app.py

import os
import pandas as pd
import torch
from sentence_transformers import SentenceTransformer, util
import faiss
import numpy as np
import pickle
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import scipy.special
from tqdm import tqdm
from sklearn.feature_extraction.text import TfidfVectorizer
from multiprocessing import Pool, cpu_count
from flask import Flask, request, jsonify
import logging

# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Disable tokenizers parallelism to avoid fork-related deadlocks
os.environ["TOKENIZERS_PARALLELISM"] = "false"

# Paths for saving artifacts
MODEL_DIR = "./saved_models"
FALLBACK_MODEL_DIR = "/tmp/saved_models"

try:
    os.makedirs(MODEL_DIR, exist_ok=True)
    logger.info(f"Using model directory: {MODEL_DIR}")
    chosen_model_dir = MODEL_DIR
except Exception as e:
    logger.warning(f"Failed to create {MODEL_DIR}: {e}. Using fallback directory.")
    os.makedirs(FALLBACK_MODEL_DIR, exist_ok=True)
    chosen_model_dir = FALLBACK_MODEL_DIR

# Update paths
UNIVERSAL_MODEL_PATH = os.path.join(chosen_model_dir, "universal_model")
DETECTOR_MODEL_PATH = os.path.join(chosen_model_dir, "detector_model")
TFIDF_PATH = os.path.join(chosen_model_dir, "tfidf_vectorizer.pkl")
SKILL_TFIDF_PATH = os.path.join(chosen_model_dir, "skill_tfidf.pkl")
QUESTION_ANSWER_PATH = os.path.join(chosen_model_dir, "question_to_answer.pkl")
FAISS_INDEX_PATH = os.path.join(chosen_model_dir, "faiss_index.index")
QUESTION_EMBEDDINGS_PATH = os.path.join(chosen_model_dir, "question_embeddings.pkl")
COURSE_EMBEDDINGS_PATH = os.path.join(chosen_model_dir, "course_embeddings.pkl")
JOB_SKILL_EMBEDDINGS_PATH = os.path.join(chosen_model_dir, "job_skill_embeddings.pkl")
JOB_TITLE_EMBEDDINGS_PATH = os.path.join(chosen_model_dir, "job_title_embeddings.pkl")

# Improved dataset loading with fallback
def load_dataset(file_path, required_columns=[], fallback_data=None):
    try:
        df = pd.read_csv(file_path)
        for col in required_columns:
            if col not in df.columns:
                logger.warning(f"Column '{col}' missing in {file_path}. Using default values.")
                df[col] = ""
        return df
    except Exception as e:
        logger.error(f"Error loading {file_path}: {e}")
        if fallback_data is not None:
            logger.info(f"Using fallback data for {file_path}")
            return pd.DataFrame(fallback_data)
        return None

# Load datasets with fallbacks
questions_df = load_dataset("Generated_Skill-Based_Questions.csv", ["Skill", "Question", "Answer"], {
    'Skill': ['Linux', 'Git', 'Node.js', 'Python', 'Kubernetes'],
    'Question': ['Advanced Linux question', 'Advanced Git question', 'Basic Node.js question', 
                 'Intermediate Python question', 'Basic Kubernetes question'],
    'Answer': ['Linux answer', 'Git answer', 'Node.js answer', 'Python answer', 'Kubernetes answer']
})

courses_df = load_dataset("coursera_course_dataset_v2_no_null.csv", ["skills", "course_title", "Organization", "level"], {
    'skills': ['Linux', 'Git', 'Node.js', 'Python', 'Kubernetes'],
    'course_title': ['Linux Admin', 'Git Mastery', 'Node.js Advanced', 'Python for Data', 'Kubernetes Basics'],
    'Organization': ['Coursera', 'Udemy', 'Pluralsight', 'edX', 'Linux Foundation'],
    'level': ['Intermediate', 'Intermediate', 'Advanced', 'Advanced', 'Intermediate'],
    'popularity': [0.85, 0.9, 0.8, 0.95, 0.9],
    'completion_rate': [0.65, 0.7, 0.6, 0.8, 0.75]
})

