 
+-	-
+-	-
+	
+Anthropic	Research Engineer, Agents
+Oracle	Research Scientist, AI & Machine Learning (PhD) 
+Together AI	LLM Inference Frameworks and Optimization Engineer
+Radiant Security	AI Applied Engineer
+Adobe	Machine Learning Engineer 
+Tesla	Machine Learning Engineer, Intelligent Scheduling Systems 
+Artisan	AI/ML Engineer
+XPENG	Senior Machine Learning Performance Engineer
+Accenture	Advanced AI Research Engineer 
+Perplexity	AI Software Engineer - Data Platform - SF 
+Ramp	Software Engineer | Applied AI 
+Latent	Machine Learning Engineer
+Anthropic	Software Engineer, Human Feedback Interface
+DoorDash	Software Engineer, Backend (All Teams) 
+EA SPORTS	Machine Learning Scientist 
+Anthropic	Research Engineer / Scientist, AI Scientist
+Atlas	Applied AI Engineer
+Allstate	Machine Learning Engineer 
+Clio - Cloud-Based Legal Technology	Machine Learning Engineer 
+Athena	CA - Machine Learning/AI Engineer Intern
+NVIDIA	Deep Learning Algorithm Engineer - New College Grad 2025
+Arc	Applied AI Engineer
+Anthropic	Research Engineer, Tokens ML Infra
+Together AI	Machine Learning Engineer
+OpenAI	Software Engineer, Discovery Engineering 
+Abridge	Machine Learning Scientist, NLP (All Levels) 
+CentML	Software Engineer - LLM Training 
+Snap Inc.	Machine Learning Engineer, Level 5 
+Blue River Technology	CVML Engineer - Modeling, See & Spray 
+National Debt Relief, LLC	Applied AI Engineer 
+Amazon	SDE II (Machine Learning), AGI Foundations 
+Nuro	Machine Learning Research Scientist: Generative Models for Behavior Modeling 
+DoorDash	Machine Learning Engineer - Conversation AI 
+Amazon	"Software Development Engineer, Millibyte, Millibyte 
+"
+Uniphore	AI Scientist 
+StubHub	Data Scientist II 
+Calm	Software Engineer, Web 
+SpaceX	"Full Stack Software Engineer (Components)
+"
+DoorDash	Machine Learning Engineer - New Verticals - Search & Recommendations 
+Amazon	software Development Engineer, Kuiper 
+Fionics	$300k-$1mm Low Latency Software Engineer (C++/Java)
+NVIDIA	Research Scientist, Design Automation - New College Grad 2025 
+Google DeepMind	"Analytics Engineer 
+"
+Teledyne Technologies Incorporated	Research Scientist 
+Five9	Analytics and Reporting Engineer 
+Amazon	Software Development Engineer , ART19 
+Capital Group	Senior Software Engineer 
+Norstella	NLP LLM Operations Architect & AWS Engineer 
+Exponent	Research Assistant 
+LegalZoom	Senior DevOps Engineer 
+Motion (Creative Analytics)	Senior Product Engineer (Full Stack) 
+Snap Inc.	Software Engineer, C++, 2+ Years Of Experience 
+Artisan	AI/ML Engineer
+Snap Inc.	Software Engineer, Level 3 
+Bluebeam	Software Engineer (Go/AWS) 
+Yahoo	Senior Software Engineer - Core Mail
+Eluvio	Artificial Intelligence Research Scientist (Gen AI - Multimodal Learning)
+xAI	Research Engineer - World Model
+Amazon	Software Development Engineer , ABServ 
+Amazon Music	Software Development Engineer, Amazon Music 
+Attis	Machine Learning Engineer (Weather / Climate / AI)
+Amazon	Software Development Engineer - Amazon Publisher Services, Amazon Publisher Services, 3P Demand 
+Acubed	Machine Learning Engineer 
+UnitedMasters	Software Engineer, Backend 
+Roblow	Software Engineer, Productivity Platforms and Ecosystems
+Amazon	Software Development Engineer, Backbone SDN Controllers
+Amazon	Sr Applied Scientist, Brand Intelligence
+Corteva Agriscience	Machine Learning Scientist 
+X, The Moonshot Factory	Connect with Tapestry 
+NVIDIA	Deep Learning Algorithm Engineer - New College Grad 2025
+	
+TikTok	Backend Software Engineer, TikTok Live Strategy Platform 
+Google	Software Engineer, PhD, Early Career, Campus, 2025 Start 
+AMD	Platform Engineer - AI Software Solutions 
+AMD	Software Engineering Intern/Co-Op (Graduate | Fall 2025 | Hybrid)
+Apple	AIML - ML Engineer
+Apple	Software Engineer, Calls Security
+ByteDance	"High-Performance Computing Research Scientist (Inference Optimization) - Doubao (Seed) Vision AI Platform - San Jose 
+"
+Waymo	"ML Engineer, Foundation Model Evaluation 
+"
+Waymo	Software Engineer, ML Infrastructure, Limited Duration 
+IBM	Software Developer 
+Oracle	Software Developer 4 
+	
+Amazon	-
+-	-
+Amazon	Applied Scientist, SPX AI Lab
+Amazon	Applied Scientist,
+Amazon	Applied Scientist II, Selling Partner Growth
+Amazon	Applied Scientist, Account Integrity
+Amazon	Applied Scientist, Seller Fees Science & Tech
+Snorkel AI	Research Engineer 
+PwC	GenAI Python Systems Engineer – Senior Associate 
+Google DeepMind	Forward Deployment Engineer 
+OpenAI	Software Engineer, GPU Infrastructure 
+NVIDIA	Performance Engineer - Deep Learning 
+Perplexity	AI Inference Engineer - SF or Palo Alto 
+Scale AI	AI Infrastructure Engineer, ML Data Platform 
+Bright Machines	"Research Perception Scientist, Computer Vision and Machine Learning 
+"
+Amazon Web Services (AWS)	Applied Scientist II 
+DoorDash	Machine Learning Engineer, Computer Vision 
+Amazon	SDE II - Perception & Planning, Last Mile Delivery 
+Tempus AI	Machine Learning Scientist, Digital Pathology 
+Circle	AI Applied Scientist 
+Amazon Web Services (AWS)	Applied Scientist II, NGDE Science 
+Jerry	Data Scientist 
+Tesla	Machine Learning Kernel Performance Engineer, Dojo 
+Together AI	LLM Training Frameworks and Optimization Engineer
+Circle	AI Applied Scientist 
+NVIDIA	Research Scientist, Generalist Embodied Agent Research - New College Grad 2025 
+Shopify	Machine Learning Infrastructure Engineers (Global) 
+OPPO	2025 Computer Vision Engineer
+StubHub	Software Engineer II – Operations 
+DoorDash	Machine Learning Engineer - New Verticals - Search & Recommendations 

templates/Arbab - Resume.tex

@@ -122,7 +122,7 @@
       Fine-tuned Llama 3.2 11B vision model using PyTorch TorchTune, improving computer navigation performance from 40\% to 90\% across diverse rendering applications.
       Implemented real-time adaptive vision-to-code generation achieving 95\% validation accuracy for complex UI interactions.}
       
-      \resumeItem{Visual Anomaly Detection for Graphics Validation (Project VAAML)}
+      \resumeItem{Visual Anomaly Detection for Graphics Validation}
       {Contributing to initiative addressing Intel's \$6.5M annual manual testing costs for hardware-game compatibility validation.
       Developing real-time anomaly detection system using Vision-Language Models that has reached 87\% detection accuracy in preliminary testing across diverse rendering scenarios.}