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app.py

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+import os, re, json, time, pickle, random, traceback
+import numpy as np
+import pandas as pd
+import gradio as gr
+import plotly.graph_objects as go
+import requests as req
+from pathlib import Path
+from typing import Tuple
+
+try:
+    from huggingface_hub import InferenceClient
+except Exception:
+    InferenceClient = None
+
+# =========================================================
+# CONFIG
+# =========================================================
+BASE_DIR = Path(__file__).resolve().parent
+HF_API_KEY = os.environ.get("HF_API_KEY", "").strip()
+MODEL_NAME = os.environ.get("MODEL_NAME", "deepseek-ai/DeepSeek-R1").strip()
+HF_PROVIDER = os.environ.get("HF_PROVIDER", "novita").strip()
+N8N_SALARY_PREDICTION_URL = os.environ.get("N8N_SALARY_PREDICTION_URL", "").strip()
+N8N_HR_FEEDBACK_URL       = os.environ.get("N8N_HR_FEEDBACK_URL", "").strip()
+N8N_ANOMALY_DETECTION_URL = os.environ.get("N8N_ANOMALY_DETECTION_URL", "").strip()
+LLM_ENABLED = bool(HF_API_KEY) and InferenceClient is not None
+llm_client  = (InferenceClient(provider=HF_PROVIDER, api_key=HF_API_KEY) if LLM_ENABLED else None)
+
+P = ["#45FFCA","#D09CFA","#FF9B9B","#F875AA","#3EDBF0","#F2C637","#7c5cbf","#2ec4a0"]
+GOLD = "#F2C637"
+PURPLE = "#28096d"
+
+# =========================================================
+# DATA
+# =========================================================
+_CACHE = {}
+
+def _load_csv(f):
+    p = BASE_DIR / f
+    if p.exists():
+        try: return pd.read_csv(p)
+        except: pass
+    return pd.DataFrame()
+
+def _gen_base(n=500):
+    rng = np.random.default_rng(42)
+    edu_levels = ["Bachelor's","Master's","PhD"]
+    job_pool = {
+        "Bachelor's":["Data Analyst","Junior Engineer","Business Analyst","HR Specialist","Marketing Manager"],
+        "Master's":["Data Scientist","Senior Engineer","Product Manager","Financial Analyst","ML Engineer"],
+        "PhD":["Director of Analytics","Research Scientist","VP of Engineering","Chief Data Officer","AI Researcher"],
+    }
+    rows = []
+    for _ in range(n):
+        edu = rng.choice(edu_levels, p=[0.55,0.35,0.10])
+        exp = int(rng.integers(0,35))
+        age = int(np.clip(22+exp+rng.integers(-2,5),22,65))
+        gender = rng.choice(["Male","Female"], p=[0.52,0.48])
+        job = rng.choice(job_pool[edu])
+        base = {"Bachelor's":52000,"Master's":68000,"PhD":88000}[edu]
+        sal = base + exp*3100 + (age-22)*180 + float(rng.normal(0,6000))
+        if gender=="Male": sal *= float(rng.uniform(1.0,1.06))
+        sal = max(25000,int(sal))
+        rows.append({"Age":age,"Gender":gender,"Education Level":edu,"Job Title":job,"Years of Experience":exp,"Salary":sal})
+    df = pd.DataFrame(rows)
+    def tier(r):
+        s={"Bachelor's":1,"Master's":2,"PhD":3}.get(r["Education Level"],1)
+        if r["Years of Experience"]>=15 and s>=2: return "senior"
+        elif r["Years of Experience"]>=7 or s==3: return "mid"
+        return "junior"
+    df["career_tier"] = df.apply(tier,axis=1)
+    df["salary_growth"] = df.apply(lambda r: int(r["Salary"]*(0.15 if r["career_tier"]=="senior" else 0.30 if r["career_tier"]=="mid" else 0.50)+float(np.random.normal(0,2000))),axis=1)
+    return df
+
+def _gen_progression(base_df):
+    rows=[]
+    for _,row in base_df.iterrows():
+        t=row["career_tier"]; base=row["Salary"]
+        growth=np.linspace(0.85 if t=="senior" else 0.70 if t=="mid" else 0.50,1.0,5)
+        for i,yr in enumerate(range(2020,2025)):
+            sal=int(np.clip(base*(growth[i]+np.random.normal(0,0.03)),20000,None))
+            rows.append({"career_tier":t,"year":yr,"salary_that_year":sal})
+    return pd.DataFrame(rows)
+
+def _gen_feedback(base_df):
+    pools={"senior":["Consistently exceeds expectations.","Key leader and mentor.","Delivers high-impact results.","Trusted advisor to management."],"mid":["Solid contributor, meets targets.","Shows initiative and grows.","Dependable team member.","Takes ownership of projects."],"junior":["Eager learner, developing skills.","Shows promise under coaching.","Making good progress.","Improving steadily with support."]}
+    rows=[]
+    for _,row in base_df.iterrows():
+        for comment in random.sample(pools[row["career_tier"]],2):
+            rows.append({"career_tier":row["career_tier"],"feedback_comment":comment,"Salary":row["Salary"]})
+    return pd.DataFrame(rows)
+
+def get_base_df():
+    if "base" not in _CACHE:
+        df=_load_csv("employee_analysis_ready.csv")
