hotfix: added stub response

.python-version

@@ -0,0 +1 @@
+3.12

Process/views.py

@@ -64,33 +64,210 @@ def process_resume(request):
             logger.info("Processing resume request")
             data = json.loads(request.body)
 
-            user_id = data.get("user_id")
-            file_link = data.get("file_link")
-            job_description = data.get("job_description")
-            logger.info(f"Received data for user_id: {user_id}")
+            # user_id = data.get("user_id")
+            # file_link = data.get("file_link")
+            # job_description = data.get("job_description")
+            # logger.info(f"Received data for user_id: {user_id}")
 
-            if not all([user_id, file_link, job_description]):
-                logger.warning("Missing required fields in request")
-                return JsonResponse({"error": "Missing requiredz fields"}, status=400)
+            # if not all([user_id, file_link, job_description]):
+            #     logger.warning("Missing required fields in request")
+            #     return JsonResponse({"error": "Missing requiredz fields"}, status=400)
 
-            logger.info("Extracting Text from the pdf")
-            resume = extract_text_from_pdf(file_link)
-            logger.info(f"Text extracted from the pdf : {resume}")
+            # logger.info("Extracting Text from the pdf")
+            # resume = extract_text_from_pdf(file_link)
+            # logger.info(f"Text extracted from the pdf : {resume}")
 
-            logger.info("Extracting resume details")
-            st_data = extract_resume_details(resume)
-            logger.info("Resume details extraction completed")
+            # logger.info("Extracting resume details")
+            # st_data = extract_resume_details(resume)
+            # logger.info("Resume details extraction completed")
 
-            logger.info("Generating ATS score")
-            ats_score = generate_ats_score(st_data, job_description)
-            logger.info("ATS score generation completed")
+            # logger.info("Generating ATS score")
+            # ats_score = generate_ats_score(st_data, job_description)
+            # logger.info("ATS score generation completed")
+
+            # response_data = {
+            #     "user_id": user_id,
+            #     "similarity": "100.00",
+            #     "ats_score": ats_score,
+            #     "structured_data": st_data,
+            # }
 
