app.py

import gradio as gr
from llama_cpp import Llama

# Load the SecurityLLM model
try:
    llm = Llama.from_pretrained(
        repo_id="QuantFactory/SecurityLLM-GGUF",
        filename="SecurityLLM.Q5_K_M.gguf",  # Ensure the file path is correct
    )
except Exception as e:
    raise RuntimeError(f"Failed to load model: {e}")

# Function to match CV to job descriptions
def match_cv_to_jobs(cv_text, job_descriptions):
    # Split job descriptions by line
    descriptions = job_descriptions.strip().split("\n")
    results = []
    
    for description in descriptions:
        # Create a prompt to compare the CV with each job description
        prompt = (
            f"Compare the following job description with this resume. Job Description: {description}. "
            f"Resume: {cv_text}. Provide a match score and a brief analysis."
        )
        
        # Generate response from the model
        response = llm.create_chat_completion(
            messages=[
                {
                    "role": "user",
                    "content": prompt
                }
            ]
        )
        
        # Extract and store the analysis text
        analysis_text = response["choices"][0]["message"]["content"]
        results.append({
            "Job Description": description,
            "Analysis": analysis_text
        })
    
    return results

# Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("# CV to Job Description Matcher")
    
    cv_text = gr.Textbox(label="CV Text", placeholder="Enter the CV text here", lines=10)
    job_descriptions = gr.Textbox(label="Job Descriptions (one per line)", placeholder="Enter each job description on a new line", lines=5)
    match_button = gr.Button("Match CV to Job Descriptions")
    
    output = gr.JSON(label="Match Results")
    
    match_button.click(fn=match_cv_to_jobs, inputs=[cv_text, job_descriptions], outputs=output)

demo.launch()