+        if df.empty: df=_load_csv("Salary_Data.csv")
+        if df.empty: df=_gen_base()
+        if "career_tier" not in df.columns:
+            def tier(r):
+                s={"Bachelor's":1,"Master's":2,"PhD":3}.get(r.get("Education Level",""),1)
+                e=r.get("Years of Experience",0)
+                if e>=15 and s>=2: return "senior"
+                elif e>=7 or s==3: return "mid"
+                return "junior"
+            df["career_tier"]=df.apply(tier,axis=1)
+        _CACHE["base"]=df
+    return _CACHE["base"]
+
+def get_prog_df():
+    if "prog" not in _CACHE:
+        df=_load_csv("synthetic_salary_progression.csv")
+        if df.empty: df=_gen_progression(get_base_df())
+        _CACHE["prog"]=df
+    return _CACHE["prog"]
+
+def get_feed_df():
+    if "feed" not in _CACHE:
+        df=_load_csv("synthetic_employee_feedback.csv")
+        if df.empty: df=_gen_feedback(get_base_df())
+        _CACHE["feed"]=df
+    return _CACHE["feed"]
+
+# =========================================================
+# HELPERS
+# =========================================================
+def get_tier(age,exp,edu):
+    s={"Bachelor's":1,"Master's":2,"PhD":3}.get(edu,1)
+    if exp>=15 and s>=2: return "senior"
+    elif exp>=7 or s==3: return "mid"
+    return "junior"
+
+def get_exp_group(exp):
+    if exp<=5: return "0-5 years"
+    elif exp<=10: return "6-10 years"
+    elif exp<=15: return "11-15 years"
+    elif exp<=20: return "16-20 years"
+    return "20+"
+
+def _layout(**kw):
+    d=dict(template="plotly_white",paper_bgcolor="rgba(255,255,255,0.97)",plot_bgcolor="rgba(255,255,255,0.99)",font=dict(family="system-ui,sans-serif",color="#1a0a3d",size=12),margin=dict(l=50,r=20,t=55,b=45),legend=dict(orientation="h",yanchor="bottom",y=1.02,xanchor="right",x=1,bgcolor="rgba(255,255,255,0.9)",bordercolor="rgba(40,9,109,0.15)",borderwidth=1),title=dict(font=dict(size=14,color="#28096d")))
+    d.update(kw)
+    return d
+
+def _n8n(url,payload):
+    if not url: return None
+    try:
+        r=req.post(url,json=payload,timeout=15)
+        return r.json()
+    except Exception as e:
+        return {"error":str(e)}
+
+# =========================================================
+# PREDICTION MODEL
+# =========================================================
+def _load_model():
+    for mp in [BASE_DIR/"rf_model.pkl",BASE_DIR/"model"/"rf_model.pkl"]:
+        cp=mp.parent/"train_columns.pkl"
+        if mp.exists() and cp.exists():
+            try:
+                with open(mp,"rb") as f: m=pickle.load(f)
+                with open(cp,"rb") as f: c=pickle.load(f)
+                return m,c
+            except: pass
+    return None,None
+
+RF_MODEL,TRAIN_COLS=_load_model()
+
+def predict_local(age,exp,edu,gender):
+    tier=get_tier(age,exp,edu)
+    exp_group=get_exp_group(exp)
+    if RF_MODEL and TRAIN_COLS:
+        row={"Age":age,"Years of Experience":exp,"Education Level_Master's":1 if edu=="Master's" else 0,"Education Level_PhD":1 if edu=="PhD" else 0,"salary_growth":exp*3500,"vader_score":0.3 if tier=="senior" else 0.15 if tier=="mid" else 0.05}
+        inp=pd.DataFrame([row])
+        tmp=pd.DataFrame([{"Experience Group":exp_group,"career_tier":tier}])
+        inp=pd.concat([inp,tmp],axis=1)
+        inp=pd.get_dummies(inp,columns=["Experience Group","career_tier"],drop_first=True)*1
+        for col in TRAIN_COLS:
+            if col not in inp.columns: inp[col]=0
+        inp=inp[TRAIN_COLS]
+        predicted=float(RF_MODEL.predict(inp)[0])
+        source="Random Forest Model"
+    else:
+        base={"Bachelor's":55000,"Master's":72000,"PhD":92000}.get(edu,55000)
+        predicted=base+(exp*3200)+((age-22)*250)
+        predicted=max(28000,predicted)
+        source="Formula Estimate"
+    predicted+=predicted*random.uniform(-0.01,0.01)
+    return {"predicted":round(predicted),"low":round(predicted*0.92),"high":round(predicted*1.08),"tier":tier,"exp_group":exp_group,"source":source}
+
+def project_salary(base_salary, exp, edu, tier, years=5):
+    """Project salary for next N years with promotion probability"""
+    projections = []
+    current = base_salary
+    current_exp = exp
+    for yr in range(1, years+1):
+        current_exp += 1
+        growth_rate = {"senior": 0.04, "mid": 0.06, "junior": 0.09}.get(tier, 0.05)
+        # Promotion bump every 3 years
+        promotion_bump = 0
+        if yr % 3 == 0:
+            promotion_bump = {"junior": 0.12, "mid": 0.10, "senior": 0.07}.get(tier, 0.08)
+            if yr % 3 == 0 and tier == "junior" and current_exp >= 7:
+                tier = "mid"
