app.py

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from llama_cpp import Llama

# Initialize FastAPI app
app = FastAPI()

# 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}")

# Define request model for CV matching
class CVMatchRequest(BaseModel):
    cv_text: str
    job_descriptions: str

# Define response model
class CVMatchResponse(BaseModel):
    results: list

# Define the route for CV and job description matching
@app.post("/match_cv_to_jobs", response_model=CVMatchResponse)
async def match_cv_to_jobs(request: CVMatchRequest):
    try:
        # Split job descriptions by line
        descriptions = request.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: {request.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 return the analysis text
            analysis_text = response["choices"][0]["message"]["content"]
            results.append({
                "Job Description": description,
                "Analysis": analysis_text
            })
        
        return CVMatchResponse(results=results)
    
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

# To run the app, use: uvicorn app:app --reload