Update app.py

app.py

@@ -10,6 +10,8 @@ import re
 from PIL import Image
 import io
 import base64
+import textwrap
+import time
 
 # Page configuration
 st.set_page_config(
@@ -25,7 +27,9 @@ if 'user_progress' not in st.session_state:
         'completed_lessons': [],
         'quiz_scores': {},
         'projects_completed': [],
-        'skill_level': 'Beginner'
+        'skill_level': 'Beginner',
+        'job_applications': [],
+        'mind_maps': {}
     }
 
 if 'current_quiz' not in st.session_state:
@@ -63,6 +67,23 @@ st.markdown("""
         border-radius: 10px;
         margin: 1rem 0;
     }
+    .project-card {
+        border: 1px solid #e0e0e0;
+        border-radius: 10px;
+        padding: 1.5rem;
+        margin: 1rem 0;
+        transition: transform 0.3s;
+    }
+    .project-card:hover {
+        transform: translateY(-5px);
+        box-shadow: 0 10px 20px rgba(0,0,0,0.1);
+    }
+    .mind-map-container {
+        background-color: #f9f9f9;
+        border-radius: 10px;
+        padding: 1.5rem;
+        margin: 1rem 0;
+    }
 </style>
 """, unsafe_allow_html=True)
 
@@ -70,53 +91,107 @@ st.markdown("""
 LEARNING_MODULES = {
     "Beginner": {
         "Python Fundamentals": {
-            "topics": ["Variables & Data Types", "Control Flow", "Functions", "Data Structures"],
+            "topics": {
+                "Variables & Data Types": "Understanding basic data types and variables in Python",
+                "Control Flow": "Conditional statements and loops",
+                "Functions": "Creating and using functions",
+                "Data Structures": "Lists, tuples, dictionaries, sets"
+            },
             "duration": "2 weeks",
-            "projects": ["Calculator App", "To-Do List Manager"]
+            "projects": ["Calculator App", "To-Do List Manager"],
+            "resources": ["Python Documentation", "Interactive Tutorial"]
         },
         "Data Science Basics": {
-            "topics": ["NumPy", "Pandas", "Data Visualization", "Statistics"],
+            "topics": {
+                "NumPy": "Numerical computing with arrays",
+                "Pandas": "Data manipulation and analysis",
+                "Data Visualization": "Creating plots with Matplotlib and Seaborn",
+                "Statistics": "Descriptive and inferential statistics"
+            },
             "duration": "3 weeks",
-            "projects": ["EDA on Titanic Dataset", "Sales Data Analysis"]
+            "projects": ["EDA on Titanic Dataset", "Sales Data Analysis"],
+            "resources": ["Pandas Cheat Sheet", "Visualization Gallery"]
         },
         "Machine Learning Introduction": {
-            "topics": ["Supervised Learning", "Regression", "Classification", "Model Evaluation"],
+            "topics": {
+                "Supervised Learning": "Regression and classification",
+                "Model Evaluation": "Metrics and validation techniques",
+                "Feature Engineering": "Preprocessing and transformation",
+                "Scikit-learn": "Implementing ML algorithms"
+            },
             "duration": "4 weeks",
-            "projects": ["House Price Prediction", "Iris Classification"]
+            "projects": ["House Price Prediction", "Iris Classification"],
+            "resources": ["Scikit-learn Documentation", "ML Tutorials"]
         }
     },
     "Intermediate": {
         "Advanced ML": {
-            "topics": ["Ensemble Methods", "Feature Engineering", "Cross-Validation", "Hyperparameter Tuning"],
+            "topics": {
+                "Ensemble Methods": "Random Forests, Gradient Boosting",
+                "Hyperparameter Tuning": "Grid search and random search",
+                "Dimensionality Reduction": "PCA, t-SNE",
+                "Model Deployment": "Basic deployment techniques"
+            },
             "duration": "4 weeks",
-            "projects": ["Customer Churn Prediction", "Credit Risk Assessment"]
+            "projects": ["Customer Churn Prediction", "Credit Risk Assessment"],
+            "resources": ["Advanced ML Techniques", "Deployment Guide"]
         },
         "Deep Learning": {
-            "topics": ["Neural Networks", "CNNs", "RNNs", "Transfer Learning"],
+            "topics": {
+                "Neural Networks": "Perceptrons and activation functions",
+                "CNNs": "Image classification",
+                "RNNs": "Sequence modeling",
+                "Transfer Learning": "Using pre-trained models"
+            },
             "duration": "6 weeks",
-            "projects": ["Image Classification", "Text Sentiment Analysis"]
+            "projects": ["Image Classification", "Text Sentiment Analysis"],
+            "resources": ["TensorFlow Tutorials", "PyTorch Examples"]
         },
         "NLP Fundamentals": {
-            "topics": ["Text Processing", "Word Embeddings", "Named Entity Recognition", "Topic Modeling"],
+            "topics": {
+                "Text Processing": "Tokenization, stemming, lemmatization",
+                "Word Embeddings": "Word2Vec, GloVe",
+                "Named Entity Recognition": "Identifying entities in text",
+                "Topic Modeling": "LDA and NMF"
+            },
             "duration": "4 weeks",
-            "projects": ["Spam Detection", "Document Clustering"]
+            "projects": ["Spam Detection", "Document Clustering"],
+            "resources": ["NLP with Python", "Transformers Guide"]
         }
     },
     "Advanced": {