             response_data = {
-                "user_id": user_id,
-                "similarity": "100.00",
-                "ats_score": ats_score,
-                "structured_data": st_data,
-            }
+                        "user_id": 12345,
+                        "user_name": "John Doe",
+                        "similarity": 0.23571285605430603,
+                        "ats_score": {
+                        "ats_score": 88.0,
+                        "detailed_scores": {
+                        "skills_match": 90.0,
+                        "experience_relevance": 85.0,
+                        "education_relevance": 90.0,
+                        "overall_formatting": 100
+                        },
+                        "feedback": {
+                        "strengths": [
+                            "Strong AI and software engineering skills",
+                            "Highly relevant project experience for AI software development",
+                            "Strong education background with good GPA",
+                            "Clean and ATS-friendly formatting"
+                        ],
+                        "improvements": [
+                            "Add clearer mapping of projects to end-to-end software engineering use cases",
+                            "Explicitly mention system design and production-scale deployment experience"
+                        ]
+                        },
+                        "detailed_feedback": {
+                        "skills_match": {
+                            "matching_elements": [
+                            "Python",
+                            "FastAPI",
+                            "Django",
+                            "Machine Learning",
+                            "NLP",
+                            "LLMs",
+                            "PyTorch",
+                            "Docker",
+                            "AWS",
+                            "React.js",
+                            "PostgreSQL",
+                            "MongoDB"
+                            ],
+                            "missing_elements": [
+                            "Explicit mention of microservices architecture",
+                            "Explicit mention of CI/CD pipelines in production"
+                            ],
+                            "explanation": "The candidate demonstrates a strong match for an AI Software Developer role with expertise in Python, backend frameworks, AI/ML, LLMs, and cloud-native tools. Skills align well with building, deploying, and optimizing AI-driven systems. Adding clearer mentions of microservices and CI/CD in real-world production contexts would further strengthen the profile."
+                        },
+                        "experience_relevance": {
+                            "matching_elements": [
+                            "Built AI-driven fashion visualization and automation pipelines",
+                            "Migrated backend systems from Flask to FastAPI with performance improvements",
+                            "Developed NLP-based summarization systems using PEGASUS, BERTsum, and BART",
+                            "Built speech-to-text systems with performance optimization",
+                            "Implemented machine learning models from scratch (MNIST classifier)"
+                            ],
+                            "missing_elements": [
+                            "Explicit ownership of large-scale production deployments",
+                            "Long-term industry experience beyond internships"
+                            ],
+                            "explanation": "The experience strongly aligns with an AI Software Developer role, covering AI research, backend engineering, system optimization, and applied ML. Internships demonstrate hands-on impact, performance gains, and real-world deployment. While the experience is strong, longer-term full-time production ownership would further improve relevance."
+                        },
+                        "education_relevance": {
+                            "matching_elements": [
+                            "MTech (Integrated) in Computer Science and Engineering",
+                            "Strong GPA: 8.59",
+                            "AI-focused coursework and projects"
+                            ],
+                            "missing_elements": [],
+                            "explanation": "The education background is highly relevant for an AI Software Developer role, providing strong foundations in computer science, AI, and engineering concepts."
+                        },
+                        "overall_formatting": {
+                            "matching_elements": [
+                            "Clear section headings",
+                            "Name, email, phone, and GitHub present",
+                            "Well-structured bullet points",
+                            "Consistent formatting suitable for ATS"
+                            ],
+                            "missing_elements": [],
+                            "explanation": "The resume formatting is clean, structured, and fully ATS-compatible, enabling easy parsing by automated systems."
+                        }
+                        }
+                    },
+                        "structured_data": {
+                        "name": "KB Harish",
+                        "email": "harishkb20205@gmail.com",
+                        "github": "https://github.com/HARISH20205",
+                        "phone": "+91-824-805-2926",
+                        "skills": ["Python", "Java", "C", "C++", "JavaScript", "TypeScript",
+                        "HTML", "CSS", "React.js", "Tailwind CSS", "Firebase",
+                        "Django", "FastAPI", "Flask", "Express.js", "MongoDB", "PostgreSQL", "Prisma",
+                        "ML", "NLP", "LLMs (Fine-tuning, Inference, Optimization)", "CNN", "YOLO", "ViT", "PyTorch",
+                        "AWS", "Docker", "Hugging Face", "Jenkins", "Selenium", "CI/CD",
+                        "Git", "Linux/Unix", "ROS2", "Raspberry Pi 5"]
+                        },
+                        "experience": [
+                        {
+                            "title": "Genrative AI Intern",
+                            "company": "TITAN Company Limited",
+                            "start_date": "Jun 2025",
+                            "end_date": "Jul 2025",
+                            "description": [
+                            "Built an AI-driven fashion visualization pipeline with Runway ML and automated retail ops using n8n, reducing catalog time by 60%, manual effort by 70%, and speeding up product launches by 3x.",
+                            "Migrated Taneira’s backend from Flask to FastAPI, improving API response time by 40%, scaling throughput by 2.5x, and integrating a modular RAG-based chatbot for AI-driven support."
+                            ]
+                        },
+                        {
+                            "title": "AI Research and Development Intern",
+                            "company": "eBramha Techworks Private Limited",
+                            "start_date": "Jun 2024",
+                            "end_date": "Oct 2024",
+                            "description": [
+                            "Conducted comprehensive analysis of advanced NLP models like PEGASUS, BERTsum, and BART; applied insights to optimize summarization tasks, improving accuracy by 25% in real-world use cases.",
+                            "Developed a speech-to-text system, reducing processing time by 40%, and Constructed an MNIST digit classifier with 95% accuracy using gradient descent and one-hot encoding."
+                            ]
+                        }
+                        ],
+                        "education": [
+                        {
+                            "institution": "Vellore Institute of Technology (VIT), Vellore, India",
+                            "degree": "MTech (Integrated) in Computer Science and Engineering",
+                            "gpa": "8.59",
+                            "start_date": "Aug 2022",
+                            "end_date": "Jul 2027"
+                        }
+                        ],
+                        "certifications": [
+                        "Coursera: Supervised Machine Learning: Regression and Classification",
+                        "Coursera: Advanced Learning Algorithms",
+                        "Coursera: Generative AI with Large Language Models"
+                        ],
+                        "areas_of_interest": "",
+                        "projects": [
+                        {
+                            "project": "FrugalSOT",
+                            "name": "FrugalSOT",
+                            "description": [
+                            "Architected an LLM system using Ollama and Raspberry Pi 5, starting with device capability detection to dynamically select models suited for hardware, reducing latency by 70%.",
+                            "Reduced computational costs by 15% by integrating intelligent model selection with fallback mechanisms, ensuring seamless task execution.",
+                            "Implemented an adaptive thresholding mechanism that updates thresholds based on prompt history, improving model selection efficiency."
+                            ],
+                            "link": "https://github.com/HARISH20205/FrugalSOT.git"
+                        },
+                        {
+                            "project": "PHYDRA",
+                            "name": "PHYDRA",
+                            "description": [
+                            "Designed a highly scalable Stowage Management System for the ISS, capable of processing over 10 million items in under 5 seconds, ensuring exceptional operational efficiency.",
+                            "Engineered a high-performance backend using FastAPI, optimized with Python and C++ subprocess orchestration, reducing compute time by 95% and integrating Prisma for seamless database compatibility.",
+                            "Built an interactive frontend using React and Tailwind CSS for a user-friendly interface."
+                            ],
+                            "link": "https://github.com/Mantissagithub/PHYDRA.git"
+                        },
+                        {
+                            "project": "LaunchLLM",
+                            "name": "LaunchLLM",
+                            "description": [
+                            "Orchestrated a frictionless LLM deployment toolchain supporting open-source models and GPU customization, reducing manual configuration by over 90% and accelerating time-to-production.",
+                            "Containerized SGLang using Docker to ensure reproducible, low-latency inference environments, achieving model initialization times under 5 seconds for most mid-tier models.",
+                            "Developed a FastAPI middleware for efficient routing and metrics, enabling scalable backend infrastructure."
+                            ],
+                            "link": ""
+                        }
+                        ],
+                        "languages": "",
+                        "awards_and_achievements": "",
+                        "volunteer_experience": "",
+                        "hobbies_and_interests": "",
+                        "publications": "",
+                        "conferences_and_presentations": "",
+                        "patents": "",
+                        "professional_affiliations": "",
+                        "portfolio_links": [
+                        "https://frugalsot.vercel.app/",
+                        "https://www.linkedin.com/in/harish-kb-9417ba252/"
+                        ],
+                        "summary_or_objective": ""
+                    }
             logger.info("Sending successful response")
             return JsonResponse(response_data, status=200)
         except json.JSONDecodeError as e:

app.py

@@ -29,7 +29,6 @@ if __name__ == "__main__":
         logger.info(f"Working directory: {os.getcwd()}")
         
         # Start the Django development server
-        # For better production deployment, consider using gunicorn
         logger.info("Launching Django server")
         subprocess.run(
             ["python", "manage.py", "runserver", f"0.0.0.0:{port}"],

main.py

@@ -0,0 +1,6 @@
+def main():
+    print("Hello from resume-ats!")
+
+
+if __name__ == "__main__":
+    main()

pyproject.toml

@@ -0,0 +1,7 @@
+[project]
+name = "resume-ats"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.12"
+dependencies = []