+            elif tier == "mid" and current_exp >= 15:
+                tier = "senior"
+        current = current * (1 + growth_rate + promotion_bump)
+        projections.append({
+            "year": 2025 + yr,
+            "salary": round(current),
+            "tier": tier,
+            "promotion": promotion_bump > 0
+        })
+    return projections
+
+# =========================================================
+# TAB 1: SALARY PREDICTOR
+# =========================================================
+def run_predictor(age, exp, edu, gender, job_title, name, feedback):
+    res = predict_local(int(age), int(exp), edu, gender)
+    predicted = res["predicted"]
+    low, high = res["low"], res["high"]
+    tier, exp_group, source = res["tier"], res["exp_group"], res["source"]
+
+    # Call Automation 1 (n8n salary prediction)
+    auto1_result = _n8n(N8N_SALARY_PREDICTION_URL, {
+        "age":int(age),"years_of_experience":int(exp),
+        "education_level":edu,"gender":gender,"job_title":job_title
+    })
+    if auto1_result and "predicted_salary" in auto1_result:
+        predicted = auto1_result["predicted_salary"]
+        low = round(predicted*0.92)
+        high = round(predicted*1.08)
+
+    # Call Automation 2 (HR feedback)
+    auto2_result = _n8n(N8N_HR_FEEDBACK_URL, {
+        "name": name or "Employee","career_tier":tier,"salary":predicted,
+        "years_of_experience":int(exp),"education_level":edu,
+        "feedback": feedback or "No feedback provided"
+    })
+    sentiment_adjustment = 0
+    sentiment_label = "Neutral"
+    sentiment_score = 0.5
+    if auto2_result and "error" not in auto2_result:
+        sentiment_adjustment = auto2_result.get("salary_adjustment", 0)
+        sentiment_score = auto2_result.get("sentiment_score", 0.5)
+        alert = auto2_result.get("alert","")
+        if "POSITIVE" in alert.upper(): sentiment_label = "Positive 😊"
+        elif "NEGATIVE" in alert.upper(): sentiment_label = "Negative 😟"
+        else: sentiment_label = "Neutral 😐"
+        if sentiment_adjustment:
+            predicted = round(predicted * (1 + sentiment_adjustment/100))
+            low = round(predicted*0.92)
+            high = round(predicted*1.08)
+
+    # Salary projection
+    projections = project_salary(predicted, int(exp), edu, tier)
+
+    # Gauge chart
+    fig_gauge = go.Figure(go.Indicator(
+        mode="gauge+number",value=predicted,
+        number={"prefix":"$","valueformat":",.0f","font":{"size":32,"color":PURPLE}},
+        gauge={"axis":{"range":[25000,250000],"tickprefix":"$","tickformat":",.0f","tickfont":{"size":9}},"bar":{"color":GOLD,"thickness":0.3},"bgcolor":"white","borderwidth":0,"steps":[{"range":[25000,80000],"color":"#e8f5f0"},{"range":[80000,140000],"color":"#d4ecff"},{"range":[140000,250000],"color":"#ede8ff"}],"threshold":{"line":{"color":"#FF9B9B","width":3},"thickness":0.8,"value":high}},
+        title={"text":"Predicted Annual Salary","font":{"size":13,"color":PURPLE}},
+    ))
+    fig_gauge.update_layout(height=280,paper_bgcolor="rgba(255,255,255,0.97)",margin=dict(l=30,r=30,t=60,b=10))
+
+    # Projection chart
+    years = [p["year"] for p in projections]
+    salaries = [p["salary"] for p in projections]
+    promotions = [p["promotion"] for p in projections]
+    proj_colors = ["#F2C637" if p else "#45FFCA" for p in promotions]
+
+    fig_proj = go.Figure()
+    fig_proj.add_trace(go.Scatter(
+        x=[2025]+years, y=[predicted]+salaries,
+        mode="lines", name="Salary Trajectory",
+        line=dict(color="#45FFCA",width=3),
+        fill="tozeroy", fillcolor="rgba(69,255,202,0.1)"
+    ))
+    for i,(yr,sal,promo) in enumerate(zip(years,salaries,promotions)):
+        if promo:
+            fig_proj.add_trace(go.Scatter(
+                x=[yr],y=[sal],mode="markers+text",
+                marker=dict(color="#F2C637",size=14,symbol="star"),
+                text=["Promotion!"],textposition="top center",
+                textfont=dict(size=10,color="#F2C637"),
+                showlegend=False,
+                hovertemplate=f"Year: {yr}<br>Salary: ${sal:,.0f}<br>⭐ Promotion<extra></extra>"
+            ))
+    fig_proj.add_annotation(x=2025,y=predicted,text=f"Now: ${predicted:,.0f}",
+        showarrow=True,arrowhead=2,font=dict(size=10,color=PURPLE))
+    fig_proj.update_layout(**_layout(height=300,showlegend=False,
+        title=dict(text="📈 5-Year Salary Projection"),
+        xaxis=dict(tickformat="d"),yaxis=dict(tickprefix="$",tickformat=",.0f")))
+
+    # Build automation status HTML
+    tier_emoji = {"junior":"🌱","mid":"📈","senior":"⭐"}.get(tier,"")
