diff --git a/Domains/AI-ML/app.py b/Domains/AI-ML/app.py
new file mode 100644
index 00000000..a096b468
--- /dev/null
+++ b/Domains/AI-ML/app.py
@@ -0,0 +1,530 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+import plotly.express as px
+import plotly.graph_objects as go
+import datetime
+import os
+
+# Add these imports for PDF export
+import io
+try:
+    import pdfkit
+except ImportError:
+    pdfkit = None
+
+# Optional: Uncomment if you want to use OpenAI for dynamic chat responses
+# import openai
+
+# ---------------------------------------
+# 🌐 PAGE CONFIG
+# ---------------------------------------
+st.set_page_config(
+    page_title="Faculty Effectiveness Analyzer",
+    page_icon="📊",
+    layout="wide",
+    initial_sidebar_state="collapsed"
+)
+
+# ---------------------------------------
+# 👩‍🏫 FACULTY DATA (with manual add section)
+# ---------------------------------------
+def save_faculty_profiles(profiles):
+    pd.DataFrame(profiles).to_json("faculty_profiles.json")
+
+def load_faculty_profiles():
+    if os.path.exists("faculty_profiles.json"):
+        return pd.read_json("faculty_profiles.json", typ='dict')
+    else:
+        return None
+
+profiles_from_file = load_faculty_profiles()
+if profiles_from_file:
+    faculty_profiles = profiles_from_file
+else:
+    faculty_profiles = {
+        "Dr. John Doe": {
+            "image": "https://cdn-icons-png.flaticon.com/512/3135/3135715.png",
+            "rating": "4.8 / 5",
+            "experience": "12 Years",
+            "designation": "Associate Professor",
+            "department": "Computer Engineering",
+            "institution": "ABC Institute of Technology",
+            "about": "Passionate about building real-world technical skills and improving student outcomes through interactive learning.",
+            "expertise": ["Machine Learning", "HCI", "Operating Systems"],
+            "email": "johndoe@abc.edu",
+            "id": "CSE-1023",
+            "achievements": [
+                "Best Teacher Award 2022",
+                "Published 10+ research papers",
+                "Invited Speaker at MLConf 2024"
+            ]
+        },
+        "Prof. Meera Sharma": {
+            "image": "https://cdn-icons-png.flaticon.com/512/4140/4140048.png",
+            "rating": "4.5 / 5",
+            "experience": "8 Years",
+            "designation": "Assistant Professor",
+            "department": "Information Technology",
+            "institution": "XYZ College of Engineering",
+            "about": "Focused on student engagement and improving conceptual understanding.",
+            "expertise": ["Cloud Computing", "IoT", "Networking"],
+            "email": "meerasharma@xyz.edu",
+            "id": "IT-1122",
+            "achievements": [
+                "Young Researcher Award 2021",
+                "Organized IoT Symposium 2023"
+            ]
+        },
+        "Dr. A. Sharma": {
+            "image": "https://cdn-icons-png.flaticon.com/512/4140/4140037.png",
+            "rating": "4.9 / 5",
+            "experience": "15 Years",
+            "designation": "Professor",
+            "department": "AI & Data Science",
+            "institution": "Global Tech University",
+            "about": "Known for delivering complex topics with clarity and adaptive teaching methods.",
+            "expertise": ["AI", "Data Science", "Deep Learning"],
+            "email": "asharma@gtu.edu",
+            "id": "AIDS-1007",
+            "achievements": [
+                "AI Excellence Award 2023",
+                "Published 20+ journal articles"
+            ]
+        }
+    }
+
+# ---------------------------------------
+# SIDEBAR: Manual Faculty Add Section
+# ---------------------------------------
+st.sidebar.markdown("### ➕ Add New Faculty Profile")
+with st.sidebar.form("add_faculty_form"):
+    new_name = st.text_input("Full Name")
+    new_email = st.text_input("Email")
+    new_id = st.text_input("Faculty ID")
+    new_designation = st.text_input("Designation")
+    new_department = st.text_input("Department")
+    new_institution = st.text_input("Institution")
+    new_experience = st.text_input("Experience (e.g. 5 Years)")
+    new_rating = st.text_input("Rating (e.g. 4.5 / 5)")
+    new_about = st.text_area("About")
+    new_expertise = st.text_input("Expertise (comma separated)")
+    new_image = st.text_input("Image URL (optional)", value="https://cdn-icons-png.flaticon.com/512/3135/3135715.png")
+    new_achievements = st.text_area("Achievements (one per line)")
+    add_faculty_btn = st.form_submit_button("Add Faculty")
+
+    if add_faculty_btn and new_name:
+        faculty_profiles[new_name] = {
+            "image": new_image,
+            "rating": new_rating,
+            "experience": new_experience,
+            "designation": new_designation,
+            "department": new_department,
+            "institution": new_institution,
+            "about": new_about,
+            "expertise": [e.strip() for e in new_expertise.split(",") if e.strip()],
+            "email": new_email,
+            "id": new_id,
+            "achievements": [a.strip() for a in new_achievements.split("\n") if a.strip()]
+        }
+        save_faculty_profiles(faculty_profiles)
+        st.sidebar.success(f"Faculty '{new_name}' added! Please refresh to see in the list.")
