app.py

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel, PeftConfig
import torch
# Initialize the FastAPI app
app = FastAPI()

# Load model and tokenizer once at startup
base_model_name = "akjindal53244/Llama-3.1-Storm-8B"
peft_model_id = "LlamaFactoryAI/cv-job-description-matching"

base_model = AutoModelForCausalLM.from_pretrained(base_model_name, torch_dtype=torch.float16)
model = PeftModel.from_pretrained(base_model, peft_model_id, torch_dtype=torch.float16)
tokenizer = AutoTokenizer.from_pretrained(base_model_name)
config = PeftConfig.from_pretrained(peft_model_id)



# Define request model
class AnalysisRequest(BaseModel):
    cv: str
    job_description: str

@app.post("/analyze")
async def analyze(request: AnalysisRequest):
    try:
        # Prepare input text with formatted message
        system_prompt = """
        You are an advanced AI model designed to analyze the compatibility between a CV and a job description. You will receive a CV and a job description. Your task is to output a structured JSON format that includes the following:

        1. matching_analysis: Analyze the CV against the job description to identify key strengths and gaps.
        2. description: Summarize the relevance of the CV to the job description in a few concise sentences.
        3. score: Provide a numerical compatibility score (0-100) based on qualifications, skills, and experience.
        4. recommendation: Suggest actions for the candidate to improve their match or readiness for the role.

        Your output must be in JSON format as follows:
        {
          "matching_analysis": "Your detailed analysis here.",
          "description": "A brief summary here.",
          "score": 85,
          "recommendation": "Your suggestions here."
        }
        """
        user_input = f"<CV> {request.cv} </CV>\n<job_description> {request.job_description} </job_description>"
        input_text = system_prompt + user_input

        # Tokenize and generate response
        inputs = tokenizer(input_text, return_tensors="pt")
        outputs = model.generate(**inputs, max_new_tokens=64)
        generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)

        return {"analysis": generated_text}

    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))