+    sent_color = "#45FFCA" if "Positive" in sentiment_label else "#FF9B9B" if "Negative" in sentiment_label else "#D3D1C7"
+    adj_text = f"{sentiment_adjustment:+.1f}%" if sentiment_adjustment else "0%"
+
+    auto1_status = "✅ Connected" if auto1_result and "error" not in auto1_result else ("⚠️ "+auto1_result.get("error","Error")[:30] if auto1_result else "⚙️ Not configured")
+    auto2_status = "✅ "+sentiment_label if auto2_result and "error" not in auto2_result else ("⚠️ Error" if auto2_result else "⚙️ Not configured")
+
+    result_html = f"""
+    <div style="font-family:system-ui,sans-serif;">
+      <!-- Main prediction card -->
+      <div style="background:linear-gradient(135deg,rgba(40,9,109,0.06),rgba(242,198,55,0.09));
+                  border-radius:16px;padding:20px;border:1.5px solid rgba(40,9,109,0.12);margin-bottom:12px;">
+        <div style="display:grid;grid-template-columns:repeat(auto-fit,minmax(130px,1fr));gap:12px;">
+          <div style="text-align:center;">
+            <div style="font-size:9px;text-transform:uppercase;letter-spacing:2px;color:#8070b0;margin-bottom:3px;">Career Tier</div>
+            <div style="font-size:18px;font-weight:800;color:{PURPLE};">{tier_emoji} {tier.title()}</div>
+          </div>
+          <div style="text-align:center;">
+            <div style="font-size:9px;text-transform:uppercase;letter-spacing:2px;color:#8070b0;margin-bottom:3px;">Experience</div>
+            <div style="font-size:14px;font-weight:700;color:{PURPLE};">{exp_group}</div>
+          </div>
+          <div style="text-align:center;">
+            <div style="font-size:9px;text-transform:uppercase;letter-spacing:2px;color:#8070b0;margin-bottom:3px;">Range</div>
+            <div style="font-size:13px;font-weight:700;color:{PURPLE};">${low:,.0f} – ${high:,.0f}</div>
+          </div>
+          <div style="text-align:center;">
+            <div style="font-size:9px;text-transform:uppercase;letter-spacing:2px;color:#8070b0;margin-bottom:3px;">Model</div>
+            <div style="font-size:10px;font-weight:600;color:#5a4090;">{source}</div>
+          </div>
+        </div>
+      </div>
+
+      <!-- Sentiment impact -->
+      <div style="background:rgba(255,255,255,0.95);border-radius:12px;padding:14px;
+                  border-left:4px solid {sent_color};margin-bottom:12px;">
+        <div style="font-size:9px;text-transform:uppercase;letter-spacing:2px;color:#8070b0;margin-bottom:6px;">💬 Feedback Sentiment Impact</div>
+        <div style="display:flex;justify-content:space-between;align-items:center;">
+          <span style="font-size:14px;font-weight:700;color:{PURPLE};">{sentiment_label}</span>
+          <span style="font-size:13px;font-weight:800;color:{'#2ec4a0' if sentiment_adjustment>0 else '#FF9B9B' if sentiment_adjustment<0 else '#8070b0'};">Salary adj: {adj_text}</span>
+          <span style="font-size:12px;color:#8070b0;">Score: {sentiment_score:.2f}</span>
+        </div>
+      </div>
+
+      <!-- n8n Automation Status -->
+      <div style="background:rgba(40,9,109,0.04);border-radius:12px;padding:14px;border:1px solid rgba(40,9,109,0.1);">
+        <div style="font-size:9px;text-transform:uppercase;letter-spacing:2px;color:#8070b0;margin-bottom:8px;">🔗 n8n Automation Status</div>
+        <div style="display:grid;grid-template-columns:1fr 1fr;gap:8px;">
+          <div style="background:white;border-radius:8px;padding:10px;border:1px solid rgba(40,9,109,0.08);">
+            <div style="font-size:9px;color:#8070b0;margin-bottom:3px;">AUTOMATION 1 — Salary Prediction</div>
+            <div style="font-size:12px;font-weight:700;color:{PURPLE};">{auto1_status}</div>
+          </div>
+          <div style="background:white;border-radius:8px;padding:10px;border:1px solid rgba(40,9,109,0.08);">
+            <div style="font-size:9px;color:#8070b0;margin-bottom:3px;">AUTOMATION 2 — HR Feedback Alert</div>
+            <div style="font-size:12px;font-weight:700;color:{PURPLE};">{auto2_status}</div>
+          </div>
+        </div>
+      </div>
+    </div>"""
+
+    # 5-year table
+    proj_rows = "".join([
+        f'<tr style="background:{"rgba(242,198,55,0.15)" if p["promotion"] else "white"};">'
+        f'<td style="padding:8px 12px;font-weight:600;">{p["year"]}</td>'
+        f'<td style="padding:8px 12px;">${p["salary"]:,.0f}</td>'
+        f'<td style="padding:8px 12px;">{p["tier"].title()}</td>'
+        f'<td style="padding:8px 12px;">{"⭐ Promotion!" if p["promotion"] else "—"}</td>'
+        f'</tr>'
+        for p in projections
+    ])
+    proj_table = f"""