+
+selected_faculty = st.sidebar.selectbox("👩‍🏫 Select Faculty", list(faculty_profiles.keys()))
+faculty = faculty_profiles[selected_faculty]
+
+# ---------------------------------------
+# 🧠 SIMULATED DATA GENERATION (PER FACULTY)
+# ---------------------------------------
+def generate_day(seed):
+    np.random.seed(seed)
+    activities = ['Instruction', 'Research', 'Admin', 'Email', 'Prep', 'Meeting', 'Break', 'Mentoring']
+    hours = np.random.choice(range(9, 18), 10)
+    return pd.DataFrame({
+        'timestamp': [f"{h:02d}:00:00" for h in hours],
+        'activity_type': np.random.choice(activities, 10),
+        'duration_minutes': np.random.randint(15, 120, 10),
+        'tcl_score': np.random.randint(10, 100, 10),
+        'context_switch_flag': np.random.choice([0, 1], 10)
+    })
+
+base_date = datetime.date(2025, 10, 13)
+faculty_data = {f: {base_date - datetime.timedelta(days=i): generate_day(i+idx*10) for i in range(30)}
+                for idx, f in enumerate(faculty_profiles.keys())}
+
+# ---------------------------------------
+# 📄 FACULTY PROFILE (Sliding Panel)
+# ---------------------------------------
+with st.expander(f"👨‍🏫 Faculty Profile: {selected_faculty}", expanded=True):
+    colA, colB = st.columns([1, 2])
+    with colA:
+        st.image(faculty['image'], width=140)
+        st.metric("⭐ Rating", faculty['rating'])
+        st.metric("🏫 Experience", faculty['experience'])
+    with colB:
+        st.write(f"**Designation:** {faculty['designation']}")
+        st.write(f"**Department:** {faculty['department']}")
+        st.write(f"**Institution:** {faculty['institution']}")
+        st.write(f"**Email:** {faculty['email']}  |  **Faculty ID:** {faculty['id']}")
+        st.write(f"**Expertise:** {', '.join(faculty['expertise'])}")
+        st.write(f"📝 {faculty['about']}")
+        # Achievements Section
+        if "achievements" in faculty:
+            st.markdown("**🏆 Achievements:**")
+            for ach in faculty["achievements"]:
+                st.markdown(f"- {ach}")
+
+# ---------------------------------------
+# 🗓 PERIOD SELECTION
+# ---------------------------------------
+st.title("📊 Advanced Faculty Effectiveness & Workload Analytics Dashboard")
+period = st.radio("Select Analysis Period:", ["Daily", "Weekly", "Monthly"], horizontal=True)
+
+if period == "Daily":
+    selected_date = st.date_input("Select Date", base_date)
+    df = faculty_data[selected_faculty].get(selected_date, pd.DataFrame())
+elif period == "Weekly":
+    dates = sorted(faculty_data[selected_faculty].keys())[:7]
+    df = pd.concat([faculty_data[selected_faculty][d] for d in dates], keys=dates).reset_index(level=1, drop=True).rename_axis('date').reset_index()
+else:
+    dates = sorted(faculty_data[selected_faculty].keys())
+    df = pd.concat([faculty_data[selected_faculty][d] for d in dates], keys=dates).reset_index(level=1, drop=True).rename_axis('date').reset_index()
+
+if df.empty:
+    st.warning("No data found.")
+    st.stop()
+
+# ---------------------------------------
+# 🧮 METRICS COMPUTATION
+# ---------------------------------------
+df['hour'] = pd.to_datetime(df['timestamp']).dt.hour
+time_allocation = df.groupby('activity_type')['duration_minutes'].sum()
+wasted_time = df['context_switch_flag'].sum() * 5
+high_load_time = df[df['tcl_score'] > 70]['duration_minutes'].sum()
+avg_tcl = df['tcl_score'].mean()
+peak_hour = df.groupby('hour')['tcl_score'].mean().idxmax()
+
+# Work-Life Balance Indicator
+break_time = df[df['activity_type'] == 'Break']['duration_minutes'].sum()
+work_time = df['duration_minutes'].sum()
+balance_score = (break_time / work_time) * 100 if work_time else 0
+
+# ---------------------------------------
+# Utility: Save manual entries to CSV for persistent storage
+# ---------------------------------------
+def save_manual_entries(faculty_name, manual_entries):
+    df_manual = pd.DataFrame(manual_entries)
+    filename = f"{faculty_name.replace(' ', '_')}_manual_entries.csv"
+    df_manual.to_csv(filename, index=False)
+    return filename
+
+def load_manual_entries(faculty_name):
+    filename = f"{faculty_name.replace(' ', '_')}_manual_entries.csv"
+    try:
+        return pd.read_csv(filename).to_dict('records')
+    except Exception:
+        return []
+
+# ---------------------------------------
+# ⏱ GAUGE CHART FOR TCL
+# ---------------------------------------
+fig_gauge = go.Figure(go.Indicator(
+    mode="gauge+number",
+    value=avg_tcl,
+    title={'text': "Real-Time TCL"},
+    gauge={'axis': {'range': [0, 100]},
+           'bar': {'color': "red" if avg_tcl > 70 else "green"},
+           'steps': [
+               {'range': [0, 50], 'color': "lightgreen"},
+               {'range': [50, 80], 'color': "orange"},
+               {'range': [80, 100], 'color': "red"}]}
+))
+st.plotly_chart(fig_gauge, use_container_width=True)
+
+# ---------------------------------------
+# 🧭 KEY INDICATORS
+# ---------------------------------------
+k1, k2, k3, k4, k5 = st.columns(5)
+k1.metric("🧠 Average TCL", f"{avg_tcl:.1f}/100")
+k2.metric("⏳ Context Switch Time Wasted", f"{wasted_time} min")
+k3.metric("🔥 High Load Duration", f"{high_load_time} min")
+k4.metric("🕒 Peak Load Hour", f"{peak_hour}:00")
+k5.metric("⚖️ Work-Life Balance", f"{balance_score:.1f}%")
+
+st.markdown("---")
+
+# ---------------------------------------
+# 📊 TABS STRUCTURE
+# ---------------------------------------
+tab1, tab2, tab3, tab4, tab5, tab6, tab7, tab8 = st.tabs([
+    "📌 Activity Overview",
+    "📈 Trends & Graphs",
+    "🤖 AI Insights",
+    "📊 Advanced Analytics",
+    "📥 Reports",
+    "🧑‍💻 Virtual Assistant",
+    "✍️ Manual Entry",
+    "🔬 Data Science & ML"
+])
+
+# ---------------------------------------
+# 📌 TAB 1: ACTIVITY OVERVIEW
+# ---------------------------------------
+with tab1:
+    col1, col2 = st.columns(2)
+    with col1:
+        fig = px.pie(values=time_allocation.values, names=time_allocation.index,
+                     hole=0.4, color_discrete_sequence=px.colors.sequential.Viridis,
+                     title="🕒 Time Allocation by Activity")
+        fig.update_traces(textinfo='percent+label')
+        st.plotly_chart(fig, use_container_width=True)
+
+    with col2:
+        df_treemap = df.groupby('activity_type')['tcl_score'].mean().reset_index()
+        fig_tree = px.treemap(df_treemap, path=['activity_type'], values='tcl_score',
+                              color='tcl_score', color_continuous_scale='Blues',
+                              title="🌳 Activity Cognitive Load Tree")
+        st.plotly_chart(fig_tree, use_container_width=True)
+
+# ---------------------------------------
+# 📈 TAB 2: TRENDS & GRAPHS
+# ---------------------------------------
+with tab2:
+    if period == "Daily":
+        hourly_avg = df.groupby('hour')['tcl_score'].mean().reset_index()
+        st.subheader("📅 Hourly Cognitive Load Trend")
+        st.plotly_chart(px.line(hourly_avg, x='hour', y='tcl_score', markers=True, title="Hourly TCL Curve"), use_container_width=True)
+    else:
+        st.subheader(f"📆 {period} Trends")
+        trend_tcl = df.groupby('date')['tcl_score'].mean().reset_index()
+        trend_time = df.groupby('date')['duration_minutes'].sum().reset_index()
+        c1, c2 = st.columns(2)
+        with c1:
+            st.plotly_chart(px.line(trend_tcl, x='date', y='tcl_score', title="📈 Avg TCL over time", markers=True), use_container_width=True)
+        with c2:
+            st.plotly_chart(px.bar(trend_time, x='date', y='duration_minutes', title="⏳ Total Workload per Day"), use_container_width=True)
+
+# ---------------------------------------
+# 🧠 TAB 3: AI INSIGHTS
+# ---------------------------------------
+with tab3:
+    st.subheader("🤖 AI-Powered Efficiency Insights")
+    insights = []
+    if wasted_time > 30:
+        insights.append(f"⚠️ High context switching detected: {wasted_time} minutes wasted. Batch administrative work at the end of the day.")