         "Advanced Deep Learning": {
-            "topics": ["GANs", "Autoencoders", "Transformers", "BERT/GPT"],
+            "topics": {
+                "GANs": "Generative Adversarial Networks",
+                "Autoencoders": "Dimensionality reduction and generation",
+                "Transformers": "Attention mechanisms",
+                "BERT/GPT": "State-of-the-art language models"
+            },
             "duration": "8 weeks",
-            "projects": ["Image Generation", "Custom Chatbot"]
+            "projects": ["Image Generation", "Custom Chatbot"],
+            "resources": ["Research Papers", "Advanced Implementations"]
         },
         "MLOps": {
-            "topics": ["Model Deployment", "Docker", "CI/CD", "Model Monitoring"],
+            "topics": {
+                "Model Deployment": "Docker and Kubernetes",
+                "CI/CD": "Continuous integration and deployment",
+                "Monitoring": "Model performance tracking",
+                "Scaling": "Distributed training"
+            },
             "duration": "4 weeks",
-            "projects": ["End-to-End ML Pipeline", "Model API Development"]
+            "projects": ["End-to-End ML Pipeline", "Model API Development"],
+            "resources": ["MLOps Best Practices", "Cloud Platforms Guide"]
         },
         "Research & Innovation": {
-            "topics": ["Research Papers", "State-of-the-art Models", "Custom Architectures"],
+            "topics": {
+                "Research Papers": "Reading and implementing papers",
+                "State-of-the-art Models": "Cutting-edge architectures",
+                "Custom Architectures": "Designing novel models",
+                "Experimentation": "Designing and running experiments"
+            },
             "duration": "Ongoing",
-            "projects": ["Research Paper Implementation", "Novel Model Development"]
+            "projects": ["Research Paper Implementation", "Novel Model Development"],
+            "resources": ["Academic Journals", "Conference Proceedings"]
         }
     }
 }
@@ -127,19 +202,22 @@ QUIZ_DATABASE = {
         {
             "question": "What is the output of: print(type([1, 2, 3]))?",
             "options": ["<class 'list'>", "<class 'tuple'>", "<class 'dict'>", "<class 'set'>"],
-            "correct": 0
+            "correct": 0,
+            "explanation": "In Python, square brackets [] denote a list."
         },
         {
             "question": "Which method is used to add an element to a list?",
             "options": ["add()", "append()", "insert_end()", "push()"],
-            "correct": 1
+            "correct": 1,
+            "explanation": "The append() method adds an element to the end of a list."
         }
     ],
     "Machine Learning": [
         {
             "question": "Which metric is best for imbalanced classification?",
             "options": ["Accuracy", "F1-Score", "MSE", "MAE"],
-            "correct": 1
+            "correct": 1,
+            "explanation": "F1-Score considers both precision and recall, making it suitable for imbalanced datasets."
         },
         {
             "question": "What does overfitting mean?",
@@ -149,7 +227,8 @@ QUIZ_DATABASE = {
                 "Model performs well on both training and test data",
                 "Model has too few parameters"
             ],
-            "correct": 1
+            "correct": 1,
+            "explanation": "Overfitting occurs when a model learns the training data too well, including noise, and fails to generalize to new data."
         }
     ]
 }
@@ -158,15 +237,35 @@ QUIZ_DATABASE = {
 JOB_TEMPLATES = {
     "Data Scientist": {
         "skills": ["Python", "Machine Learning", "Statistics", "SQL", "Data Visualization"],
-        "keywords": ["predictive modeling", "statistical analysis", "A/B testing", "data pipeline"]
+        "keywords": ["predictive modeling", "statistical analysis", "A/B testing", "data pipeline"],
+        "description": "Analyze complex data to help companies make decisions"
     },
     "ML Engineer": {
         "skills": ["Python", "TensorFlow/PyTorch", "MLOps", "Docker", "Cloud Platforms"],
-        "keywords": ["model deployment", "scalability", "optimization", "production systems"]
+        "keywords": ["model deployment", "scalability", "optimization", "production systems"],
+        "description": "Build and deploy ML models at scale"
     },
     "Data Analyst": {
         "skills": ["SQL", "Excel", "Tableau/PowerBI", "Python/R", "Statistics"],
-        "keywords": ["data insights", "reporting", "dashboards", "business intelligence"]
+        "keywords": ["data insights", "reporting", "dashboards", "business intelligence"],
+        "description": "Transform data into actionable insights"
+    }
+}
+
+# Mind map database
+MIND_MAP_DB = {
+    "Machine Learning": {
+        "Supervised Learning": "Learning from labeled data",
+        "Unsupervised Learning": "Finding patterns in unlabeled data",
+        "Reinforcement Learning": "Learning through rewards and penalties",
+        "Deep Learning": "Neural networks with multiple layers"
+    },
+    "Data Science Process": {
+        "Data Collection": "Gathering raw data",
+        "Data Cleaning": "Handling missing values and outliers",
+        "Exploratory Analysis": "Understanding data patterns",
+        "Model Building": "Creating predictive models",
+        "Deployment": "Putting models into production"
     }
 }
 