jobs_df = load_dataset("Updated_Job_Posting_Dataset.csv", ["job_title", "company_name", "location", "required_skills", "job_description"], {
    'job_title': ['DevOps Engineer', 'Cloud Architect', 'Software Engineer', 'Data Scientist', 'Security Analyst'],
    'company_name': ['Tech Corp', 'Cloud Inc', 'Tech Solutions', 'Data Co', 'SecuriTech'],
    'location': ['Remote', 'Islamabad', 'Karachi', 'Remote', 'Islamabad'],
    'required_skills': ['Linux, Kubernetes', 'AWS, Kubernetes', 'Python, Node.js', 'Python, SQL', 'Cybersecurity, Linux'],
    'job_description': ['DevOps role description', 'Cloud architecture position', 'Software engineering role', 'Data science position', 'Security analyst role'],
    'level': ['Intermediate', 'Advanced', 'Intermediate', 'Intermediate', 'Intermediate']
})

# Validate questions_df
if questions_df is None or questions_df.empty:
    logger.error("questions_df is empty or could not be loaded. Exiting.")
    exit(1)
if not all(col in questions_df.columns for col in ["Skill", "Question", "Answer"]):
    logger.error("questions_df is missing required columns. Exiting.")
    exit(1)
logger.info(f"questions_df loaded with {len(questions_df)} rows. Skills available: {list(questions_df['Skill'].unique())}")

# Load or Initialize Models
if os.path.exists(UNIVERSAL_MODEL_PATH):
    universal_model = SentenceTransformer(UNIVERSAL_MODEL_PATH)
else:
    universal_model = SentenceTransformer("all-MiniLM-L6-v2")

if os.path.exists(DETECTOR_MODEL_PATH):
    detector_tokenizer = AutoTokenizer.from_pretrained(DETECTOR_MODEL_PATH)
    detector_model = AutoModelForSequenceClassification.from_pretrained(DETECTOR_MODEL_PATH)
else:
    detector_tokenizer = AutoTokenizer.from_pretrained("roberta-base-openai-detector")
    detector_model = AutoModelForSequenceClassification.from_pretrained("roberta-base-openai-detector")

# Global variables for cached embeddings
question_embeddings = None
course_embeddings = None
job_skill_embeddings = None
job_title_embeddings = None

# Precompute Resources with Caching
def initialize_resources(user_skills):
    global tfidf_vectorizer, skill_tfidf, question_to_answer, faiss_index, question_embeddings, course_embeddings, job_skill_embeddings, job_title_embeddings
    
    user_skills_lower = [s.lower() for s in user_skills]
    needs_recompute = False

    # Check if cached resources exist
    required_paths = [TFIDF_PATH, SKILL_TFIDF_PATH, QUESTION_ANSWER_PATH, FAISS_INDEX_PATH,
                      QUESTION_EMBEDDINGS_PATH, COURSE_EMBEDDINGS_PATH, JOB_SKILL_EMBEDDINGS_PATH, JOB_TITLE_EMBEDDINGS_PATH]
    if all(os.path.exists(p) for p in required_paths):
        try:
            with open(TFIDF_PATH, 'rb') as f:
                tfidf_vectorizer = pickle.load(f)
            with open(SKILL_TFIDF_PATH, 'rb') as f:
                skill_tfidf = pickle.load(f)
            with open(QUESTION_ANSWER_PATH, 'rb') as f:
                question_to_answer = pickle.load(f)
            faiss_index = faiss.read_index(FAISS_INDEX_PATH)
            with open(QUESTION_EMBEDDINGS_PATH, 'rb') as f:
                question_embeddings = pickle.load(f)
            with open(COURSE_EMBEDDINGS_PATH, 'rb') as f:
                course_embeddings = pickle.load(f)
            with open(JOB_SKILL_EMBEDDINGS_PATH, 'rb') as f:
                job_skill_embeddings = pickle.load(f)
            with open(JOB_TITLE_EMBEDDINGS_PATH, 'rb') as f:
                job_title_embeddings = pickle.load(f)
            