+    if high_load_time > 120:
+        insights.append("🔥 High cognitive load period exceeded 2 hours. Introduce structured breaks.")
+    if avg_tcl < 40:
+        insights.append("📉 TCL below expected average. Increase interactive sessions.")
+    if avg_tcl > 80:
+        insights.append("🧠 Exceptional focus periods detected. Schedule complex lectures during these hours.")
+    if balance_score < 5:
+        insights.append("⚖️ Work-life balance is low. Encourage more breaks.")
+    if not insights:
+        st.success("🎉 Excellent balance detected. No major issues found.")
+    else:
+        for ins in insights:
+            st.info(ins)
+
+# ---------------------------------------
+# 📊 TAB 4: ADVANCED ANALYTICS
+# ---------------------------------------
+with tab4:
+    st.subheader("📊 Activity vs Cognitive Load Scatter Analysis")
+    fig_scatter = px.scatter(
+        df, x='duration_minutes', y='tcl_score', color='activity_type',
+        size='duration_minutes', hover_data=['timestamp'],
+        title="⏳ Duration vs 🧠 TCL Score Scatter"
+    )
+    st.plotly_chart(fig_scatter, use_container_width=True)
+
+    st.subheader("🔸 Load Distribution Heatmap")
+    df_heat = df.pivot_table(index='activity_type', columns='hour', values='tcl_score', aggfunc='mean')
+    fig_heat = go.Figure(data=go.Heatmap(z=df_heat.values, x=df_heat.columns, y=df_heat.index, colorscale='Reds'))
+    fig_heat.update_layout(title="Heatmap of Cognitive Load by Hour & Activity", xaxis_nticks=12)
+    st.plotly_chart(fig_heat, use_container_width=True)
+
+# ---------------------------------------
+# 📥 TAB 5: REPORTS
+# ---------------------------------------
+with tab5:
+    st.subheader("📤 Generate & Download Report")
+    st.dataframe(df)
+    total_duration = df['duration_minutes'].sum() / 60
+    st.markdown(f"🕒 **Total Hours Worked:** {total_duration:.2f} hrs")
+    st.markdown(f"📌 **Distinct Activities:** {df['activity_type'].nunique()}")
+    st.download_button(
+        label="📥 Download CSV Report",
+        data=df.to_csv(index=False).encode('utf-8'),
+        file_name=f"{selected_faculty}_{period.lower()}_report.csv",
+        mime='text/csv'
+    )
+    # PDF Export Option
+    if pdfkit:
+        html = df.to_html(index=False)
+        pdf_bytes = pdfkit.from_string(html, False)
+        st.download_button(
+            label="📄 Download PDF Report",
+            data=pdf_bytes,
+            file_name=f"{selected_faculty}_{period.lower()}_report.pdf",
+            mime='application/pdf'
+        )
+    else:
+        st.info("PDF export requires `pdfkit` and `wkhtmltopdf` installed. Run `pip install pdfkit` and download wkhtmltopdf from https://wkhtmltopdf.org/downloads.html")
+
+# ---------------------------------------
+# 🧑‍💻 TAB 6: VIRTUAL ASSISTANT (Dynamic Chat)
+# ---------------------------------------
+with tab6:
+    st.markdown("## 💬 Faculty Virtual Assistant")
+    st.write("Ask about your schedule, workload, breaks, or cognitive load. Example: *How busy am I today?*")
+
+    if "chat_history" not in st.session_state:
+        st.session_state.chat_history = []
+
+    user_query = st.chat_input("Type your question here...")
+
+    def get_dynamic_response(query):
+        context = (
+            f"Faculty: {selected_faculty}\n"
+            f"Total work time: {work_time:.1f} minutes\n"
+            f"Break time: {break_time:.1f} minutes\n"
+            f"Work-life balance score: {balance_score:.1f}%\n"
+            f"Average TCL: {avg_tcl:.1f}/100\n"
+            f"High load duration: {high_load_time} minutes\n"
+            f"Context switch time wasted: {wasted_time} minutes\n"
+            f"Peak busy hour: {peak_hour}:00\n"
+            f"Distinct activities: {df['activity_type'].nunique()}\n"
+        )
+        prompt = (
+            f"You are a helpful assistant for faculty workload analytics. "
+            f"Here is the faculty's current dashboard data:\n{context}\n"
+            f"User question: {query}\n"
+            f"Answer in a friendly, concise way using the data above."
