import gradio as gr
import pandas as pd
import random
from typing import List, Dict
from sentence_transformers import SentenceTransformer, util

# -----------------------------
# Job Database Generation
# -----------------------------
class JobDatabase:
    def __init__(self):
        self.jobs = self._generate_job_database()
        self.df_jobs = pd.DataFrame(self.jobs)
        # Prepare a textual representation of skills for embeddings
        self.df_jobs['skills_text'] = self.df_jobs['requirements'].apply(lambda x: ", ".join(x))
        # Load lightweight embedding model
        self.model = SentenceTransformer('all-MiniLM-L6-v2')
        # Encode all job skills in advance
        self.job_embeddings = self.model.encode(self.df_jobs['skills_text'].tolist(), convert_to_tensor=True)

    def _generate_job_database(self) -> List[Dict]:
        """Generate a minimal example database; replace with your full database"""
        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"]}
            ],
            "Finance": [
                {"title": "Financial Analyst", "desc": "Analyze financial data and market trends",
                 "skills": ["Financial Modeling", "Excel", "Data Analysis", "Financial Reporting", "Market Research"]}
            ]
        }

        experience_levels = ["Entry-level", "Mid-level", "Senior", "Lead/Principal"]
        salary_ranges = {
            "Entry-level": ["$35k-$50k", "$40k-$55k"],
            "Mid-level": ["$55k-$75k", "$60k-$80k"],
            "Senior": ["$80k-$110k", "$90k-$120k"],
            "Lead/Principal": ["$120k-$150k", "$130k-$160k"]
        }
        additional_skills = {
            "Technology": ["Debugging", "Code Review", "System Design"],
            "Finance": ["Financial Regulations", "Risk Management", "Excel Advanced"]
        }

        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)
                salary = random.choice(salary_ranges[exp_level])
                base_skills = template["skills"].copy()
                extra_skills = random.sample(additional_skills[category],
                                             random.randint(1, min(3, len(additional_skills[category]))))
                all_skills = base_skills + extra_skills
                unique_skills = list(dict.fromkeys(all_skills))[:8]

                job = {
                    "id": job_id,
                    "title": title,
                    "description": template["desc"],
                    "requirements": unique_skills,
                    "experience_level": exp_level,
                    "salary_range": salary,
                    "category": category,
                    "location": random.choice([
                        "Remote", "New York, NY", "San Francisco, CA", "Chicago, IL",
                        "Austin, TX", "Seattle, WA", "Boston, MA", "Los Angeles, CA",
                        "Denver, CO", "Atlanta, GA", "Miami, FL", "Portland, OR"
                    ])
                }
                jobs.append(job)
                job_id += 1
        return jobs

# -----------------------------
# Job Matching Function
# -----------------------------
def match_jobs_embeddings(user_skills: List[str], db: JobDatabase, top_n=5):
    # Convert user input into single string
    skills_text = ", ".join([s.strip() for s in user_skills if s.strip()])
    if not skills_text:
        return pd.DataFrame([{"title":"No skills entered","description":"","requirements":"","experience_level":"","salary_range":"","location":""}])
    
    # Encode user skills
    user_embedding = db.model.encode(skills_text, convert_to_tensor=True)
    # Compute cosine similarity
    cos_scores = util.cos_sim(user_embedding, db.job_embeddings)[0]
    # Get top N matches
    top_results = cos_scores.topk(top_n)
    indices = top_results.indices.tolist()
    
    matched_jobs = db.df_jobs.iloc[indices]
    return matched_jobs[['title', 'description', 'requirements', 'experience_level', 'salary_range', 'location']]

# -----------------------------
# Gradio Interface
# -----------------------------
db = JobDatabase()

def find_jobs_ui(user_skills_text):
    user_skills = [skill.strip() for skill in user_skills_text.split(",")]
    return match_jobs_embeddings(user_skills, db)

iface = gr.Interface(
    fn=find_jobs_ui,
    inputs=gr.Textbox(lines=2, placeholder="Enter skills separated by commas, e.g. Python, SQL, Excel"),
    outputs=gr.Dataframe(headers=["Title","Description","Requirements","Experience Level","Salary","Location"]),
    title="Job Finder with AI Embeddings",
    description="Enter your skills and get top matching jobs using AI embeddings."
)

if __name__ == "__main__":
    iface.launch()