            if set(skill_tfidf.keys()) != set(user_skills_lower):
                logger.info("Skill mismatch detected, recomputing resources")
                needs_recompute = True
        except Exception as e:
            logger.error(f"Error loading saved resources: {e}")
            needs_recompute = True
    else:
        needs_recompute = True

    if needs_recompute:
        logger.info("Building new resources")
        tfidf_vectorizer = TfidfVectorizer(stop_words='english')
        all_texts = user_skills + questions_df['Answer'].fillna("").tolist() + questions_df['Question'].tolist()
        tfidf_vectorizer.fit(all_texts)
        
        skill_tfidf = {skill.lower(): tfidf_vectorizer.transform([skill]).toarray()[0] for skill in user_skills}
        question_to_answer = dict(zip(questions_df['Question'], questions_df['Answer']))
        answers = list(question_to_answer.values())
        question_embeddings = universal_model.encode(answers, batch_size=128, convert_to_tensor=True).cpu().numpy()
        
        faiss_index = faiss.IndexFlatL2(question_embeddings.shape[1])
        faiss_index.add(question_embeddings)
        
        # Precompute embeddings for courses and jobs
        course_embeddings = universal_model.encode(courses_df['skills'].fillna("").tolist(), batch_size=128, convert_to_tensor=True).cpu().numpy()
        job_field = 'required_skills' if 'required_skills' in jobs_df.columns else 'job_description'
        job_skill_embeddings = universal_model.encode(jobs_df[job_field].fillna("").tolist(), batch_size=128, convert_to_tensor=True).cpu().numpy()
        job_title_embeddings = universal_model.encode(jobs_df['job_title'].fillna("").tolist(), batch_size=128, convert_to_tensor=True).cpu().numpy()
        
        # Save resources
        with open(TFIDF_PATH, 'wb') as f: pickle.dump(tfidf_vectorizer, f)
        with open(SKILL_TFIDF_PATH, 'wb') as f: pickle.dump(skill_tfidf, f)
        with open(QUESTION_ANSWER_PATH, 'wb') as f: pickle.dump(question_to_answer, f)
        faiss.write_index(faiss_index, FAISS_INDEX_PATH)
        with open(QUESTION_EMBEDDINGS_PATH, 'wb') as f: pickle.dump(question_embeddings, f)
        with open(COURSE_EMBEDDINGS_PATH, 'wb') as f: pickle.dump(course_embeddings, f)
        with open(JOB_SKILL_EMBEDDINGS_PATH, 'wb') as f: pickle.dump(job_skill_embeddings, f)
        with open(JOB_TITLE_EMBEDDINGS_PATH, 'wb') as f: pickle.dump(job_title_embeddings, f)
        universal_model.save(UNIVERSAL_MODEL_PATH)
        logger.info(f"Resources saved to {chosen_model_dir}")

# Enhanced evaluation with batch processing and parallelization
def evaluate_response(args):
    try:
        skill, user_answer, expected_answer, user_answer_embedding, expected_answer_embedding = args
        if not user_answer:
            return skill, 0.0, False

        inputs = detector_tokenizer(user_answer, return_tensors="pt", truncation=True, max_length=512)
        with torch.no_grad():
            logits = detector_model(**inputs).logits
        probs = scipy.special.softmax(logits, axis=1).tolist()[0]
        is_ai = probs[1] > 0.5

        score = util.pytorch_cos_sim(user_answer_embedding, expected_answer_embedding).item() * 100

        user_tfidf = tfidf_vectorizer.transform([user_answer]).toarray()[0]
        skill_vec = skill_tfidf.get(skill.lower(), np.zeros_like(user_tfidf))
        relevance = np.dot(user_tfidf, skill_vec) / (np.linalg.norm(user_tfidf) * np.linalg.norm(skill_vec) + 1e-10)
        score *= max(0.5, min(1.0, relevance))

        return skill, round(max(0, score), 2), is_ai
    except Exception as e:
        logger.error(f"Evaluation error for {skill}: {e}")
        return skill, 0.0, False