+    <div style="font-family:system-ui,sans-serif;background:white;border-radius:12px;overflow:hidden;border:1px solid rgba(40,9,109,0.1);">
+      <div style="background:{PURPLE};padding:12px 16px;color:white;font-weight:700;font-size:13px;">📅 5-Year Salary Forecast</div>
+      <table style="width:100%;border-collapse:collapse;font-size:13px;">
+        <thead><tr style="background:rgba(40,9,109,0.05);">
+          <th style="padding:8px 12px;text-align:left;color:#8070b0;font-size:10px;text-transform:uppercase;">Year</th>
+          <th style="padding:8px 12px;text-align:left;color:#8070b0;font-size:10px;text-transform:uppercase;">Projected Salary</th>
+          <th style="padding:8px 12px;text-align:left;color:#8070b0;font-size:10px;text-transform:uppercase;">Tier</th>
+          <th style="padding:8px 12px;text-align:left;color:#8070b0;font-size:10px;text-transform:uppercase;">Event</th>
+        </tr></thead>
+        <tbody>{proj_rows}</tbody>
+      </table>
+    </div>"""
+
+    return fig_gauge, fig_proj, result_html, proj_table
+
+# =========================================================
+# TAB 2: SALARY ANALYZER
+# =========================================================
+def run_analyzer(salary, age, exp, edu, gender, job_title):
+    salary = float(salary)
+    tier = get_tier(int(age), int(exp), edu)
+    exp_group = get_exp_group(int(exp))
+
+    # Call Automation 3
+    auto3_result = _n8n(N8N_ANOMALY_DETECTION_URL, {
+        "salary":salary,"age":int(age),
+        "years_of_experience":int(exp),"education_level":edu
+    })
+
+    # Local anomaly calculation as fallback
+    base_expected = {"Bachelor's":55000,"Master's":72000,"PhD":92000}.get(edu,55000)
+    expected = base_expected + (int(exp)*3200) + ((int(age)-22)*250)
+    deviation = ((salary - expected) / expected) * 100
+
+    if auto3_result and "error" not in auto3_result:
+        is_anomaly = auto3_result.get("anomaly", False)
+        flag = auto3_result.get("flag", "NORMAL RANGE")
+        severity = auto3_result.get("severity", "normal")
+        dev = auto3_result.get("deviation_pct", round(deviation,1))
+        expected_sal = auto3_result.get("expected_salary", round(expected))
+        auto3_status = "✅ Connected"
+    else:
+        dev = round(deviation,1)
+        expected_sal = round(expected)
+        is_anomaly = abs(dev) > 20
+        if dev > 40: flag,severity = "OVERPAID","high"
+        elif dev > 20: flag,severity = "SLIGHTLY OVERPAID","medium"
+        elif dev < -40: flag,severity = "UNDERPAID","high"
+        elif dev < -20: flag,severity = "SLIGHTLY UNDERPAID","medium"
+        else: flag,severity = "NORMAL RANGE","normal"
+        auto3_status = "⚙️ Using local calculation" if not auto3_result else "⚠️ Error"
+
+    # Color theme based on result
+    sev_color = {"high":"#FF9B9B","medium":"#F2C637","normal":"#45FFCA"}.get(severity,"#45FFCA")
+    sev_icon = {"high":"🚨","medium":"⚠️","normal":"✅"}.get(severity,"✅")
+
+    # Comparison gauge
+    fig_compare = go.Figure()
+    fig_compare.add_trace(go.Bar(
+        x=["Your Salary","Market Expected"],
+        y=[salary, expected_sal],
+        marker_color=[sev_color, "#45FFCA"],
+        text=[f"${salary:,.0f}", f"${expected_sal:,.0f}"],
+        textposition="outside",
+        hovertemplate="%{x}: $%{y:,.0f}<extra></extra>"
+    ))
+    fig_compare.update_layout(**_layout(height=320,showlegend=False,
+        title=dict(text="Your Salary vs Market Expected"),
+        yaxis=dict(tickprefix="$",tickformat=",.0f")))
+
+    # Deviation gauge
+    fig_dev = go.Figure(go.Indicator(
+        mode="gauge+number+delta",value=dev,
+        number={"suffix":"%","valueformat":".1f","font":{"size":28,"color":PURPLE}},
+        delta={"reference":0,"valueformat":".1f","suffix":"%"},
+        gauge={"axis":{"range":[-60,60],"ticksuffix":"%"},"bar":{"color":sev_color,"thickness":0.3},"bgcolor":"white","borderwidth":0,"steps":[{"range":[-60,-20],"color":"rgba(255,155,155,0.3)"},{"range":[-20,20],"color":"rgba(69,255,202,0.2)"},{"range":[20,60],"color":"rgba(208,156,250,0.3)"}],"threshold":{"line":{"color":"red","width":3},"thickness":0.8,"value":40 if dev>0 else -40}},
+        title={"text":"Salary Deviation from Market","font":{"size":13,"color":PURPLE}},
+    ))
+    fig_dev.update_layout(height=280,paper_bgcolor="rgba(255,255,255,0.97)",margin=dict(l=30,r=30,t=60,b=10))
+
+    # Percentile estimation
+    df = get_base_df()
+    if "Salary" in df.columns:
+        percentile = round((df["Salary"] < salary).mean() * 100)
+    else:
+        percentile = 50
+
+    # Build result HTML
+    result_html = f"""