+        )
+        # Uncomment below to use OpenAI
+        # try:
+        #     response = openai.ChatCompletion.create(
+        #         model="gpt-3.5-turbo",
+        #         messages=[
+        #             {"role": "system", "content": "You are a helpful assistant for faculty workload analytics."},
+        #             {"role": "user", "content": prompt}
+        #         ],
+        #         max_tokens=150
+        #     )
+        #     return response.choices[0].message.content.strip()
+        # except Exception as e:
+        #     return f"Sorry, I couldn't fetch a response. ({e})"
+        # --- Fallback: Static rule-based response ---
+        query = query.lower()
+        if "busy" in query or "schedule" in query:
+            return f"You have worked a total of {work_time:.1f} minutes today. Peak busy hour: {peak_hour}:00."
+        elif "break" in query or "rest" in query:
+            return f"You took {break_time:.1f} minutes of break today. Work-life balance score: {balance_score:.1f}%."
+        elif "tcl" in query or "load" in query:
+            return f"Your average TCL is {avg_tcl:.1f}/100. High load duration: {high_load_time} minutes."
+        elif "context switch" in query or "distraction" in query:
+            return f"Context switching wasted {wasted_time} minutes today. Try batching similar tasks."
+        elif "activities" in query:
+            return f"Distinct activities performed: {df['activity_type'].nunique()}."
+        elif "report" in query or "download" in query:
+            return "You can download your report from the 'Reports' tab."
+        else:
+            return "I'm here to help! Ask about your workload, schedule, breaks, or cognitive load."
+
+    if user_query:
+        st.session_state.chat_history.append(("user", user_query))
+        response = get_dynamic_response(user_query)
+        st.session_state.chat_history.append(("assistant", response))
+
+    for role, msg in st.session_state.chat_history:
+        st.chat_message(role).write(msg)
+
+# ---------------------------------------
+# ✍️ TAB 7: MANUAL ENTRY (with persistent storage)
+# ---------------------------------------
+with tab7:
+    st.subheader("✍️ Add Your Own Experience Data")
+    st.write("Manually log your activities, workload, or notes for today. Entries will be saved and used for analytics.")
+
+    if "manual_entries" not in st.session_state:
+        st.session_state.manual_entries = load_manual_entries(selected_faculty)
+
+    with st.form("manual_entry_form"):
+        activity = st.selectbox("Activity Type", ['Instruction', 'Research', 'Admin', 'Email', 'Prep', 'Meeting', 'Break', 'Mentoring', 'Other'])
+        duration = st.number_input("Duration (minutes)", min_value=1, max_value=480, value=60)
+        tcl = st.slider("TCL Score (1-100)", min_value=1, max_value=100, value=50)
+        notes = st.text_area("Notes (optional)")
+        submitted = st.form_submit_button("Add Entry")
+        if submitted:
+            entry = {
+                "activity_type": activity,
+                "duration_minutes": duration,
+                "tcl_score": tcl,
+                "notes": notes,
+                "timestamp": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+            }
+            st.session_state.manual_entries.append(entry)
+            save_manual_entries(selected_faculty, st.session_state.manual_entries)
+            st.success("Entry added and saved!")
+
+    if st.session_state.manual_entries:
+        manual_df = pd.DataFrame(st.session_state.manual_entries)
+        st.write("Your Manual Entries:")
+        st.dataframe(manual_df)
+
+# ---------------------------------------
+# 🔬 TAB 8: DATA SCIENCE & MACHINE LEARNING ANALYSIS
+# ---------------------------------------
+with tab8:
+    st.subheader("🔬 Data Science & Machine Learning Analysis")
+    st.write("Analyze your activity data using clustering and regression.")
+
+    # Combine simulated and manual data for analysis
+    combined_df = pd.concat([df, pd.DataFrame(st.session_state.manual_entries)], ignore_index=True, sort=False)
+    combined_df = combined_df.dropna(subset=['activity_type', 'duration_minutes', 'tcl_score'])
+
+    # Clustering: Group activities by TCL and duration
+    from sklearn.cluster import KMeans
+    X = combined_df[['duration_minutes', 'tcl_score']]
+    if len(X) >= 3:
+        kmeans = KMeans(n_clusters=3, random_state=42)
+        combined_df['cluster'] = kmeans.fit_predict(X)
+        st.write("Activity Clusters (based on duration & TCL):")
+        st.dataframe(combined_df[['activity_type', 'duration_minutes', 'tcl_score', 'cluster']])
+        fig_cluster = px.scatter(combined_df, x='duration_minutes', y='tcl_score', color='cluster', hover_data=['activity_type'])
+        st.plotly_chart(fig_cluster, use_container_width=True)
+    else:
+        st.info("Add more manual entries for clustering analysis.")
+
+    # Regression: Predict TCL from duration (manual regression line, no statsmodels needed)
+    from sklearn.linear_model import LinearRegression
+    if len(X) >= 3:
+        reg = LinearRegression()
+        reg.fit(X[['duration_minutes']], X['tcl_score'])
+        combined_df['predicted_tcl'] = reg.predict(X[['duration_minutes']])
+        st.write("Regression: Predict TCL from Duration")
+        st.dataframe(combined_df[['duration_minutes', 'tcl_score', 'predicted_tcl']])
+
+        scatter = go.Scatter(
+            x=combined_df['duration_minutes'],
+            y=combined_df['tcl_score'],
+            mode='markers',
+            name='Actual TCL'
+        )
+        line = go.Scatter(
+            x=combined_df['duration_minutes'],
+            y=combined_df['predicted_tcl'],
+            mode='lines',
+            name='Regression Line'
+        )
+        fig_reg = go.Figure([scatter, line])
+        fig_reg.update_layout(title="TCL vs Duration Regression",
+                             xaxis_title="Duration (minutes)",
+                             yaxis_title="TCL Score")
+        st.plotly_chart(fig_reg, use_container_width=True)
+    else:
+        st.info("Add more manual entries for regression analysis.")
