from sentence_transformers import SentenceTransformer, util
import gradio as gr

# Load the SentenceTransformer model for sentence similarity
try:
    model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
except Exception as e:
    print(f"Error loading SentenceTransformer model: {e}")

def match_cv_to_job(cv_text, job_description):
    debug_info = "Debug Info:\n"
    
    # Encode the entire CV text
    try:
        cv_embedding = model.encode(cv_text, convert_to_tensor=True)
        debug_info += f"CV Embedding: {cv_embedding}\n"
    except Exception as e:
        debug_info += f"Error encoding CV text: {e}\n"
        return None, debug_info
    
    # Encode the entire job description
    try:
        job_description_embedding = model.encode(job_description, convert_to_tensor=True)
        debug_info += f"Job Description Embedding: {job_description_embedding}\n"
    except Exception as e:
        debug_info += f"Error encoding job description: {e}\n"
        return None, debug_info
    
    # Compute similarity score between the entire CV and the entire job description
    try:
        similarity_score = util.pytorch_cos_sim(cv_embedding, job_description_embedding).item()
        debug_info += f"Overall Similarity Score: {similarity_score}\n"
        
        # Convert similarity score to a percentage for easier interpretation
        match_percentage = similarity_score * 100
        debug_info += f"Overall Match Percentage: {match_percentage:.2f}%\n"
    except Exception as e:
        debug_info += f"Error calculating similarity score: {e}\n"
        return None, debug_info
    
    return {"Match Percentage": f"{match_percentage:.2f}%"}, debug_info

# Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("# CV and Job Description Matcher with Overall Similarity Score")
    
    # Input fields for CV and job description
    cv_text = gr.Textbox(label="CV Text", placeholder="Enter the CV text here", lines=10)
    job_description = gr.Textbox(label="Job Description", placeholder="Enter the entire job description text here", lines=10)
    
    # Button and output area
    match_button = gr.Button("Calculate Match Percentage")
    output = gr.JSON(label="Match Result")
    debug_output = gr.Textbox(label="Debug Info", lines=10)  # Debug box for detailed output
    
    # Set button click to run the function
    match_button.click(fn=match_cv_to_job, inputs=[cv_text, job_description], outputs=[output, debug_output])

demo.launch()