+    <div style="font-family:system-ui,sans-serif;">
+      <!-- Main anomaly card -->
+      <div style="background:linear-gradient(135deg,rgba(255,255,255,0.98),rgba(255,255,255,0.95));
+                  border-radius:16px;padding:20px;border-left:6px solid {sev_color};margin-bottom:12px;
+                  box-shadow:0 4px 20px rgba(40,9,109,0.08);">
+        <div style="display:flex;align-items:center;gap:12px;margin-bottom:14px;">
+          <span style="font-size:32px;">{sev_icon}</span>
+          <div>
+            <div style="font-size:18px;font-weight:800;color:{PURPLE};">{flag}</div>
+            <div style="font-size:12px;color:#8070b0;">Salary anomaly detection result</div>
+          </div>
+        </div>
+        <div style="display:grid;grid-template-columns:repeat(auto-fit,minmax(120px,1fr));gap:12px;">
+          <div style="background:rgba(40,9,109,0.04);border-radius:10px;padding:12px;text-align:center;">
+            <div style="font-size:9px;text-transform:uppercase;letter-spacing:2px;color:#8070b0;margin-bottom:4px;">Your Salary</div>
+            <div style="font-size:16px;font-weight:800;color:{PURPLE};">${salary:,.0f}</div>
+          </div>
+          <div style="background:rgba(40,9,109,0.04);border-radius:10px;padding:12px;text-align:center;">
+            <div style="font-size:9px;text-transform:uppercase;letter-spacing:2px;color:#8070b0;margin-bottom:4px;">Market Expected</div>
+            <div style="font-size:16px;font-weight:800;color:{PURPLE};">${expected_sal:,.0f}</div>
+          </div>
+          <div style="background:rgba(40,9,109,0.04);border-radius:10px;padding:12px;text-align:center;">
+            <div style="font-size:9px;text-transform:uppercase;letter-spacing:2px;color:#8070b0;margin-bottom:4px;">Deviation</div>
+            <div style="font-size:16px;font-weight:800;color:{sev_color};">{dev:+.1f}%</div>
+          </div>
+          <div style="background:rgba(40,9,109,0.04);border-radius:10px;padding:12px;text-align:center;">
+            <div style="font-size:9px;text-transform:uppercase;letter-spacing:2px;color:#8070b0;margin-bottom:4px;">Percentile</div>
+            <div style="font-size:16px;font-weight:800;color:{PURPLE};">Top {100-percentile}%</div>
+          </div>
+        </div>
+      </div>
+
+      <!-- Recommendation -->
+      <div style="background:rgba(40,9,109,0.04);border-radius:12px;padding:14px;border:1px solid rgba(40,9,109,0.1);margin-bottom:12px;">
+        <div style="font-size:9px;text-transform:uppercase;letter-spacing:2px;color:#8070b0;margin-bottom:6px;">💡 Recommendation</div>
+        <div style="font-size:13px;color:{PURPLE};font-weight:600;">
+          {'Your salary is significantly above market. Ensure your performance justifies this compensation.' if dev > 30 else
+           'Your salary is slightly above market — you are in a strong position.' if dev > 10 else
+           'Your salary is within market norms. You are fairly compensated.' if abs(dev) <= 10 else
+           'Your salary is slightly below market. Consider negotiating a raise.' if dev > -30 else
+           'Your salary is significantly below market. You may be underpaid — seek negotiation or new opportunities.'}
+        </div>
+      </div>
+
+      <!-- n8n status -->
+      <div style="background:rgba(40,9,109,0.04);border-radius:10px;padding:10px 14px;border:1px solid rgba(40,9,109,0.08);">
+        <div style="font-size:9px;text-transform:uppercase;letter-spacing:2px;color:#8070b0;margin-bottom:4px;">🔗 AUTOMATION 3 — Anomaly Detection</div>
+        <div style="font-size:12px;font-weight:700;color:{PURPLE};">{auto3_status}</div>
+      </div>
+    </div>"""
+
+    return fig_dev, fig_compare, result_html
+
+# =========================================================
+# TAB 3: INSIGHTS (Dashboard + AI)
+# =========================================================
+def build_edu_chart():
+    df=get_base_df()
+    if "Education Level" not in df.columns: return go.Figure()
+    grp=df.groupby("Education Level")["Salary"].mean().reset_index()
+    order=["Bachelor's","Master's","PhD"]
+    grp["Education Level"]=pd.Categorical(grp["Education Level"],categories=order,ordered=True)
+    grp=grp.sort_values("Education Level")
+    fig=go.Figure(go.Bar(x=grp["Education Level"],y=grp["Salary"],marker_color=P[:3],text=[f"${v:,.0f}" for v in grp["Salary"]],textposition="outside",hovertemplate="<b>%{x}</b><br>$%{y:,.0f}<extra></extra>"))
+    fig.update_layout(**_layout(height=320,showlegend=False,title=dict(text="Avg Salary by Education"),yaxis=dict(tickprefix="$",tickformat=",.0f")))
+    return fig
+
+def build_exp_chart():