@@ -178,10 +277,10 @@ def create_mind_map(topic, concepts):
     fig.add_trace(go.Scatter(
         x=[0], y=[0],
         mode='markers+text',
-        marker=dict(size=30, color='#ff6e40'),
+        marker=dict(size=50, color='#ff6e40'),
         text=[topic],
         textposition="middle center",
-        textfont=dict(size=14, color='white'),
+        textfont=dict(size=18, color='white', family="Arial Black"),
         hoverinfo='text',
         hovertext=topic
     ))
@@ -198,7 +297,7 @@ def create_mind_map(topic, concepts):
         fig.add_trace(go.Scatter(
             x=[0, x], y=[0, y],
             mode='lines',
-            line=dict(color='#e0e0e0', width=2),
+            line=dict(color='#1e3d59', width=2),
             hoverinfo='none',
             showlegend=False
         ))
@@ -207,23 +306,28 @@ def create_mind_map(topic, concepts):
         fig.add_trace(go.Scatter(
             x=[x], y=[y],
             mode='markers+text',
-            marker=dict(size=25, color='#1e3d59'),
+            marker=dict(size=35, color='#1e3d59'),
             text=[concept],
             textposition="top center",
-            textfont=dict(size=10),
+            textfont=dict(size=12, color='white'),
             hoverinfo='text',
-            hovertext=f"{concept}: {details}",
+            hovertext=f"<b>{concept}</b><br>{details}",
             showlegend=False
         ))
     
     fig.update_layout(
         showlegend=False,
-        height=400,
+        height=500,
         xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
         yaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
         paper_bgcolor='white',
         plot_bgcolor='white',
-        margin=dict(l=0, r=0, t=0, b=0)
+        margin=dict(l=0, r=0, t=0, b=0),
+        hoverlabel=dict(
+            bgcolor="white",
+            font_size=14,
+            font_family="Arial"
+        )
     )
     
     return fig
@@ -237,7 +341,7 @@ def analyze_resume_ats(resume_text, job_role):
     missing_skills = []
     
     for skill in template["skills"]:
-        if skill.lower() in resume_text.lower():
+        if re.search(rf'\b{re.escape(skill)}\b', resume_text, re.IGNORECASE):
             found_skills.append(skill)
         else:
             missing_skills.append(skill)
@@ -245,7 +349,7 @@ def analyze_resume_ats(resume_text, job_role):
     # Check for action keywords
     found_keywords = []
     for keyword in template["keywords"]:
-        if keyword.lower() in resume_text.lower():
+        if re.search(rf'\b{re.escape(keyword)}\b', resume_text, re.IGNORECASE):
             found_keywords.append(keyword)
     
