Update app.py

app.py

@@ -1,175 +1,399 @@
-import streamlit as st
+import os
 import pandas as pd
-import numpy as np
-import pymongo
-from sentence_transformers import SentenceTransformer
+import torch
+from sentence_transformers import SentenceTransformer, util
 import faiss
+import numpy as np
 import pickle
-import os
-from dotenv import load_dotenv
-
-# Load environment variables
-load_dotenv()
-MONGO_URI = os.getenv("MONGO_URI", "mongodb://muhammadbinimran1001:[email protected]:27017,dsm-shard-00-01.inrzs.mongodb.net:27017,dsm-shard-00-02.inrzs.mongodb.net:27017/?ssl=true&replicaSet=atlas-nbg4er-shard-0&authSource=admin&retryWrites=true&w=majority")
-
-# Connect to MongoDB
-client = pymongo.MongoClient(MONGO_URI)
-db = client['test']
-users_collection = db['users']
-jobs_collection = db['jobs']
-courses_collection = db['courses']
-
-# Load datasets
-@st.cache_data
-def load_datasets():
-    questions_df = pd.read_csv("Generated_Skill-Based_Questions.csv")
-    courses_df = pd.read_csv("coursera_course_dataset_v2_no_null.csv")
-    jobs_df = pd.read_csv("Updated_Job_Posting_Dataset.csv")
-    return questions_df, courses_df, jobs_df
-
-questions_df, courses_df, jobs_df = load_datasets()
-
-# Load precomputed resources
-@st.cache_resource
-def load_resources():
-    universal_model = SentenceTransformer("all-MiniLM-L6-v2")
-    with open("tfidf_vectorizer.pkl", "rb") as f: tfidf_vectorizer = pickle.load(f)
-    with open("skill_tfidf.pkl", "rb") as f: skill_tfidf = pickle.load(f)
-    with open("question_to_answer.pkl", "rb") as f: question_to_answer = pickle.load(f)
-    faiss_index = faiss.read_index("faiss_index.index")
-    with open("answer_embeddings.pkl", "rb") as f: answer_embeddings = pickle.load(f)
-    with open("course_similarity.pkl", "rb") as f: course_similarity = pickle.load(f)
-    with open("job_similarity.pkl", "rb") as f: job_similarity = pickle.load(f)
-    return universal_model, tfidf_vectorizer, skill_tfidf, question_to_answer, faiss_index, answer_embeddings, course_similarity, job_similarity
-
-universal_model, tfidf_vectorizer, skill_tfidf, question_to_answer, faiss_index, answer_embeddings, course_similarity, job_similarity = load_resources()
-
-# Evaluate response
-def evaluate_response(skill, user_answer, question_idx):
-    if not user_answer or user_answer.lower() == "skip":
-        return skill, 0.0
-    user_embedding = universal_model.encode([user_answer])[0]
-    expected_embedding = answer_embeddings[question_idx]
-    score = np.dot(user_embedding, expected_embedding) / (np.linalg.norm(user_embedding) * np.linalg.norm(expected_embedding) + 1e-10) * 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)
-    return skill, max(0, score * max(0.5, min(1.0, relevance)))
-
-# Recommend courses
+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):
-    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:
+    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 []
-    similarities = course_similarity[skill_indices]
-    popularity = courses_df['popularity'].fillna(0.8).values
-    completion_rate = courses_df['completion_rate'].fillna(0.7).values
-    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]
-    filtered = candidates[candidates['level'].str.contains(target_level, case=False, na=False)]
-    return filtered[['course_title', 'Organization']].values.tolist()[:3] if not filtered.empty else candidates[['course_title', 'Organization']].values.tolist()[:3]
-
-# Recommend jobs
+
+# Job recommendation with precomputed similarity
 def recommend_jobs(user_skills, user_level):
-    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:
+    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 []
-    similarities = job_similarity[skill_indices]
-    total_scores = 0.5 * np.max(similarities, axis=0)
-    level_map = {'Beginner': 0, 'Intermediate': 1, 'Advanced': 2}
-    user_level_num = level_map.get(user_level, 1)