+    df=get_base_df().copy()
+    if "Years of Experience" not in df.columns: return go.Figure()
+    df["eg"]=df["Years of Experience"].apply(get_exp_group)
+    grp=df.groupby("eg")["Salary"].mean().reset_index()
+    order=["0-5 years","6-10 years","11-15 years","16-20 years","20+"]
+    grp["eg"]=pd.Categorical(grp["eg"],categories=order,ordered=True)
+    grp=grp.sort_values("eg")
+    fig=go.Figure(go.Bar(x=grp["eg"],y=grp["Salary"],marker_color=P[:5],text=[f"${v:,.0f}" for v in grp["Salary"]],textposition="outside",hovertemplate="<b>%{x}</b><br>$%{y:,.0f}<extra></extra>"))
+    fig.update_layout(**_layout(height=320,showlegend=False,title=dict(text="Avg Salary by Experience"),yaxis=dict(tickprefix="$",tickformat=",.0f")))
+    return fig
+
+def build_gender_chart():
+    df=get_base_df()
+    if "Gender" not in df.columns: return go.Figure()
+    grp=df.groupby("Gender")["Salary"].mean().reset_index()
+    fig=go.Figure(go.Bar(x=grp["Gender"],y=grp["Salary"],marker_color=[P[0],P[2]],text=[f"${v:,.0f}" for v in grp["Salary"]],textposition="outside",hovertemplate="<b>%{x}</b><br>$%{y:,.0f}<extra></extra>"))
+    fig.update_layout(**_layout(height=320,showlegend=False,title=dict(text="Avg Salary by Gender"),yaxis=dict(tickprefix="$",tickformat=",.0f")))
+    return fig
+
+def build_tier_chart():
+    df=get_base_df()
+    if "career_tier" not in df.columns: return go.Figure()
+    grp=df.groupby("career_tier")["Salary"].mean().reset_index()
+    order=["junior","mid","senior"]
+    grp["career_tier"]=pd.Categorical(grp["career_tier"],categories=order,ordered=True)
+    grp=grp.sort_values("career_tier")
+    fig=go.Figure(go.Bar(x=grp["career_tier"].str.title(),y=grp["Salary"],marker_color=[P[0],P[1],P[2]],text=[f"${v:,.0f}" for v in grp["Salary"]],textposition="outside",hovertemplate="<b>%{x}</b><br>$%{y:,.0f}<extra></extra>"))
+    fig.update_layout(**_layout(height=320,showlegend=False,title=dict(text="Avg Salary by Career Tier"),yaxis=dict(tickprefix="$",tickformat=",.0f")))
+    return fig
+
+def build_prog_chart():
+    df=get_prog_df()
+    if df.empty or "year" not in df.columns: return go.Figure()
+    avg=df.groupby(["year","career_tier"])["salary_that_year"].mean().reset_index()
+    fig=go.Figure()
+    for i,t in enumerate(["junior","mid","senior"]):
+        sub=avg[avg["career_tier"]==t]
+        fig.add_trace(go.Scatter(x=sub["year"],y=sub["salary_that_year"],name=t.title(),mode="lines+markers",line=dict(color=P[i],width=2.5),marker=dict(size=7),hovertemplate=f"<b>{t.title()}</b><br>%{{x}}: $%{{y:,.0f}}<extra></extra>"))
+    fig.update_layout(**_layout(height=340,title=dict(text="Salary Progression by Career Tier (2020–2024)"),yaxis=dict(tickprefix="$",tickformat=",.0f"),xaxis=dict(tickformat="d")))
+    return fig
+
+def build_sentiment_chart():
+    df=get_feed_df().copy()
+    if df.empty: return go.Figure()
+    try:
+        from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
+        ana=SentimentIntensityAnalyzer()
+        df["score"]=df["feedback_comment"].apply(lambda x: ana.polarity_scores(str(x))["compound"])
+        df["sentiment"]=df["score"].apply(lambda s: "positive" if s>=0.05 else ("negative" if s<=-0.05 else "neutral"))
+    except:
+        df["sentiment"]=df["career_tier"].map({"senior":"positive","mid":"positive","junior":"neutral"}).fillna("neutral")
+    counts=df.groupby(["career_tier","sentiment"]).size().unstack(fill_value=0).reset_index()
+    for c in ["negative","neutral","positive"]:
+        if c not in counts.columns: counts[c]=0
+    tot=counts[["negative","neutral","positive"]].sum(axis=1)
+    for c in ["negative","neutral","positive"]: counts[c]=(counts[c]/tot*100).round(1)
+    order=["junior","mid","senior"]
+    counts["career_tier"]=pd.Categorical(counts["career_tier"],categories=order,ordered=True)
+    counts=counts.sort_values("career_tier")
+    colors={"negative":"#FF9B9B","neutral":"#D3D1C7","positive":"#45FFCA"}
+    fig=go.Figure()
+    for s in ["negative","neutral","positive"]:
+        fig.add_trace(go.Bar(x=counts["career_tier"].str.title(),y=counts[s],name=s.title(),marker_color=colors[s],text=counts[s].apply(lambda v: f"{v:.0f}%"),textposition="inside",hovertemplate=f"<b>{s.title()}</b>: %{{y:.1f}}%<extra></extra>"))
+    fig.update_layout(**_layout(height=320,barmode="stack",title=dict(text="Feedback Sentiment by Career Tier (%)"),yaxis=dict(ticksuffix="%",range=[0,100])))
+    return fig
+
+def build_scatter_chart():
+    df=get_base_df()
+    if "Years of Experience" not in df.columns: return go.Figure()
+    tc={"junior":P[0],"mid":P[1],"senior":P[2]}
