wehub-resource-sync
92 lines
3.3 KiB
Python
92 lines
3.3 KiB
Python
from typing import Dict, List, Optional, Tuple, Any
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from pydantic import BaseModel, Field, field_validator
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from models import JSONResume, EvaluationData
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from llm_utils import initialize_llm_provider, extract_json_from_response
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import logging
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import json
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import re
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MAX_BONUS_POINTS = 20
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MIN_FINAL_SCORE = -20
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MAX_FINAL_SCORE = 120
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from prompt import (
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DEFAULT_MODEL,
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MODEL_PARAMETERS,
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MODEL_PROVIDER_MAPPING,
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GEMINI_API_KEY,
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)
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from prompts.template_manager import TemplateManager
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logger = logging.getLogger(__name__)
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class ResumeEvaluator:
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def __init__(self, model_name: str = DEFAULT_MODEL, model_params: dict = None):
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if not model_name:
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raise ValueError("Model name cannot be empty")
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self.model_name = model_name
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self.model_params = model_params or MODEL_PARAMETERS.get(
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model_name, {"temperature": 0.5, "top_p": 0.9}
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)
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self.template_manager = TemplateManager()
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self._initialize_llm_provider()
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def _initialize_llm_provider(self):
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"""Initialize the appropriate LLM provider based on the model."""
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self.provider = initialize_llm_provider(self.model_name)
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def _load_evaluation_prompt(self, resume_text: str) -> str:
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criteria_template = self.template_manager.render_template(
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"resume_evaluation_criteria", text_content=resume_text
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)
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if criteria_template is None:
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raise ValueError("Failed to load resume evaluation criteria template")
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return criteria_template
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def evaluate_resume(self, resume_text: str) -> EvaluationData:
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self._last_resume_text = resume_text
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full_prompt = self._load_evaluation_prompt(resume_text)
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# logger.info(f"🔤 Evaluation prompt being sent: {full_prompt}")
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try:
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system_message = self.template_manager.render_template(
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"resume_evaluation_system_message"
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)
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if system_message is None:
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raise ValueError(
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"Failed to load resume evaluation system message template"
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)
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# Prepare chat parameters
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chat_params = {
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"model": self.model_name,
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"messages": [
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{"role": "system", "content": system_message},
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{"role": "user", "content": full_prompt},
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],
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"options": {
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"stream": False,
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"temperature": self.model_params.get("temperature", 0.5),
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"top_p": self.model_params.get("top_p", 0.9),
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},
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}
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# Add format parameter for structured output
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kwargs = {"format": EvaluationData.model_json_schema()}
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# Use the appropriate provider to make the API call
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response = self.provider.chat(**chat_params, **kwargs)
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response_text = response["message"]["content"]
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response_text = extract_json_from_response(response_text)
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logger.error(f"🔤 Prompt response: {response_text}")
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evaluation_dict = json.loads(response_text)
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evaluation_data = EvaluationData(**evaluation_dict)
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return evaluation_data
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except Exception as e:
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logger.error(f"Error evaluating resume: {str(e)}")
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raise
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