# Improved course recommendation with cached embeddings
def recommend_courses(skills_to_improve, user_level, upgrade=False):
    try:
        if not skills_to_improve or courses_df.empty:
            return []

        if 'popularity' not in courses_df:
            courses_df['popularity'] = 0.8
        if 'completion_rate' not in courses_df:
            courses_df['completion_rate'] = 0.7

        skill_embeddings = universal_model.encode(skills_to_improve, batch_size=128, convert_to_tensor=True)
        similarities = util.pytorch_cos_sim(skill_embeddings, torch.tensor(course_embeddings)).numpy()

        total_scores = 0.6 * similarities + 0.2 * courses_df['popularity'].values + 0.2 * courses_df['completion_rate'].values

        recommendations = []
        target_level = 'Advanced' if upgrade else user_level
        for i, skill in enumerate(skills_to_improve):
            idx = np.argsort(-total_scores[i])[:5]
            candidates = courses_df.iloc[idx]
            candidates = candidates[candidates['level'].str.contains(target_level, case=False, na=False)]
            recommendations.extend(candidates[['course_title', 'Organization']].values.tolist()[:3])

        return list(dict.fromkeys(map(tuple, recommendations)))
    except Exception as e:
        logger.error(f"Course recommendation error: {e}")
        return []

# Enhanced job recommendation with cached embeddings
def recommend_jobs(user_skills, user_level):
    try:
        if jobs_df.empty:
            return []

        user_embedding = universal_model.encode(" ".join(user_skills), batch_size=128, convert_to_tensor=True)
        skill_similarities = util.pytorch_cos_sim(user_embedding, torch.tensor(job_skill_embeddings)).numpy()[0]

        if 'level' not in jobs_df.columns:
            jobs_df['level'] = 'Intermediate'
        level_col = jobs_df['level'].astype(str)
        level_map = {'Beginner': 0, 'Intermediate': 1, 'Advanced': 2}
        user_level_num = level_map.get(user_level, 1)
        level_scores = level_col.apply(lambda x: 1 - abs(level_map.get(x, 1) - user_level_num)/2)

        location_pref = jobs_df.get('location', pd.Series(['Remote'] * len(jobs_df))).apply(lambda x: 1.0 if x in ['Islamabad', 'Karachi'] else 0.7)
        industry_similarities = util.pytorch_cos_sim(user_embedding, torch.tensor(job_title_embeddings)).numpy()[0]

        total_job_scores = 0.5 * skill_similarities + 0.2 * level_scores + 0.1 * location_pref + 0.2 * industry_similarities
        top_job_indices = np.argsort(-total_job_scores)[:5]

        return [(jobs_df.iloc[i]['job_title'], jobs_df.iloc[i]['company_name'], 
                jobs_df.iloc[i].get('location', 'Remote')) for i in top_job_indices]
    except Exception as e:
        logger.error(f"Job recommendation error: {e}")
        return []

# Flask application setup
app = Flask(__name__)

@app.route('/')
def health_check():
    return jsonify({"status": "active", "model_dir": chosen_model_dir})

@app.route('/assess', methods=['POST'])
def assess_skills():
    try:
        data = request.get_json()
        if not data or 'skills' not in data or 'answers' not in data:
            return jsonify({"error": "Missing required fields"}), 400

        user_skills = [s.strip() for s in data['skills'] if isinstance(s, str)]
        answers = [a.strip() for a in data['answers'] if isinstance(a, str)]
        user_level = data.get('user_level', 'Intermediate').strip()

        if len(answers) != len(user_skills):
            return jsonify({"error": "Answers count must match skills count"}), 400

        initialize_resources(user_skills)

