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 sklearn.feature_extraction.text import TfidfVectorizer
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")
ANSWER_EMBEDDINGS_PATH = os.path.join(chosen_model_dir, "answer_embeddings.pkl")
COURSE_SIMILARITY_PATH = os.path.join(chosen_model_dir, "course_similarity.pkl")
JOB_SIMILARITY_PATH = os.path.join(chosen_model_dir, "job_similarity.pkl")

# Global variables for precomputed data
tfidf_vectorizer = None
skill_tfidf = None
question_to_answer = None
faiss_index = None
answer_embeddings = None
course_similarity = None
job_similarity = None

# Improved dataset loading with fallback
def load_dataset(file_path, required_columns=[], additional_columns=['popularity', 'completion_rate'], fallback_data=None):
    try:
        df = pd.read_csv(file_path)
        missing_required = [col for col in required_columns if col not in df.columns]
        missing_additional = [col for col in additional_columns if col not in df.columns]
        
        # Handle missing required columns
        if missing_required:
            logger.warning(f"Required columns {missing_required} missing in {file_path}. Adding empty values.")
            for col in missing_required:
                df[col] = ""
        
        # Handle missing additional columns (popularity, completion_rate, etc.)
        if missing_additional:
            logger.warning(f"Additional columns {missing_additional} missing in {file_path}. Adding default values.")
            for col in missing_additional:
                if col == 'popularity':
                    df[col] = 0.8  # Default value for popularity
                elif col == 'completion_rate':
                    df[col] = 0.7  # Default value for completion_rate
                else:
                    df[col] = 0.0  # Default for other additional columns
        
        # Ensure 'level' column has valid values (not empty)
        if 'level' in df.columns:
            df['level'] = df['level'].apply(lambda x: 'Intermediate' if pd.isna(x) or x.strip() == "" else x)
        else:
            logger.warning(f"'level' column missing in {file_path}. Adding default 'Intermediate'.")
            df['level'] = 'Intermediate'
        
        return df
    except ValueError as ve:
        logger.error(f"ValueError loading {file_path}: {ve}. Using fallback data.")
        if fallback_data is not None:
            logger.info(f"Using fallback data for {file_path}")
            return pd.DataFrame(fallback_data)
        return None
    except Exception as e:
        logger.error(f"Error loading {file_path}: {e}. Using fallback data.")
        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"], ['popularity', 'completion_rate'], {
    '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 with Fallback
def load_universal_model():
    default_model = "all-MiniLM-L6-v2"
    try:
        if os.path.exists(UNIVERSAL_MODEL_PATH):
            logger.info(f"Loading universal model from {UNIVERSAL_MODEL_PATH}")
            return SentenceTransformer(UNIVERSAL_MODEL_PATH)
        else:
            logger.info(f"Loading universal model: {default_model}")
            model = SentenceTransformer(default_model)
            model.save(UNIVERSAL_MODEL_PATH)
            return model
    except Exception as e:
        logger.error(f"Failed to load universal model {default_model}: {e}. Exiting.")
        exit(1)

universal_model = load_universal_model()

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")

# Load Precomputed Resources
def load_precomputed_resources():
    global tfidf_vectorizer, skill_tfidf, question_to_answer, faiss_index, answer_embeddings, course_similarity, job_similarity
    if all(os.path.exists(p) for p in [TFIDF_PATH, SKILL_TFIDF_PATH, QUESTION_ANSWER_PATH, FAISS_INDEX_PATH, ANSWER_EMBEDDINGS_PATH, COURSE_SIMILARITY_PATH, JOB_SIMILARITY_PATH]):
        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(ANSWER_EMBEDDINGS_PATH, 'rb') as f: answer_embeddings = pickle.load(f)
            with open(COURSE_SIMILARITY_PATH, 'rb') as f: course_similarity = pickle.load(f)
            with open(JOB_SIMILARITY_PATH, 'rb') as f: job_similarity = pickle.load(f)
            logger.info("Loaded precomputed resources successfully")
        except Exception as e:
            logger.error(f"Error loading precomputed resources: {e}")
            precompute_resources()
    else:
        precompute_resources()

# Precompute Resources Offline (to be run separately)
def precompute_resources():
    global tfidf_vectorizer, skill_tfidf, question_to_answer, faiss_index, answer_embeddings, course_similarity, job_similarity
    logger.info("Precomputing resources offline")
    try:
        tfidf_vectorizer = TfidfVectorizer(stop_words='english')
        all_texts = questions_df['Answer'].tolist() + questions_df['Question'].tolist()
        tfidf_vectorizer.fit(all_texts)
        
        skill_tfidf = {skill.lower(): tfidf_vectorizer.transform([skill]).toarray()[0] for skill in questions_df['Skill'].unique()}
        question_to_answer = dict(zip(questions_df['Question'], questions_df['Answer']))
        answer_embeddings = universal_model.encode(questions_df['Answer'].tolist(), batch_size=128, convert_to_tensor=True, device="cuda" if torch.cuda.is_available() else "cpu").cpu().numpy()
        
        faiss_index = faiss.IndexFlatL2(answer_embeddings.shape[1])
        faiss_index.add(answer_embeddings)
        
