import gradio as gr
import os
from utils.text_extraction import extract_text_from_pdf, extract_text_from_txt
from utils.text_processing import preprocess_text, extract_dates, parse_date
from utils.skill_extraction import SkillExtractor
from utils.similarity import SimilarityCalculator

class ResumeAnalyzer:
    def __init__(self):
        self.skill_extractor = SkillExtractor()
        self.similarity_calculator = SimilarityCalculator()
        
    def analyze(self, resume_file, job_desc_file):
        """Main analysis function."""
        try:
            # Extract text from files
            resume_text = extract_text_from_pdf(resume_file.name)
            job_desc_text = extract_text_from_txt(job_desc_file.name)
            
            if isinstance(resume_text, str) and resume_text.startswith("Error"):
                return resume_text
            if isinstance(job_desc_text, str) and job_desc_text.startswith("Error"):
                return job_desc_text
                
            # Extract skills
            resume_skills = self.skill_extractor.extract_skills(resume_text)
            job_skills = self.skill_extractor.extract_skills(job_desc_text)
            
            # Calculate similarities
            text_similarity = self.similarity_calculator.calculate_text_similarity(
                resume_text, job_desc_text
            )
            skill_match = self.similarity_calculator.calculate_skill_match(
                resume_skills, job_skills
            )
            
            # Generate detailed analysis
            analysis = self._generate_analysis(
                resume_text, job_desc_text,
                resume_skills, job_skills,
                text_similarity, skill_match
            )
            
            return analysis
            
        except Exception as e:
            return f"Error during analysis: {str(e)}"
    
    def _generate_analysis(self, resume_text, job_desc_text, resume_skills, 
                         job_skills, text_similarity, skill_match):
        """Generate formatted analysis output."""
        return f"""
        ### Skills Analysis
        **Resume Skills:** {', '.join(resume_skills)}
        **Required Skills:** {', '.join(job_skills)}
        **Skill Match Score:** {skill_match:.2f}%
        
        ### Overall Match
        **Text Similarity Score:** {text_similarity:.2f}%
        **Combined Match Score:** {((text_similarity + skill_match) / 2):.2f}%
        
        ### Recommendation
        {self._get_recommendation(text_similarity, skill_match)}
        """
    
    def _get_recommendation(self, text_similarity, skill_match):
        """Generate recommendation based on scores."""
        average_score = (text_similarity + skill_match) / 2
        if average_score >= 75:
            return "Strong Match: Your profile aligns well with the job requirements."
        elif average_score >= 50:
            return "Moderate Match: You meet some requirements but might need additional skills."
        else:
            return "Low Match: Consider developing more relevant skills for this position."

def create_interface():
    """Create Gradio interface."""
    analyzer = ResumeAnalyzer()
    
    with gr.Blocks(title="Resume Analyzer", theme=gr.themes.Soft()) as app:
        gr.Markdown("# Resume Analyzer")
        gr.Markdown("Upload your resume and job description to get a detailed analysis.")
        
        with gr.Row():
            resume_file = gr.File(
                label="Upload Resume (PDF)",
                file_types=[".pdf"]
            )
            job_desc_file = gr.File(
                label="Upload Job Description (TXT)",
                file_types=[".txt"]
            )
        
        analyze_btn = gr.Button("Analyze", variant="primary")
        output = gr.Markdown(label="Analysis Results")
        
        analyze_btn.click(
            fn=analyzer.analyze,
            inputs=[resume_file, job_desc_file],
            outputs=output
        )
    
    return app

if __name__ == "__main__":
    app = create_interface()
    app.launch(share=True)