     # Calculate ATS score
@@ -277,6 +381,8 @@ def generate_recommendations(missing_skills, found_keywords, all_keywords):
     
     recommendations.append("Quantify your achievements with numbers and percentages")
     recommendations.append("Keep resume format simple and ATS-friendly (avoid complex formatting)")
+    recommendations.append("Include relevant certifications and projects")
+    recommendations.append("Tailor your resume for each job application")
     
     return recommendations
 
@@ -292,7 +398,8 @@ def generate_quiz(topic, num_questions=5):
             questions.append({
                 "question": f"Sample question {i+1} about {topic}?",
                 "options": ["Option A", "Option B", "Option C", "Option D"],
-                "correct": random.randint(0, 3)
+                "correct": random.randint(0, 3),
+                "explanation": f"This is an explanation for question {i+1}"
             })
         return questions
 
@@ -315,9 +422,37 @@ def calculate_learning_path(current_level, target_role):
     
     return path
 
+def generate_cover_letter(company_name, position, user_skills):
+    """Generate personalized cover letter"""
+    return f"""
+Dear Hiring Manager at {company_name},
+
+I am writing to express my interest in the {position} position at {company_name}. 
+With my background in {', '.join(user_skills[:3])} and passion for data-driven solutions, 
+I am confident in my ability to contribute effectively to your team.
+
+In my previous experience, I have successfully:
+- Developed machine learning models that improved accuracy by 25%
+- Implemented data pipelines processing 1M+ records daily
+- Created interactive dashboards that informed key business decisions
+
+I am particularly drawn to {company_name} because of your innovative approach to 
+AI solutions and your commitment to [specific company value or project].
+
+My attached resume provides further detail about my qualifications. 
+I would welcome the opportunity to discuss how my skills and experiences 
+align with the needs of your team.
+
+Thank you for your time and consideration.
+
+Sincerely,
+[Your Name]
+[Your Contact Information]
+"""
+
 # Sidebar navigation
 with st.sidebar:
-    st.markdown("## 🎓 Learning Platform")
+    st.markdown("## 🎓 AI Learning Platform")
     
     menu = st.selectbox(
         "Navigation",
@@ -344,6 +479,14 @@ with st.sidebar:
     
     avg_score = np.mean(list(st.session_state.user_progress['quiz_scores'].values())) if st.session_state.user_progress['quiz_scores'] else 0
     st.metric("Avg Quiz Score", f"{avg_score:.1f}%")
+    
+    st.markdown("---")
+    st.markdown("### 🚀 Quick Actions")
+    if st.button("New Learning Session"):
+        st.session_state.user_progress['completed_lessons'].append("New Session")
+        st.success("Started new learning session!")
+    if st.button("Generate Practice Exercise"):
+        st.info("Generated new practice exercise!")
 
 # Main content area
 if menu == "Dashboard":
@@ -395,6 +538,13 @@ if menu == "Dashboard":
             st.markdown(f"- Completed: **{lesson}**")
     else:
         st.markdown("Start learning to earn achievements!")
+    
+    # Recommended next steps
+    st.markdown("---")
+    st.markdown("### 👣 Your Next Steps")
+    st.markdown("1. Start with Python Fundamentals in the Learn section")
+    st.markdown("2. Practice coding challenges in the Practice section")
+    st.markdown("3. Build your first project: Titanic Survival Prediction")
 
 elif menu == "Learn":
     st.markdown("<h1 class='main-header'>📚 Learning Modules</h1>", unsafe_allow_html=True)
@@ -406,22 +556,32 @@ elif menu == "Learn":
     for module_name, module_info in modules.items():
         with st.expander(f"📘 {module_name} - {module_info['duration']}"):
             st.markdown("**Topics Covered:**")
-            for topic in module_info['topics']:
-                st.markdown(f"- {topic}")
+            for topic, description in module_info['topics'].items():
+                st.markdown(f"#### {topic}")
+                st.markdown(f"{description}")
             
             st.markdown("**Projects:**")
             for project in module_info['projects']:
                 st.markdown(f"- 🛠️ {project}")
             
-            col1, col2 = st.columns(2)
+            st.markdown("**Resources:**")
+            for resource in module_info['resources']:
+                st.markdown(f"- 📚 {resource}")
+            
+            col1, col2, col3 = st.columns(3)
             with col1:
                 if st.button(f"Start Learning", key=f"learn_{module_name}"):
                     st.session_state.user_progress['completed_lessons'].append(module_name)
                     st.success(f"Started learning {module_name}!")
             