+
+st.markdown("---")
+st.caption("© 2025 Advanced Faculty Effectiveness Dashboard | Built with ❤️ Streamlit + Plotly")
\ No newline at end of file
diff --git a/Domains/Frontend/MiniProjects/WorkloadAnalyzer/app.py b/Domains/Frontend/MiniProjects/WorkloadAnalyzer/app.py
new file mode 100644
index 00000000..16021266
--- /dev/null
+++ b/Domains/Frontend/MiniProjects/WorkloadAnalyzer/app.py
@@ -0,0 +1,530 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+import plotly.express as px
+import plotly.graph_objects as go
+import datetime
+import os
+
+# Add these imports for PDF export
+import io
+try:
+    import pdfkit
+except ImportError:
+    pdfkit = None
+
+# Optional: Uncomment if you want to use OpenAI for dynamic chat responses
+# import openai
+
+# ---------------------------------------
+# 🌐 PAGE CONFIG
+# ---------------------------------------
+st.set_page_config(
+    page_title="Faculty Effectiveness Analyzer",
+    page_icon="📊",
+    layout="wide",
+    initial_sidebar_state="collapsed"
+)
+
+# ---------------------------------------
+# 👩‍🏫 FACULTY DATA (with manual add section)
+# ---------------------------------------
+def save_faculty_profiles(profiles):
+    pd.DataFrame(profiles).to_json("faculty_profiles.json")
+
+def load_faculty_profiles():
+    if os.path.exists("faculty_profiles.json"):
+        return pd.read_json("faculty_profiles.json", typ='dict')
+    else:
+        return None
+
+profiles_from_file = load_faculty_profiles()
+if profiles_from_file:
+    faculty_profiles = profiles_from_file
+else:
+    faculty_profiles = {
+        "Dr. John Doe": {
+            "image": "https://cdn-icons-png.flaticon.com/512/3135/3135715.png",
+            "rating": "4.8 / 5",
+            "experience": "12 Years",
+            "designation": "Associate Professor",
+            "department": "Computer Engineering",
+            "institution": "ABC Institute of Technology",
+            "about": "Passionate about building real-world technical skills and improving student outcomes through interactive learning.",
+            "expertise": ["Machine Learning", "HCI", "Operating Systems"],
+            "email": "johndoe@abc.edu",
+            "id": "CSE-1023",
+            "achievements": [
+                "Best Teacher Award 2022",
+                "Published 10+ research papers",
+                "Invited Speaker at MLConf 2024"
+            ]
+        },
+        "Prof. Meera Sharma": {
+            "image": "https://cdn-icons-png.flaticon.com/512/4140/4140048.png",
+            "rating": "4.5 / 5",
+            "experience": "8 Years",
+            "designation": "Assistant Professor",
+            "department": "Information Technology",
+            "institution": "XYZ College of Engineering",
+            "about": "Focused on student engagement and improving conceptual understanding.",
+            "expertise": ["Cloud Computing", "IoT", "Networking"],
+            "email": "meerasharma@xyz.edu",
+            "id": "IT-1122",
+            "achievements": [
+                "Young Researcher Award 2021",
+                "Organized IoT Symposium 2023"
+            ]
+        },
+        "Dr. A. Sharma": {
+            "image": "https://cdn-icons-png.flaticon.com/512/4140/4140037.png",
+            "rating": "4.9 / 5",
+            "experience": "15 Years",
+            "designation": "Professor",
+            "department": "AI & Data Science",
+            "institution": "Global Tech University",
+            "about": "Known for delivering complex topics with clarity and adaptive teaching methods.",
+            "expertise": ["AI", "Data Science", "Deep Learning"],
+            "email": "asharma@gtu.edu",
+            "id": "AIDS-1007",
+            "achievements": [
+                "AI Excellence Award 2023",
+                "Published 20+ journal articles"
+            ]
+        }
+    }
+
+# ---------------------------------------
+# SIDEBAR: Manual Faculty Add Section
+# ---------------------------------------
+st.sidebar.markdown("### ➕ Add New Faculty Profile")
+with st.sidebar.form("add_faculty_form"):
+    new_name = st.text_input("Full Name")
+    new_email = st.text_input("Email")
+    new_id = st.text_input("Faculty ID")
+    new_designation = st.text_input("Designation")
+    new_department = st.text_input("Department")
+    new_institution = st.text_input("Institution")
+    new_experience = st.text_input("Experience (e.g. 5 Years)")
+    new_rating = st.text_input("Rating (e.g. 4.5 / 5)")
+    new_about = st.text_area("About")
+    new_expertise = st.text_input("Expertise (comma separated)")
+    new_image = st.text_input("Image URL (optional)", value="https://cdn-icons-png.flaticon.com/512/3135/3135715.png")
+    new_achievements = st.text_area("Achievements (one per line)")
+    add_faculty_btn = st.form_submit_button("Add Faculty")
+
+    if add_faculty_btn and new_name:
+        faculty_profiles[new_name] = {
+            "image": new_image,
+            "rating": new_rating,
+            "experience": new_experience,
+            "designation": new_designation,
+            "department": new_department,
+            "institution": new_institution,
+            "about": new_about,
+            "expertise": [e.strip() for e in new_expertise.split(",") if e.strip()],
+            "email": new_email,
+            "id": new_id,
+            "achievements": [a.strip() for a in new_achievements.split("\n") if a.strip()]
+        }
+        save_faculty_profiles(faculty_profiles)
+        st.sidebar.success(f"Faculty '{new_name}' added! Please refresh to see in the list.")