        # Precompute course similarities
        course_skills = courses_df['skills'].fillna("").tolist()
        course_embeddings = universal_model.encode(course_skills, batch_size=128, convert_to_tensor=True, device="cuda" if torch.cuda.is_available() else "cpu")
        skill_embeddings = universal_model.encode(questions_df['Skill'].unique().tolist(), batch_size=128, convert_to_tensor=True, device="cuda" if torch.cuda.is_available() else "cpu")
        course_similarity = util.pytorch_cos_sim(skill_embeddings, course_embeddings).cpu().numpy()
        
        # Precompute job similarities
        job_skills = jobs_df['required_skills'].fillna("").tolist()
        job_embeddings = universal_model.encode(job_skills, batch_size=128, convert_to_tensor=True, device="cuda" if torch.cuda.is_available() else "cpu")
        job_similarity = util.pytorch_cos_sim(skill_embeddings, job_embeddings).cpu().numpy()
        
        # Save precomputed 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(ANSWER_EMBEDDINGS_PATH, 'wb') as f: pickle.dump(answer_embeddings, f)
        with open(COURSE_SIMILARITY_PATH, 'wb') as f: pickle.dump(course_similarity, f)
        with open(JOB_SIMILARITY_PATH, 'wb') as f: pickle.dump(job_similarity, f)
        universal_model.save(UNIVERSAL_MODEL_PATH)
        logger.info(f"Precomputed resources saved to {chosen_model_dir}")
    except Exception as e:
        logger.error(f"Error during precomputation: {e}")
        raise

# Evaluation with precomputed data
def evaluate_response(args):
    try:
        skill, user_answer, question_idx = 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

        user_embedding = universal_model.encode([user_answer], batch_size=128, convert_to_tensor=True, device="cuda" if torch.cuda.is_available() else "cpu")[0]
        expected_embedding = torch.tensor(answer_embeddings[question_idx])
        score = util.pytorch_cos_sim(user_embedding, expected_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

# Course recommendation with precomputed similarity
def recommend_courses(skills_to_improve, user_level, upgrade=False):
    try:
        if not skills_to_improve or courses_df.empty:
            logger.info("No skills to improve or courses_df is empty.")
            return []

        skill_indices = [list(questions_df['Skill'].unique()).index(skill) for skill in skills_to_improve if skill in questions_df['Skill'].unique()]
        if not skill_indices:
            logger.info("No matching skill indices found.")
            return []

        similarities = course_similarity[skill_indices]
        # Use default arrays to avoid KeyError
        popularity = courses_df['popularity'].values if 'popularity' in courses_df else np.full(len(courses_df), 0.8)
        completion_rate = courses_df['completion_rate'].values if 'completion_rate' in courses_df else np.full(len(courses_df), 0.7)
        total_scores = 0.6 * np.max(similarities, axis=0) + 0.2 * popularity + 0.2 * completion_rate

        target_level = 'Advanced' if upgrade else user_level
        idx = np.argsort(-total_scores)[:5]
        candidates = courses_df.iloc[idx]
        
        # Filter by level, but fallback to all courses if none match
        filtered_candidates = candidates[candidates['level'].str.contains(target_level, case=False, na=False)]
        if filtered_candidates.empty:
            logger.warning(f"No courses found for level {target_level}. Returning top courses regardless of level.")
            filtered_candidates = candidates
        
        return filtered_candidates[['course_title', 'Organization']].values.tolist()[:3]
    except Exception as e:
        logger.error(f"Course recommendation error: {e}")
        return []

# Job recommendation with precomputed similarity
def recommend_jobs(user_skills, user_level):
    try:
        if jobs_df.empty:
            return []

        skill_indices = [list(questions_df['Skill'].unique()).index(skill) for skill in user_skills if skill in questions_df['Skill'].unique()]
        if not skill_indices:
            return []

        similarities = job_similarity[skill_indices]
        total_scores = 0.5 * np.max(similarities, axis=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)
        total_job_scores = total_scores + 0.2 * level_scores + 0.1 * location_pref
        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

        load_precomputed_resources()  # Load precomputed resources before processing

        user_questions = []
        for skill in user_skills:
            skill_questions = questions_df[questions_df['Skill'] == skill]
            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)

        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))
            else:
                question_idx = questions_df.index[questions_df['Question'] == row['Question']][0]
                user_responses.append((row['Skill'], answer, question_idx))

        results = [evaluate_response(response) for response in user_responses]

        user_scores = {}
        ai_flags = {}
        scores_list = []
        skipped_questions = [f"{skill} ({question})" for skill, user_code, _ 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]

        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)