Delete src/llm_utils.py

src/llm_utils.py

@@ -1,123 +0,0 @@
-import os
-from dotenv import load_dotenv
-from huggingface_hub import InferenceClient
-import re
-from typing import List
-import logging
-
-# Set up logging
-logging.basicConfig(filename="llm_errors.log", level=logging.ERROR)
-
-# Load environment variables
-load_dotenv()
-
-class LLMHelper:
-    def __init__(self):
-        self.client = InferenceClient(
-            model=os.getenv("HF_MODEL", "HuggingFaceH4/zephyr-7b-beta"),
-            token=os.getenv("HF_TOKEN")
-        )
-        
-    def generate_text(self, prompt: str, max_tokens: int = 500) -> str:
-        """Generate text from the LLM with error handling and retry"""
-        for attempt in range(2):  # Retry once on failure
-            try:
-                response = self.client.text_generation(
-                    prompt,
-                    max_new_tokens=max_tokens,
-                    temperature=0.7,
-                    do_sample=True
-                )
-                return response.strip()
-            except Exception as e:
-                logging.error(f"LLM Error (Attempt {attempt + 1}): {str(e)}")
-                if attempt == 1:
-                    return ""
-        return ""
-
-def parse_tech_stack(input_str: str) -> List[str]:
-    """Parse tech stack input into a cleaned list of technologies"""
-    if not input_str.strip():
-        return []
-    
-    replacements = {
-        "c#": "C#",
-        "c++": "C++",
-        "f#": "F#",
-        "golang": "Go",
-        "js": "JavaScript",
-        "ts": "TypeScript",
-        "nodejs": "Node.js",
-        "node": "Node.js",
-        "reactjs": "React",
-        "vuejs": "Vue.js",
-        "postgresql": "PostgreSQL",
-        "mysql": "MySQL"
-    }
-    
-    techs = re.split(r'[,;/\n]', input_str)
-    cleaned = []
-    
-    for tech in techs:
-        tech = tech.strip()
-        if tech:
-            tech = tech.lower()
-            tech = replacements.get(tech, tech)
-            if not re.match(r'^[a-z0-9+#]+$', tech):
-                tech = tech.title()
-            if tech not in cleaned and len(tech) > 1:
-                cleaned.append(tech)
-    
-    return cleaned
-
-def generate_tech_questions(tech_stack: List[str], years_experience: int) -> List[str]:
-    """Generate technical questions based on tech stack and experience level"""
-    if not tech_stack:
-        return ["Please describe your technical experience."]
-
-    llm = LLMHelper()
-    difficulty = "beginner" if years_experience < 2 else "intermediate" if years_experience < 5 else "advanced"
-    questions = []
-    
-    for tech in tech_stack:
-        prompt = f"""Generate exactly 3 technical questions about {tech} for a candidate with {years_experience} years of experience.
-Difficulty level: {difficulty}
-Format each question clearly numbered like:
-1. [Question about {tech}]
-2. [Question about {tech}] 
-3. [Question about {tech}]
-
-The questions should:
-- Be technical and specific to {tech}
-- Cover different aspects (syntax, architecture, debugging)
-- Require detailed answers
-- Avoid simple yes/no or one-word answers
-- Be unique and not repetitive
-- Be relevant to real-world use cases
-
-Example for Python (intermediate):
-1. How would you optimize memory usage in a Python application processing large datasets?
-2. Explain the differences between multiprocessing and threading in Python with examples.
-3. Describe how you would implement and test a custom context manager in Python.
-"""
-        response = llm.generate_text(prompt)
-        
-        tech_questions = []
-        for line in response.split('\n'):
-            line = line.strip()
-            if line and re.match(r'^\d+\.\s*\[.*\]\s*$', line):
-                question = line.split('.', 1)[1].strip()[1:-1].strip()
-                if question:
-                    tech_questions.append(f"{tech}: {question}")
-        
-        if len(tech_questions) < 3:
-            default_questions = [
-                f"{tech}: Explain the most challenging {tech} project you've worked on and the key technical decisions you made.",
-                f"{tech}: How would you optimize performance in a {tech} application handling high traffic?",
-                f"{tech}: Describe your approach to debugging a complex issue in a {tech} application."
-            ]
-            tech_questions.extend(default_questions[:3 - len(tech_questions)])
-        
-        questions.extend(tech_questions[:3])
-    
-    return questions[:15]  # Limit to 15 questions max to avoid overwhelming candidates