Create app.py

app.py

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+import random
+from typing import List, Dict
+import pandas as pd
+import gradio as gr
+from sentence_transformers import SentenceTransformer
+from sklearn.metrics.pairwise import cosine_similarity
+
+# ------------------------------
+# 1. Generate synthetic job database
+# ------------------------------
+def generate_job_database() -> List[Dict]:
+    job_templates = {
+        "Technology": [
+            {"title": "Software Engineer", "desc": "Design and develop software applications",
+             "skills": ["Python", "Java", "JavaScript", "Git", "Agile", "Problem Solving"]},
+            {"title": "Data Scientist", "desc": "Analyze complex data to extract business insights",
+             "skills": ["Python", "R", "Machine Learning", "SQL", "Statistics", "Pandas"]},
+            {"title": "DevOps Engineer", "desc": "Manage infrastructure and deployment pipelines",
+             "skills": ["AWS", "Docker", "Kubernetes", "Linux", "Python", "Terraform"]},
+            {"title": "Frontend Developer", "desc": "Create user interfaces and web experiences",
+             "skills": ["JavaScript", "React", "CSS", "HTML", "TypeScript", "Responsive Design"]},
+            {"title": "Backend Developer", "desc": "Build server-side applications and APIs",
+             "skills": ["Python", "Node.js", "Django", "PostgreSQL", "REST APIs", "MongoDB"]},
+            {"title": "Mobile Developer", "desc": "Develop applications for mobile platforms",
+             "skills": ["React Native", "Swift", "Kotlin", "Flutter", "iOS", "Android"]},
+        ],
+        "Healthcare": [
+            {"title": "Registered Nurse", "desc": "Provide patient care and medical support",
+             "skills": ["Patient Care", "Medical Knowledge", "CPR", "Communication", "Compassion"]},
+            {"title": "Medical Doctor", "desc": "Diagnose and treat medical conditions",
+             "skills": ["Medical Diagnosis", "Patient Care", "Clinical Skills", "Medical Ethics", "Communication"]},
+        ],
+        "Finance": [
+            {"title": "Financial Analyst", "desc": "Analyze financial data and market trends",
+             "skills": ["Financial Modeling", "Excel", "Data Analysis", "Financial Reporting", "Market Research"]},
+            {"title": "Accountant", "desc": "Manage financial records and tax preparation",
+             "skills": ["Accounting", "QuickBooks", "Tax Preparation", "Financial Reporting", "Attention to Detail"]},
+        ],
+        "Marketing": [
+            {"title": "Digital Marketing Manager", "desc": "Develop and execute digital marketing strategies",
+             "skills": ["Digital Marketing", "SEO", "Social Media", "Google Analytics", "Content Strategy"]},
+            {"title": "Content Marketing Specialist", "desc": "Create engaging content for marketing campaigns",
+             "skills": ["Content Creation", "SEO", "Social Media", "Writing", "Brand Management"]},
+        ],
+        "Education": [
+            {"title": "Elementary School Teacher", "desc": "Teach fundamental subjects to young students",
+             "skills": ["Teaching", "Classroom Management", "Curriculum Development", "Student Assessment", "Communication"]},
+            {"title": "High School Teacher", "desc": "Educate teenagers in specialized subjects",
+             "skills": ["Subject Expertise", "Teaching", "Classroom Management", "Lesson Planning", "Student Mentoring"]},
+        ],
+        "Sales": [
+            {"title": "Sales Representative", "desc": "Sell products and services to customers",
+             "skills": ["Sales", "Customer Relations", "Communication", "Negotiation", "Product Knowledge"]},
+            {"title": "Account Manager", "desc": "Manage relationships with key clients",
+             "skills": ["Account Management", "Client Relations", "Sales", "Communication", "Problem Solving"]},
+        ],
+        "Operations": [
+            {"title": "Operations Manager", "desc": "Oversee daily business operations",
+             "skills": ["Operations Management", "Process Improvement", "Team Leadership", "Budget Management", "Problem Solving"]},
+            {"title": "Project Manager", "desc": "Lead and coordinate project execution",
+             "skills": ["Project Management", "PMP", "Agile", "Risk Management", "Communication"]},
+        ],
+        "Creative": [
+            {"title": "Graphic Designer", "desc": "Create visual designs for various media",
+             "skills": ["Adobe Creative Suite", "Graphic Design", "Typography", "Brand Design", "Creativity"]},
+            {"title": "Content Writer", "desc": "Create written content for various platforms",
+             "skills": ["Writing", "Content Creation", "SEO", "Research", "Editing"]},
+        ]
+    }
+
+    experience_levels = ["Entry-level", "Mid-level", "Senior", "Lead/Principal"]
+
+    jobs = []
+    job_id = 1
+    categories = list(job_templates.keys())
+    jobs_per_category = 1000 // len(categories)
+    remaining_jobs = 1000 % len(categories)
+
+    for i, category in enumerate(categories):
+        templates = job_templates[category]
+        jobs_for_this_category = jobs_per_category + (1 if i < remaining_jobs else 0)
+
+        for j in range(jobs_for_this_category):
+            template = templates[j % len(templates)]
+            title_variations = [
+                template["title"],
+                f"Senior {template['title']}",
+                f"Junior {template['title']}",
+                f"Lead {template['title']}",
+                f"{template['title']} Specialist"
+            ]
+            title = title_variations[j % len(title_variations)]
+            exp_level = random.choice(experience_levels)
+            job = {
+                "id": job_id,
+                "title": title,
+                "description": template["desc"],
+                "requirements": template["skills"],
+                "experience_level": exp_level,
+                "category": category,
+                "location": random.choice([
+                    "Remote", "New York, NY", "San Francisco, CA", "Chicago, IL",
+                    "Austin, TX", "Seattle, WA", "Boston, MA", "Los Angeles, CA"
+                ])
+            }
+            jobs.append(job)
+            job_id += 1
+    return jobs
+
+# ------------------------------
+# 2. Prepare DataFrame and Embeddings
+# ------------------------------
+jobs = generate_job_database()
+df = pd.DataFrame(jobs)
+model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
+job_embeddings = model.encode(df['requirements'].apply(lambda x: " ".join(x)).tolist())
+
+# ------------------------------
+# 3. Job Recommendation Function
+# ------------------------------
+def recommend_jobs(user_skills, top_k=5):
+    user_embedding = model.encode([" ".join(user_skills)])
+    similarities = cosine_similarity(user_embedding, job_embeddings)[0]
+    top_indices = similarities.argsort()[-top_k:][::-1]
+    top_jobs = df.iloc[top_indices]
+    return top_jobs[['title', 'description', 'requirements', 'experience_level', 'category', 'location']]
+
+# ------------------------------
+# 4. Gradio Interface
+# ------------------------------
+def gradio_interface(skills_text):
+    user_skills = [skill.strip() for skill in skills_text.split(",")]
+    recommended = recommend_jobs(user_skills)
+    return recommended.to_dict(orient="records")
+
+app = gr.Interface(
+    fn=gradio_interface,
+    inputs=gr.Textbox(lines=2, placeholder="Enter your skills separated by commas"),
+    outputs=gr.Dataframe(headers=["Title", "Description", "Skills", "Experience", "Category", "Location"]),
+    title="AI Job Finder",
+    description="Enter your skills and find the best matching jobs!"
+)
+
+if __name__ == "__main__":
+    app.launch()