import gradio as gr
from llama_cpp import Llama
from transformers import pipeline
import json

# Load the Llama model
try:
    llm = Llama.from_pretrained(
        repo_id="HuggingFaceTB/SmolLM2-360M-Instruct-GGUF",
        filename="smollm2-360m-instruct-q8_0.gguf"  # Replace with the correct path to your GGUF file
    )
except Exception as e:
    raise RuntimeError(f"Failed to load model: {e}")

# Load summarization model
summarizer = pipeline("summarization")

# Summarize text to fit within token limits
def summarize_text(text, max_length=100):
    # Use the summarizer to condense the text
    summary = summarizer(text, max_length=max_length, min_length=25, do_sample=False)
    return summary[0]["summary_text"]

# Function to match CV to job descriptions with debug information
def match_cv_to_jobs(cv_text, job_descriptions):
    debug_info = "Debug Info:\n"
    results = []
    
    # Summarize `cv_text` and `job_descriptions` to manage token limits
    summarized_cv = summarize_text(cv_text, max_length=400)
    debug_info += f"Summarized CV Text: {summarized_cv}\n"
    
    descriptions = job_descriptions.strip().split("\n")
    for description in descriptions:
        summarized_description = summarize_text(description, max_length=100)
        debug_info += f"\nSummarized Job Description: {summarized_description}\n"
        
        # Create a prompt to compare the summarized CV with each summarized job description
        prompt = (
            f"Compare the following job description with this resume. Job Description: {summarized_description}. "
            f"Resume: {summarized_cv}. Provide a match score and a brief analysis."
        )
        debug_info += f"\nGenerated Prompt: {prompt}\n"
        
        # Generate response from the model
        try:
            response = llm.create_chat_completion(
                messages=[
                    {
                        "role": "user",
                        "content": prompt
                    }
                ]
            )
            
            # Extract the analysis text
            response_content = response["choices"][0]["message"]["content"]
            debug_info += f"Model Response: {response_content}\n"
            
            # Attempt to parse as JSON; if not JSON, use the raw text
            try:
                response_data = json.loads(response_content)
                results.append(response_data)
            except json.JSONDecodeError:
                results.append({
                    "Job Description": description,
                    "Analysis": response_content
                })
        except Exception as e:
            debug_info += f"Error: {str(e)}\n"
            results.append({"Job Description": description, "Error": str(e)})
    
    return results, debug_info

# Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("# CV and Job Description Matcher with Summarization and Debugging")
    
    # Input fields for CV and job descriptions
    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)
    
    # Button and output area
    match_button = gr.Button("Match CV to Job Descriptions")
    output = gr.JSON(label="Match Results")
    debug_output = gr.Textbox(label="Debug Info", lines=10)  # Add a debug box to display debug info
    
    # Set button click to run the function
    match_button.click(fn=match_cv_to_jobs, inputs=[cv_text, job_descriptions], outputs=[output, debug_output])

demo.launch()