-    level_scores = jobs_df['level'].apply(lambda x: 1 - abs(level_map.get(x, 1) - user_level_num)/2).fillna(0.5)
-    location_pref = jobs_df['location'].apply(lambda x: 1.0 if x in ['Islamabad', 'Karachi'] else 0.7).fillna(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]
-
-# Streamlit UI
-st.title("Skill Assessment and Recommendations")
-
-# Simulate user signup and skill extraction
-if 'user_skills' not in st.session_state:
-    st.session_state.user_skills = []
-    st.session_state.user_level = "Intermediate"
-
-with st.form("signup_form"):
-    name = st.text_input("Name")
-    email = st.text_input("Email")
-    skills_input = st.text_area("Enter your skills (comma-separated)")
-    submit = st.form_submit_button("Sign Up")
-    if submit and name and email and skills_input:
-        st.session_state.user_skills = [s.strip() for s in skills_input.split(",") if s.strip()]
-        user_data = {
-            "name": name,
-            "email": email,
-            "skills": st.session_state.user_skills,
-            "createdAt": pd.Timestamp.now(),
-            "lastLogin": pd.Timestamp.now()
-        }
-        users_collection.insert_one(user_data)
-        st.success("User registered successfully!")
-
-# Skill Assessment
-if st.session_state.user_skills:
-    st.write("### Skill Assessment")
-    user_questions = []
-    for skill in st.session_state.user_skills:
-        skill_questions = questions_df[questions_df['Skill'] == skill]
-        if not skill_questions.empty:
-            user_questions.append(skill_questions.sample(1).iloc[0])
-    user_questions = pd.DataFrame(user_questions).reset_index(drop=True)
-
-    answers = {}
-    with st.form("assessment_form"):
-        for idx, row in user_questions.iterrows():
-            answers[row['Question']] = st.text_area(f"Question for {row['Skill']}: {row['Question']}", key=f"q_{idx}")
-        submit_assessment = st.form_submit_button("Submit Assessment")
-    
-    if submit_assessment:
-        scores = {}
+
+# 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():
-            question_idx = questions_df.index[questions_df['Question'] == row['Question']][0]
-            skill, score = evaluate_response(row['Skill'], answers.get(row['Question'], ""), question_idx)
-            scores[skill] = max(scores.get(skill, 0), score)
-        
-        mean_score = np.mean(list(scores.values())) if scores else 50
+            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 scores.items() if score < dynamic_threshold]
-
-        st.session_state.scores = scores
-        st.session_state.weak_skills = weak_skills
-        st.session_state.mean_score = mean_score
-
-        # Update user scores in MongoDB
-        user = users_collection.find_one({"email": email})
-        if user:
-            users_collection.update_one(
-                {"_id": user["_id"]},
-                {"$set": {"skills_scores": scores}}
-            )
-
-    if 'scores' in st.session_state:
-        st.write("### Assessment Results")
-        for skill, score in st.session_state.scores.items():
-            st.write(f"{skill}: {score:.2f}%")
-        st.write(f"Mean Score: {st.session_state.mean_score:.2f}%")
-        st.write(f"Weak Skills: {', '.join(st.session_state.weak_skills)}")
-
-        # Recommendations
-        st.write("### Recommended Courses")
-        courses = recommend_courses(st.session_state.weak_skills or st.session_state.user_skills, st.session_state.user_level)
-        for course in courses:
-            st.write(f"- {course[0]} by {course[1]}")
-
-        st.write("### Recommended Jobs")
-        jobs = recommend_jobs(st.session_state.user_skills, st.session_state.user_level)
-        for job in jobs:
-            st.write(f"- {job[0]} at {job[1]} ({job[2]})")
-
-# Run the app
-if __name__ == "__main__":
-    st.set_page_config(layout="wide")
+        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)