+
+selected_faculty = st.sidebar.selectbox("👩‍🏫 Select Faculty", list(faculty_profiles.keys()))
+faculty = faculty_profiles[selected_faculty]
+
+# ---------------------------------------
+# 🧠 SIMULATED DATA GENERATION (PER FACULTY)
+# ---------------------------------------
+def generate_day(seed):
+    np.random.seed(seed)
+    activities = ['Instruction', 'Research', 'Admin', 'Email', 'Prep', 'Meeting', 'Break', 'Mentoring']
+    hours = np.random.choice(range(9, 18), 10)
+    return pd.DataFrame({
+        'timestamp': [f"{h:02d}:00:00" for h in hours],
+        'activity_type': np.random.choice(activities, 10),
+        'duration_minutes': np.random.randint(15, 120, 10),
+        'tcl_score': np.random.randint(10, 100, 10),
+        'context_switch_flag': np.random.choice([0, 1], 10)
+    })
+
+base_date = datetime.date(2025, 10, 13)
+faculty_data = {f: {base_date - datetime.timedelta(days=i): generate_day(i+idx*10) for i in range(30)}
+                for idx, f in enumerate(faculty_profiles.keys())}
+
+# ---------------------------------------
+# 📄 FACULTY PROFILE (Sliding Panel)
+# ---------------------------------------
+with st.expander(f"👨‍🏫 Faculty Profile: {selected_faculty}", expanded=True):
+    colA, colB = st.columns([1, 2])
+    with colA:
+        st.image(faculty['image'], width=140)
+        st.metric("⭐ Rating", faculty['rating'])
+        st.metric("🏫 Experience", faculty['experience'])
+    with colB:
+        st.write(f"**Designation:** {faculty['designation']}")
+        st.write(f"**Department:** {faculty['department']}")
+        st.write(f"**Institution:** {faculty['institution']}")
+        st.write(f"**Email:** {faculty['email']}  |  **Faculty ID:** {faculty['id']}")
+        st.write(f"**Expertise:** {', '.join(faculty['expertise'])}")
+        st.write(f"📝 {faculty['about']}")
+        # Achievements Section
+        if "achievements" in faculty:
+            st.markdown("**🏆 Achievements:**")
+            for ach in faculty["achievements"]:
+                st.markdown(f"- {ach}")
+
+# ---------------------------------------
+# 🗓 PERIOD SELECTION
+# ---------------------------------------
+st.title("📊 Advanced Faculty Effectiveness & Workload Analytics Dashboard")
+period = st.radio("Select Analysis Period:", ["Daily", "Weekly", "Monthly"], horizontal=True)
+
+if period == "Daily":
+    selected_date = st.date_input("Select Date", base_date)
+    df = faculty_data[selected_faculty].get(selected_date, pd.DataFrame())
+elif period == "Weekly":
+    dates = sorted(faculty_data[selected_faculty].keys())[:7]
+    df = pd.concat([faculty_data[selected_faculty][d] for d in dates], keys=dates).reset_index(level=1, drop=True).rename_axis('date').reset_index()
+else:
+    dates = sorted(faculty_data[selected_faculty].keys())
+    df = pd.concat([faculty_data[selected_faculty][d] for d in dates], keys=dates).reset_index(level=1, drop=True).rename_axis('date').reset_index()
+
+if df.empty:
+    st.warning("No data found.")
+    st.stop()
+
+# ---------------------------------------
+# 🧮 METRICS COMPUTATION
+# ---------------------------------------
+df['hour'] = pd.to_datetime(df['timestamp']).dt.hour
+time_allocation = df.groupby('activity_type')['duration_minutes'].sum()
+wasted_time = df['context_switch_flag'].sum() * 5
+high_load_time = df[df['tcl_score'] > 70]['duration_minutes'].sum()
+avg_tcl = df['tcl_score'].mean()
+peak_hour = df.groupby('hour')['tcl_score'].mean().idxmax()
+
+# Work-Life Balance Indicator
+break_time = df[df['activity_type'] == 'Break']['duration_minutes'].sum()
+work_time = df['duration_minutes'].sum()
+balance_score = (break_time / work_time) * 100 if work_time else 0
+
+# ---------------------------------------
+# Utility: Save manual entries to CSV for persistent storage
+# ---------------------------------------
+def save_manual_entries(faculty_name, manual_entries):
+    df_manual = pd.DataFrame(manual_entries)
+    filename = f"{faculty_name.replace(' ', '_')}_manual_entries.csv"
+    df_manual.to_csv(filename, index=False)
+    return filename
+
+def load_manual_entries(faculty_name):
+    filename = f"{faculty_name.replace(' ', '_')}_manual_entries.csv"
+    try:
+        return pd.read_csv(filename).to_dict('records')
+    except Exception:
+        return []
+
+# ---------------------------------------
+# ⏱ GAUGE CHART FOR TCL
+# ---------------------------------------
+fig_gauge = go.Figure(go.Indicator(
+    mode="gauge+number",
+    value=avg_tcl,
+    title={'text': "Real-Time TCL"},
+    gauge={'axis': {'range': [0, 100]},
+           'bar': {'color': "red" if avg_tcl > 70 else "green"},
+           'steps': [
+               {'range': [0, 50], 'color': "lightgreen"},
+               {'range': [50, 80], 'color': "orange"},
+               {'range': [80, 100], 'color': "red"}]}
+))
+st.plotly_chart(fig_gauge, use_container_width=True)
+
+# ---------------------------------------
+# 🧭 KEY INDICATORS
+# ---------------------------------------
+k1, k2, k3, k4, k5 = st.columns(5)
+k1.metric("🧠 Average TCL", f"{avg_tcl:.1f}/100")
+k2.metric("⏳ Context Switch Time Wasted", f"{wasted_time} min")
+k3.metric("🔥 High Load Duration", f"{high_load_time} min")
+k4.metric("🕒 Peak Load Hour", f"{peak_hour}:00")
+k5.metric("⚖️ Work-Life Balance", f"{balance_score:.1f}%")
+
+st.markdown("---")
+
+# ---------------------------------------
+# 📊 TABS STRUCTURE
+# ---------------------------------------
+tab1, tab2, tab3, tab4, tab5, tab6, tab7, tab8 = st.tabs([
+    "📌 Activity Overview",
+    "📈 Trends & Graphs",
+    "🤖 AI Insights",
+    "📊 Advanced Analytics",
+    "📥 Reports",
+    "🧑‍💻 Virtual Assistant",
+    "✍️ Manual Entry",
+    "🔬 Data Science & ML"
+])
+
+# ---------------------------------------
+# 📌 TAB 1: ACTIVITY OVERVIEW
+# ---------------------------------------
+with tab1:
+    col1, col2 = st.columns(2)
+    with col1:
+        fig = px.pie(values=time_allocation.values, names=time_allocation.index,
+                     hole=0.4, color_discrete_sequence=px.colors.sequential.Viridis,