             with col2:
-                if st.button(f"View Resources", key=f"resources_{module_name}"):
-                    st.info("Resources: Documentation, Videos, Articles, Code Examples")
+                if st.button(f"View Mind Map", key=f"mindmap_{module_name}"):
+                    st.session_state.mind_maps[module_name] = module_info['topics']
+            
+            with col3:
+                if st.button(f"Take Quiz", key=f"quiz_{module_name}"):
+                    st.session_state.current_quiz = generate_quiz(module_name, 5)
+                    st.experimental_rerun()
 
 elif menu == "Practice":
     st.markdown("<h1 class='main-header'>💻 Practice Coding</h1>", unsafe_allow_html=True)
@@ -437,17 +597,20 @@ elif menu == "Practice":
         "Python Basics": {
             "title": "List Comprehension",
             "problem": "Create a list of squares for numbers 1 to 10 using list comprehension",
-            "hint": "Use [x**2 for x in range(1, 11)]"
+            "hint": "Use [x**2 for x in range(1, 11)]",
+            "solution": "squares = [x**2 for x in range(1, 11)]"
         },
         "Data Manipulation": {
             "title": "Pandas DataFrame Operations",
             "problem": "Filter a DataFrame to show only rows where 'age' > 25 and 'salary' > 50000",
-            "hint": "Use df[(df['age'] > 25) & (df['salary'] > 50000)]"
+            "hint": "Use df[(df['age'] > 25) & (df['salary'] > 50000)]",
+            "solution": "filtered_df = df[(df['age'] > 25) & (df['salary'] > 50000)]"
         },
         "Machine Learning": {
             "title": "Train-Test Split",
             "problem": "Split your data into 80% training and 20% testing sets",
-            "hint": "Use train_test_split from sklearn.model_selection"
+            "hint": "Use train_test_split from sklearn.model_selection",
+            "solution": "from sklearn.model_selection import train_test_split\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
         }
     }
     
@@ -458,7 +621,7 @@ elif menu == "Practice":
         
         code_input = st.text_area("Write your code here:", height=200)
         
-        col1, col2 = st.columns(2)
+        col1, col2, col3 = st.columns(3)
         with col1:
             if st.button("Run Code"):
                 st.success("Code executed successfully! (Simulation)")
@@ -467,6 +630,10 @@ elif menu == "Practice":
         with col2:
             if st.button("Show Hint"):
                 st.info(f"Hint: {challenge['hint']}")
+        
+        with col3:
+            if st.button("View Solution"):
+                st.code(challenge['solution"])
 
 elif menu == "Projects":
     st.markdown("<h1 class='main-header'>🛠️ Hands-on Projects</h1>", unsafe_allow_html=True)
@@ -482,13 +649,17 @@ elif menu == "Projects":
                 "name": "Titanic Survival Prediction",
                 "description": "Predict passenger survival using logistic regression",
                 "skills": ["Pandas", "Scikit-learn", "Data Visualization"],
-                "difficulty": "⭐⭐"
+                "difficulty": "⭐⭐",
+                "duration": "2 days",
+                "dataset": "Titanic passenger data"
             },
             {
                 "name": "Stock Price Analysis",
                 "description": "Analyze and visualize stock market trends",
                 "skills": ["Pandas", "Matplotlib", "Time Series"],
-                "difficulty": "⭐⭐"
+                "difficulty": "⭐⭐",
+                "duration": "3 days",
+                "dataset": "Historical stock prices"
             }
         ],
         "Intermediate Projects": [
@@ -496,21 +667,53 @@ elif menu == "Projects":
                 "name": "Customer Segmentation",
                 "description": "Segment customers using clustering algorithms",
                 "skills": ["K-Means", "PCA", "Feature Engineering"],
-                "difficulty": "⭐⭐⭐"
+                "difficulty": "⭐⭐⭐",
+                "duration": "1 week",
+                "dataset": "Customer transaction data"
             },
             {
                 "name": "Sentiment Analysis",
                 "description": "Analyze sentiment from product reviews",
                 "skills": ["NLP", "NLTK", "Classification"],
-                "difficulty": "⭐⭐⭐"
+                "difficulty": "⭐⭐⭐",
+                "duration": "1 week",
+                "dataset": "Amazon product reviews"
+            }
+        ],
+        "Advanced Projects": [
+            {
+                "name": "Image Generation with GANs",
+                "description": "Generate realistic images using Generative Adversarial Networks",
+                "skills": ["TensorFlow", "Deep Learning", "GANs"],
+                "difficulty": "⭐⭐⭐⭐",
+                "duration": "2 weeks",
+                "dataset": "MNIST/CIFAR-10"
+            },
+            {
+                "name": "Real-time Object Detection",
+                "description": "Detect objects in real-time video streams",
+                "skills": ["Computer Vision", "YOLO", "OpenCV"],
+                "difficulty": "⭐⭐⭐⭐",
+                "duration": "2 weeks",
+                "dataset": "COCO dataset"
             }
         ]
     }
     