        # Get relevant questions
        filtered_questions = questions_df[questions_df['Skill'].str.lower().isin([skill.lower() for skill in user_skills])]
        if filtered_questions.empty:
            return jsonify({"error": "No matching questions found for the user's skills."}), 500

        user_questions = []
        for skill in user_skills:
            skill_questions = filtered_questions[filtered_questions['Skill'].str.lower() == skill.lower()]
            if not skill_questions.empty:
                user_questions.append(skill_questions.sample(1).iloc[0])
            else:
                user_questions.append({
                    'Skill': skill,
                    'Question': f"What are the best practices for using {skill} in a production environment?",
                    'Answer': f"Best practices for {skill} include proper documentation, monitoring, and security measures."
                })
        user_questions = pd.DataFrame(user_questions).reset_index(drop=True)

        if len(user_questions) != len(user_skills):
            return jsonify({"error": f"Internal error: Number of selected questions ({len(user_questions)}) does not match number of skills ({len(user_skills)})."}), 500

        # Batch encode all user answers and expected answers
        user_answers = []
        expected_answers = []
        user_responses = []
        for idx, row in user_questions.iterrows():
            answer = answers[idx]
            if not answer or answer.lower() == 'skip':
                user_responses.append((row['Skill'], None, None, None, None))
            else:
                user_answers.append(answer)
                expected_answer = question_to_answer.get(row['Question'], "")
                expected_answers.append(expected_answer)
                user_responses.append((row['Skill'], answer, expected_answer, None, None))

        if user_answers:
            all_embeddings = universal_model.encode(user_answers + expected_answers, batch_size=128, convert_to_tensor=True)
            user_answer_embeddings = all_embeddings[:len(user_answers)]
            expected_answer_embeddings = all_embeddings[len(user_answers):]
            
            for idx, (skill, answer, expected, _, _) in enumerate(user_responses):
                if answer:
                    user_responses[idx] = (skill, answer, expected, user_answer_embeddings[idx], expected_answer_embeddings[idx])

        # Parallelize evaluation
        with Pool(processes=min(cpu_count(), 4)) as pool:
            eval_args = [response for response in user_responses if response[1]]
            results = pool.map(evaluate_response, eval_args)

        user_scores = {}
        ai_flags = {}
        scores_list = []
        skipped_questions = [f"{skill} ({question})" for skill, user_code, question, _, _ in user_responses if not user_code]
        for skill, score, is_ai in results:
            if skill in user_scores:
                user_scores[skill] = max(user_scores[skill], score)
                ai_flags[skill] = ai_flags[skill] or is_ai
            else:
                user_scores[skill] = score
                ai_flags[skill] = is_ai
            scores_list.append(score)

        mean_score = np.mean(scores_list) if scores_list else 50
        dynamic_threshold = max(40, mean_score)
        weak_skills = [skill for skill, score in user_scores.items() if score < dynamic_threshold]

        # Generate recommendations
        courses = recommend_courses(weak_skills or user_skills, user_level, upgrade=not weak_skills)
        jobs = recommend_jobs(user_skills, user_level)

        return jsonify({
            "assessment_results": {
                "skills": [
                    {
                        "skill": skill,
                        "progress": f"{'■' * int(score//10)}{'-' * (10 - int(score//10))}",
                        "score": f"{score:.2f} %",
                        "origin": "AI-Generated" if is_ai else "Human-Written"
                    } for skill, score, is_ai in results
                ],
                "mean_score": mean_score,
                "dynamic_threshold": dynamic_threshold,
                "weak_skills": weak_skills,
                "skipped_questions": skipped_questions
            },
            "recommended_courses": courses[:3],
            "recommended_jobs": jobs[:5]
        })
    except Exception as e:
        logger.error(f"Assessment error: {e}")
        return jsonify({"error": "Internal server error"}), 500

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=7860, threaded=True)