+                     title="🕒 Time Allocation by Activity")
+        fig.update_traces(textinfo='percent+label')
+        st.plotly_chart(fig, use_container_width=True)
+
+    with col2:
+        df_treemap = df.groupby('activity_type')['tcl_score'].mean().reset_index()
+        fig_tree = px.treemap(df_treemap, path=['activity_type'], values='tcl_score',
+                              color='tcl_score', color_continuous_scale='Blues',
+                              title="🌳 Activity Cognitive Load Tree")
+        st.plotly_chart(fig_tree, use_container_width=True)
+
+# ---------------------------------------
+# 📈 TAB 2: TRENDS & GRAPHS
+# ---------------------------------------
+with tab2:
+    if period == "Daily":
+        hourly_avg = df.groupby('hour')['tcl_score'].mean().reset_index()
+        st.subheader("📅 Hourly Cognitive Load Trend")
+        st.plotly_chart(px.line(hourly_avg, x='hour', y='tcl_score', markers=True, title="Hourly TCL Curve"), use_container_width=True)
+    else:
+        st.subheader(f"📆 {period} Trends")
+        trend_tcl = df.groupby('date')['tcl_score'].mean().reset_index()
+        trend_time = df.groupby('date')['duration_minutes'].sum().reset_index()
+        c1, c2 = st.columns(2)
+        with c1:
+            st.plotly_chart(px.line(trend_tcl, x='date', y='tcl_score', title="📈 Avg TCL over time", markers=True), use_container_width=True)
+        with c2:
+            st.plotly_chart(px.bar(trend_time, x='date', y='duration_minutes', title="⏳ Total Workload per Day"), use_container_width=True)
+
+# ---------------------------------------
+# 🧠 TAB 3: AI INSIGHTS
+# ---------------------------------------
+with tab3:
+    st.subheader("🤖 AI-Powered Efficiency Insights")
+    insights = []
+    if wasted_time > 30:
+        insights.append(f"⚠️ High context switching detected: {wasted_time} minutes wasted. Batch administrative work at the end of the day.")
+    if high_load_time > 120:
+        insights.append("🔥 High cognitive load period exceeded 2 hours. Introduce structured breaks.")
+    if avg_tcl < 40:
+        insights.append("📉 TCL below expected average. Increase interactive sessions.")
+    if avg_tcl > 80:
+        insights.append("🧠 Exceptional focus periods detected. Schedule complex lectures during these hours.")
+    if balance_score < 5:
+        insights.append("⚖️ Work-life balance is low. Encourage more breaks.")
+    if not insights:
+        st.success("🎉 Excellent balance detected. No major issues found.")
+    else:
+        for ins in insights:
+            st.info(ins)
+
+# ---------------------------------------
+# 📊 TAB 4: ADVANCED ANALYTICS
+# ---------------------------------------
+with tab4:
+    st.subheader("📊 Activity vs Cognitive Load Scatter Analysis")
+    fig_scatter = px.scatter(
+        df, x='duration_minutes', y='tcl_score', color='activity_type',
+        size='duration_minutes', hover_data=['timestamp'],
+        title="⏳ Duration vs 🧠 TCL Score Scatter"
+    )
+    st.plotly_chart(fig_scatter, use_container_width=True)
+
+    st.subheader("🔸 Load Distribution Heatmap")
+    df_heat = df.pivot_table(index='activity_type', columns='hour', values='tcl_score', aggfunc='mean')
+    fig_heat = go.Figure(data=go.Heatmap(z=df_heat.values, x=df_heat.columns, y=df_heat.index, colorscale='Reds'))
+    fig_heat.update_layout(title="Heatmap of Cognitive Load by Hour & Activity", xaxis_nticks=12)
+    st.plotly_chart(fig_heat, use_container_width=True)
+
+# ---------------------------------------
+# 📥 TAB 5: REPORTS
+# ---------------------------------------
+with tab5:
+    st.subheader("📤 Generate & Download Report")
+    st.dataframe(df)
+    total_duration = df['duration_minutes'].sum() / 60
+    st.markdown(f"🕒 **Total Hours Worked:** {total_duration:.2f} hrs")
+    st.markdown(f"📌 **Distinct Activities:** {df['activity_type'].nunique()}")
+    st.download_button(
+        label="📥 Download CSV Report",
+        data=df.to_csv(index=False).encode('utf-8'),
+        file_name=f"{selected_faculty}_{period.lower()}_report.csv",
+        mime='text/csv'
+    )
+    # PDF Export Option
+    if pdfkit:
+        html = df.to_html(index=False)
+        pdf_bytes = pdfkit.from_string(html, False)
+        st.download_button(
+            label="📄 Download PDF Report",
+            data=pdf_bytes,
+            file_name=f"{selected_faculty}_{period.lower()}_report.pdf",
+            mime='application/pdf'
+        )
+    else:
+        st.info("PDF export requires `pdfkit` and `wkhtmltopdf` installed. Run `pip install pdfkit` and download wkhtmltopdf from https://wkhtmltopdf.org/downloads.html")
+
+# ---------------------------------------
+# 🧑‍💻 TAB 6: VIRTUAL ASSISTANT (Dynamic Chat)
+# ---------------------------------------
+with tab6:
+    st.markdown("## 💬 Faculty Virtual Assistant")
+    st.write("Ask about your schedule, workload, breaks, or cognitive load. Example: *How busy am I today?*")
+
+    if "chat_history" not in st.session_state:
+        st.session_state.chat_history = []
+
+    user_query = st.chat_input("Type your question here...")
+
+    def get_dynamic_response(query):
+        context = (
+            f"Faculty: {selected_faculty}\n"
+            f"Total work time: {work_time:.1f} minutes\n"
+            f"Break time: {break_time:.1f} minutes\n"
+            f"Work-life balance score: {balance_score:.1f}%\n"
+            f"Average TCL: {avg_tcl:.1f}/100\n"
+            f"High load duration: {high_load_time} minutes\n"
+            f"Context switch time wasted: {wasted_time} minutes\n"
+            f"Peak busy hour: {peak_hour}:00\n"
+            f"Distinct activities: {df['activity_type'].nunique()}\n"
+        )
+        prompt = (
+            f"You are a helpful assistant for faculty workload analytics. "
+            f"Here is the faculty's current dashboard data:\n{context}\n"
+            f"User question: {query}\n"
+            f"Answer in a friendly, concise way using the data above."