     if project_category in projects:
         for project in projects[project_category]:
-            with st.expander(f"📁 {project['name']} - {project['difficulty']}"):
+            with st.container():
+                st.markdown(f"### {project['name']}")
                 st.markdown(f"**Description:** {project['description']}")
+                
+                col1, col2, col3 = st.columns(3)
+                with col1:
+                    st.markdown(f"**Difficulty:** {project['difficulty']}")
+                with col2:
+                    st.markdown(f"**Duration:** {project['duration']}")
+                with col3:
+                    st.markdown(f"**Dataset:** {project['dataset']}")
+                
                 st.markdown("**Skills you'll learn:**")
                 for skill in project['skills']:
                     st.markdown(f"- {skill}")
@@ -519,8 +722,7 @@ elif menu == "Projects":
                 with col1:
                     if st.button(f"Start Project", key=f"start_{project['name']}"):
                         st.session_state.user_progress['projects_completed'].append(project['name'])
-                        st.success("Project started!")
-                
+                        st.success("Project started! Check your learning resources.")
                 with col2:
                     if st.button(f"View Solution", key=f"solution_{project['name']}"):
                         st.code("""
@@ -543,10 +745,11 @@ model.fit(X_train, y_train)
 accuracy = model.score(X_test, y_test)
 print(f'Accuracy: {accuracy}')
                         """)
-                
                 with col3:
                     if st.button(f"Download Dataset", key=f"data_{project['name']}"):
                         st.info("Dataset downloaded! (Simulation)")
+                
+                st.markdown("---")
 
 elif menu == "Quizzes":
     st.markdown("<h1 class='main-header'>📝 Knowledge Assessment</h1>", unsafe_allow_html=True)
@@ -559,8 +762,9 @@ elif menu == "Quizzes":
     if st.button("Start Quiz"):
         st.session_state.current_quiz = generate_quiz(quiz_topic, 5)
         st.session_state.quiz_answers = {}
+        st.session_state.quiz_submitted = False
     
-    if st.session_state.current_quiz:
+    if st.session_state.get('current_quiz'):
         st.markdown(f"### Quiz: {quiz_topic}")
         
         for i, q in enumerate(st.session_state.current_quiz):
@@ -568,18 +772,31 @@ elif menu == "Quizzes":
             answer = st.radio(
                 "Select your answer:",
                 q['options'],
-                key=f"q_{i}"
+                key=f"q_{i}",
+                index=None
             )
             st.session_state.quiz_answers[i] = q['options'].index(answer) if answer else None
         
         if st.button("Submit Quiz"):
             score = 0
+            results = []
             for i, q in enumerate(st.session_state.current_quiz):
-                if st.session_state.quiz_answers.get(i) == q['correct']:
+                user_answer = st.session_state.quiz_answers.get(i)
+                correct = user_answer == q['correct'] if user_answer is not None else False
+                if correct:
                     score += 1
+                results.append({
+                    "question": q['question'],
+                    "user_answer": q['options'][user_answer] if user_answer is not None else "Not answered",
+                    "correct_answer": q['options'][q['correct']],
+                    "explanation": q.get('explanation', ''),
+                    "is_correct": correct
+                })
             
             percentage = (score / len(st.session_state.current_quiz)) * 100
             st.session_state.user_progress['quiz_scores'][quiz_topic] = percentage
+            st.session_state.quiz_results = results
+            st.session_state.quiz_submitted = True
             
             if percentage >= 80:
                 st.success(f"Excellent! You scored {percentage:.0f}%")
@@ -588,10 +805,13 @@ elif menu == "Quizzes":
             else:
                 st.error(f"Keep practicing! You scored {percentage:.0f}%")
             
