import gradio as gr
import pandas as pd
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.neighbors import NearestNeighbors

# Load dataset (replace 'dataset.csv' with your file path)
file_path = 'Job_Roles_and_Skills.csv'
dataset = pd.read_csv(file_path, encoding='latin1') # or 'ISO-8859-1' or 'cp1252'

#print(dataset.head()) 

# Preprocessing: Extract required fields
# Assuming 'skills required' contains the required skills
# Replace column names if they differ in your dataset
# Preprocessing: Extract required fields
# Assuming 'skills required' contains the required skills
# Replace column names if they differ in your dataset
def preprocess_skills(dataset):
    # Check if the element is a string before applying split
    dataset["Skills Required"] = dataset["Skills Required"].apply(lambda x: [skill.strip().lower() for skill in x.split(",") if isinstance(x, str)] if isinstance(x, str) else x) 
    return dataset


dataset = preprocess_skills(dataset)

# Train a simple NearestNeighbors model to match job roles (optional, for extensibility)
vectorizer = CountVectorizer(tokenizer=lambda x: x, preprocessor=lambda x: x)
skill_matrix = vectorizer.fit_transform(dataset["Skills Required"])

nn_model = NearestNeighbors(n_neighbors=1, metric="cosine")
nn_model.fit(skill_matrix)

# Define function to find missing skills
def find_missing_skills(job_role, current_skills):
    current_skills = [skill.strip().lower() for skill in current_skills.split(",")]
    
    # Match the job role
    job_row = dataset[dataset["Job Role"].str.lower() == job_role.lower()]
    if job_row.empty:
        return f"Job Role '{job_role}' not found in the dataset. Please try another option."

    required_skills = job_row.iloc[0]["Skills Required"]
    missing_skills = [skill for skill in required_skills if skill not in current_skills]

    return missing_skills if missing_skills else "No missing skills! You are fully qualified."

# Define the Gradio interface
def career_gap_analysis(job_role, current_skills):
    missing_skills = find_missing_skills(job_role, current_skills)
    if isinstance(missing_skills, list):
        return f"Missing Skills for '{job_role}': {', '.join(missing_skills)}"
    return missing_skills

# Extract unique job roles for dropdown options
job_roles = dataset["Job Role"].unique().tolist()

# Gradio App
# Gradio App
demo = gr.Interface(
    fn=career_gap_analysis,
    inputs=[
        gr.Dropdown(label="Job Role", choices=job_roles),  # Remove placeholder
        gr.Textbox(label="Current Skills", placeholder="Enter your current skills separated by commas (e.g., Python, SQL)"),
    ],
    outputs="text",
    title="Career Gap Analysis",
    description="Identify missing skills for a specific job role based on your current skill set."
)

# Launch the app
demo.launch()