+        )
+        # Uncomment below to use OpenAI
+        # try:
+        #     response = openai.ChatCompletion.create(
+        #         model="gpt-3.5-turbo",
+        #         messages=[
+        #             {"role": "system", "content": "You are a helpful assistant for faculty workload analytics."},
+        #             {"role": "user", "content": prompt}
+        #         ],
+        #         max_tokens=150
+        #     )
+        #     return response.choices[0].message.content.strip()
+        # except Exception as e:
+        #     return f"Sorry, I couldn't fetch a response. ({e})"
+        # --- Fallback: Static rule-based response ---
+        query = query.lower()
+        if "busy" in query or "schedule" in query:
+            return f"You have worked a total of {work_time:.1f} minutes today. Peak busy hour: {peak_hour}:00."
+        elif "break" in query or "rest" in query:
+            return f"You took {break_time:.1f} minutes of break today. Work-life balance score: {balance_score:.1f}%."
+        elif "tcl" in query or "load" in query:
+            return f"Your average TCL is {avg_tcl:.1f}/100. High load duration: {high_load_time} minutes."
+        elif "context switch" in query or "distraction" in query:
+            return f"Context switching wasted {wasted_time} minutes today. Try batching similar tasks."
+        elif "activities" in query:
+            return f"Distinct activities performed: {df['activity_type'].nunique()}."
+        elif "report" in query or "download" in query:
+            return "You can download your report from the 'Reports' tab."
+        else:
+            return "I'm here to help! Ask about your workload, schedule, breaks, or cognitive load."
+
+    if user_query:
+        st.session_state.chat_history.append(("user", user_query))
+        response = get_dynamic_response(user_query)
+        st.session_state.chat_history.append(("assistant", response))
+
+    for role, msg in st.session_state.chat_history:
+        st.chat_message(role).write(msg)
+
+# ---------------------------------------
+# ✍️ TAB 7: MANUAL ENTRY (with persistent storage)
+# ---------------------------------------
+with tab7:
+    st.subheader("✍️ Add Your Own Experience Data")
+    st.write("Manually log your activities, workload, or notes for today. Entries will be saved and used for analytics.")
+
+    if "manual_entries" not in st.session_state:
+        st.session_state.manual_entries = load_manual_entries(selected_faculty)
+
+    with st.form("manual_entry_form"):
+        activity = st.selectbox("Activity Type", ['Instruction', 'Research', 'Admin', 'Email', 'Prep', 'Meeting', 'Break', 'Mentoring', 'Other'])
+        duration = st.number_input("Duration (minutes)", min_value=1, max_value=480, value=60)
+        tcl = st.slider("TCL Score (1-100)", min_value=1, max_value=100, value=50)
+        notes = st.text_area("Notes (optional)")
+        submitted = st.form_submit_button("Add Entry")
+        if submitted:
+            entry = {
+                "activity_type": activity,
+                "duration_minutes": duration,
+                "tcl_score": tcl,
+                "notes": notes,
+                "timestamp": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+            }
+            st.session_state.manual_entries.append(entry)
+            save_manual_entries(selected_faculty, st.session_state.manual_entries)
+            st.success("Entry added and saved!")
+
+    if st.session_state.manual_entries:
+        manual_df = pd.DataFrame(st.session_state.manual_entries)
+        st.write("Your Manual Entries:")
+        st.dataframe(manual_df)
+
+# ---------------------------------------
+# 🔬 TAB 8: DATA SCIENCE & MACHINE LEARNING ANALYSIS
+# ---------------------------------------
+with tab8:
+    st.subheader("🔬 Data Science & Machine Learning Analysis")
+    st.write("Analyze your activity data using clustering and regression.")
+
+    # Combine simulated and manual data for analysis
+    combined_df = pd.concat([df, pd.DataFrame(st.session_state.manual_entries)], ignore_index=True, sort=False)
+    combined_df = combined_df.dropna(subset=['activity_type', 'duration_minutes', 'tcl_score'])
+
+    # Clustering: Group activities by TCL and duration
+    from sklearn.cluster import KMeans
+    X = combined_df[['duration_minutes', 'tcl_score']]
+    if len(X) >= 3:
+        kmeans = KMeans(n_clusters=3, random_state=42)
+        combined_df['cluster'] = kmeans.fit_predict(X)
+        st.write("Activity Clusters (based on duration & TCL):")
+        st.dataframe(combined_df[['activity_type', 'duration_minutes', 'tcl_score', 'cluster']])
+        fig_cluster = px.scatter(combined_df, x='duration_minutes', y='tcl_score', color='cluster', hover_data=['activity_type'])
+        st.plotly_chart(fig_cluster, use_container_width=True)
+    else:
+        st.info("Add more manual entries for clustering analysis.")
+
+    # Regression: Predict TCL from duration (manual regression line, no statsmodels needed)
+    from sklearn.linear_model import LinearRegression
+    if len(X) >= 3:
+        reg = LinearRegression()
+        reg.fit(X[['duration_minutes']], X['tcl_score'])
+        combined_df['predicted_tcl'] = reg.predict(X[['duration_minutes']])
+        st.write("Regression: Predict TCL from Duration")
+        st.dataframe(combined_df[['duration_minutes', 'tcl_score', 'predicted_tcl']])
+
+        scatter = go.Scatter(
+            x=combined_df['duration_minutes'],
+            y=combined_df['tcl_score'],
+            mode='markers',
+            name='Actual TCL'
+        )
+        line = go.Scatter(
+            x=combined_df['duration_minutes'],
+            y=combined_df['predicted_tcl'],
+            mode='lines',
+            name='Regression Line'
+        )
+        fig_reg = go.Figure([scatter, line])
+        fig_reg.update_layout(title="TCL vs Duration Regression",
+                             xaxis_title="Duration (minutes)",
+                             yaxis_title="TCL Score")
+        st.plotly_chart(fig_reg, use_container_width=True)
+    else:
+        st.info("Add more manual entries for regression analysis.")
+
+st.markdown("---")
+st.caption("© 2025 Advanced Faculty Effectiveness Dashboard | Built with ❤️ Streamlit + Plotly")
\ No newline at end of file