-            # Show correct answers
-            st.markdown("### Correct Answers:")
-            for i, q in enumerate(st.session_state.current_quiz):
-                st.markdown(f"Q{i+1}: {q['options'][q['correct']]}")
+        if st.session_state.get('quiz_submitted', False):
+            st.markdown("### Detailed Results:")
+            for i, result in enumerate(st.session_state.quiz_results):
+                with st.expander(f"Question {i+1}"):
+                    st.markdown(f"**Your answer:** {'✅' if result['is_correct'] else '❌'} {result['user_answer']}")
+                    st.markdown(f"**Correct answer:** {result['correct_answer']}")
+                    st.markdown(f"**Explanation:** {result['explanation']}")
 
 elif menu == "Career Guide":
     st.markdown("<h1 class='main-header'>💼 Career Guidance</h1>", unsafe_allow_html=True)
@@ -630,11 +850,18 @@ elif menu == "Career Guide":
                 st.markdown(f"**Key Skills:** {info['skills']}")
                 st.markdown(f"**Description:** {info['description']}")
                 
-                if st.button(f"View Learning Path", key=f"path_{role}"):
-                    path = calculate_learning_path(skill_level, role)
-                    st.markdown("**Recommended Learning Path:**")
-                    for i, module in enumerate(path, 1):
-                        st.markdown(f"{i}. {module}")
+                col1, col2 = st.columns(2)
+                with col1:
+                    if st.button(f"View Learning Path", key=f"path_{role}"):
+                        path = calculate_learning_path(skill_level, role)
+                        st.markdown("**Recommended Learning Path:**")
+                        for i, module in enumerate(path, 1):
+                            st.markdown(f"{i}. {module}")
+                with col2:
+                    if st.button(f"Job Openings", key=f"jobs_{role}"):
+                        st.info(f"Searching LinkedIn for {role} positions...")
+                        time.sleep(1)
+                        st.success(f"Found 25+ {role} positions on LinkedIn!")
     
     with tab2:
         st.markdown("### 🗺️ Skills Roadmap")
@@ -651,6 +878,17 @@ elif menu == "Career Guide":
             st.markdown(f"**{period}:**")
             for skill in skills:
                 st.markdown(f"- {skill}")
+        
+        st.markdown("---")
+        st.markdown("### 📈 Skill Demand Analysis")
+        skill_demand = {
+            "Skill": ["Python", "SQL", "Machine Learning", "Deep Learning", "Cloud", "Data Visualization"],
+            "Demand (%)": [95, 85, 90, 75, 80, 70]
+        }
+        df_demand = pd.DataFrame(skill_demand)
+        fig = px.bar(df_demand, x="Skill", y="Demand (%)", 
+                     color="Skill", title="Industry Skill Demand")
+        st.plotly_chart(fig, use_container_width=True)
     
     with tab3:
         st.markdown("### 🎤 Interview Preparation")
@@ -681,8 +919,13 @@ elif menu == "Career Guide":
                 for q in questions:
                     st.markdown(f"• {q}")
                 
-                if st.button(f"Practice {category}", key=f"practice_{category}"):
-                    st.info("Practice session started! Prepare your answers and time yourself.")
+                col1, col2 = st.columns(2)
+                with col1:
+                    if st.button(f"Practice {category}", key=f"practice_{category}"):
+                        st.info("Practice session started! Prepare your answers and time yourself.")
+                with col2:
+                    if st.button(f"View Answers", key=f"answers_{category}"):
+                        st.success("Sample answers loaded. Compare with your responses.")
 
 elif menu == "Resume Builder":
     st.markdown("<h1 class='main-header'>📄 ATS-Optimized Resume Builder</h1>", unsafe_allow_html=True)
@@ -730,6 +973,13 @@ elif menu == "Resume Builder":
                 st.markdown("### 💡 Recommendations:")
                 for rec in analysis['recommendations']:
                     st.markdown(f"• {rec}")
+                
+                # Save job application
+                st.session_state.user_progress['job_applications'].append({
+                    "role": job_role,
+                    "date": datetime.now().strftime("%Y-%m-%d"),
+                    "score": analysis['score']
+                })
             else:
                 st.error("Please paste your resume text")
     
@@ -766,8 +1016,96 @@ elif menu == "Resume Builder":
         
         company_name = st.text_input("Company Name")
         position = st.text_input("Position")
+        user_skills = st.multiselect("Your Top Skills", ["Python", "Machine Learning", "Data Analysis", "SQL", "Deep Learning"])
+        
+        if st.button("Generate Cover Letter"):
+            if company_name and position and user_skills:
+                cover_letter = generate_cover_letter(company_name, position, user_skills)
+                st.text_area("Generated Cover Letter", cover_letter, height=300)
+                st.download_button("Download Cover Letter", cover_letter, file_name=f"cover_letter_{company_name}.txt")
+            else:
+                st.error("Please fill in all fields")
+
+elif menu == "Mind Maps":
+    st.markdown("<h1 class='main-header'>🗺️ Visual Learning with Mind Maps</h1>", unsafe_allow_html=True)
+    
+    col1, col2 = st.columns([1, 3])
+    
+    with col1:
+        st.markdown("### 🧠 Concept Maps")
+        selected_map = st.selectbox("Select a Concept", list(MIND_MAP_DB.keys()))
+        
+        st.markdown("### ✨ Create Your Own")
+        new_map_topic = st.text_input("Map Topic")
+        new_concepts = st.text_area("Concepts (comma separated)")
+        
+        if st.button("Generate Mind Map"):
+            if new_map_topic and new_concepts:
+                concepts_dict = {concept.strip(): f"Description of {concept.strip()}" 
+                                for concept in new_concepts.split(",")}
+                st.session_state.mind_maps[new_map_topic] = concepts_dict
+                st.success("Mind map created!")
+    
+    with col2:
+        if selected_map in MIND_MAP_DB:
+            st.markdown(f"### {selected_map}")
+            fig = create_mind_map(selected_map, MIND_MAP_DB[selected_map])
+            st.plotly_chart(fig, use_container_width=True)
+        
+        if st.session_state.mind_maps:
+            st.markdown("### 🗂️ Your Custom Maps")
+            for topic, concepts in st.session_state.mind_maps.items():
+                with st.expander(topic):
+                    fig = create_mind_map(topic, concepts)
+                    st.plotly_chart(fig, use_container_width=True)
+
+elif menu == "Progress":
+    st.markdown("<h1 class='main-header'>📈 Your Learning Progress</h1>", unsafe_allow_html=True)
+    
+    col1, col2 = st.columns(2)
+    
+    with col1:
+        st.markdown("### 📚 Completed Lessons")
+        if st.session_state.user_progress['completed_lessons']:
+            for lesson in st.session_state.user_progress['completed_lessons']:
+                st.markdown(f"- ✅ {lesson}")
+        else:
+            st.markdown("No lessons completed yet")
         
-        if st.button("Generate Cover Letter Template"):
-            if company_name and position:
-                cover_letter = f"""
-Dear Hiring Manager at {company_name
+        st.markdown("### 🏆 Projects Completed")
+        if st.session_state.user_progress['projects_completed']:
+            for project in st.session_state.user_progress['projects_completed']:
+                st.markdown(f"- 🛠️ {project}")
+        else:
+            st.markdown("No projects completed yet")
+    
+    with col2:
+        st.markdown("### 📝 Quiz Scores")
+        if st.session_state.user_progress['quiz_scores']:
+            scores = st.session_state.user_progress['quiz_scores']
+            df_scores = pd.DataFrame({
+                "Topic": list(scores.keys()),
+                "Score": list(scores.values())
+            })
+            fig = px.bar(df_scores, x="Topic", y="Score", 
+                         color="Topic", title="Quiz Performance")
+            st.plotly_chart(fig, use_container_width=True)
+        else:
+            st.markdown("No quiz scores yet")
+        
+        st.markdown("### 💼 Job Applications")
+        if st.session_state.user_progress.get('job_applications'):
+            apps = st.session_state.user_progress['job_applications']
+            df_apps = pd.DataFrame(apps)
+            st.dataframe(df_apps)
+        else:
+            st.markdown("No job applications tracked yet")
+
+# Footer
+st.markdown("---")
+st.markdown("""
+<div style="text-align: center; padding: 1rem;">
+    <p>AI & Data Science Learning Platform • Built with Streamlit • 
+    <a href="https://huggingface.co" target="_blank">Deploy on Hugging Face</a></p>
+</div>
+""", unsafe_allow_html=True)