+    fig=go.Figure()
+    for t in ["junior","mid","senior"]:
+        sub=df[df["career_tier"]==t]
+        fig.add_trace(go.Scatter(x=sub["Years of Experience"],y=sub["Salary"],mode="markers",name=t.title(),marker=dict(color=tc[t],size=5,opacity=0.65),hovertemplate="Exp: %{x}y<br>$%{y:,.0f}<extra></extra>"))
+    fig.update_layout(**_layout(height=320,title=dict(text="Experience vs Salary by Career Tier"),xaxis=dict(title="Years of Experience"),yaxis=dict(tickprefix="$",tickformat=",.0f")))
+    return fig
+
+CHART_MAP={"salary_education":build_edu_chart,"salary_experience":build_exp_chart,"gender_salary":build_gender_chart,"career_tier":build_tier_chart,"salary_progression":build_prog_chart,"sentiment":build_sentiment_chart,"scatter":build_scatter_chart}
+
+def render_kpis():
+    df=get_base_df()
+    n=len(df); avg=df["Salary"].mean() if "Salary" in df.columns else 0
+    avg_exp=df["Years of Experience"].mean() if "Years of Experience" in df.columns else 0
+    def card(icon,label,value,color):
+        return (f'<div style="background:rgba(255,255,255,.9);border-radius:14px;padding:14px 10px;text-align:center;'
+                f'border:1.5px solid rgba(255,255,255,.8);box-shadow:0 4px 14px rgba(40,9,109,.07);border-top:3px solid {color};">'
+                f'<div style="font-size:20px;margin-bottom:4px;">{icon}</div>'
+                f'<div style="color:#8070b0;font-size:8px;text-transform:uppercase;letter-spacing:2px;margin-bottom:4px;font-weight:800;">{label}</div>'
+                f'<div style="color:#1a0a3d;font-size:14px;font-weight:800;">{value}</div></div>')
+    html=('<div style="display:grid;grid-template-columns:repeat(auto-fit,minmax(120px,1fr));gap:10px;margin-bottom:16px;">'
+          +card("👥","Employees",f"{n:,}",GOLD)+card("💰","Avg Salary",f"${avg:,.0f}","#45FFCA")
+          +card("📅","Avg Experience",f"{avg_exp:.1f} yrs","#D09CFA")+card("🎯","Career Tiers","3","#FF9B9B")
+          +"</div>")
+    return html
+
+AI_SYSTEM = """You are an AI HR analytics assistant for the F2 Salary Prediction project at ESCP Business School.
+Dataset: Age, Gender, Education Level (Bachelor's/Master's/PhD), Job Title, Years of Experience, Salary, career_tier (junior/mid/senior), synthetic salary progression 2020-2024, VADER sentiment.
+Key findings: PhD earns ~35% more than Bachelor's. Senior tier = highest stable salaries. Junior grows fastest. Gender gap ~4%. RF model ~92-95% accuracy.
+Answer in 2-4 sentences with specific numbers. End with: ```json {"chart": "<n>"}```
+Chart options: salary_education | salary_experience | gender_salary | career_tier | salary_progression | sentiment | scatter | none"""
+
+def _keyword_reply(msg):
+    m=msg.lower()
+    if any(w in m for w in ["education","degree","bachelor","master","phd"]): return ("PhD holders earn ~35% more than Bachelor's graduates. Education is one of the top salary predictors.","salary_education")
+    if any(w in m for w in ["gender","male","female","gap"]): return ("There is a ~4% gender pay gap, with male employees earning slightly more on average.","gender_salary")
+    if any(w in m for w in ["experience","years","exp"]) and "prog" not in m: return ("Employees with 20+ years earn nearly 3× those with 0-5 years. The steepest growth is in the first 10 years.","salary_experience")
+    if any(w in m for w in ["tier","career","junior","mid","senior"]): return ("Senior employees earn the most and have stable salaries. Junior employees show the fastest growth rate.","career_tier")
+    if any(w in m for w in ["progress","growth","time","2020","trend"]): return ("Junior employees grow from 50% to 100% of base salary over 5 years. Seniors maintain high stable salaries.","salary_progression")
+    if any(w in m for w in ["sentiment","feedback","comment","review"]): return ("Senior employees receive 90%+ positive feedback. There is a moderate positive correlation between sentiment and salary.","sentiment")
+    if any(w in m for w in ["scatter","correlation","relationship"]): return ("Experience correlates strongly with salary across all tiers. The scatter shows clear tier separation.","scatter")
+    return ("Ask me about: education vs salary, experience trends, gender pay gap, career tier distributions, salary progression, or feedback sentiment.","none")
+
+def ai_chat(user_msg, history):
+    if not user_msg or not user_msg.strip(): return history or [], "", None
+    safe_history=[item for item in (history or []) if isinstance(item,dict) and "role" in item]
+    chart_name="none"
+    if LLM_ENABLED:
+        try:
+            msgs=[{"role":"system","content":AI_SYSTEM}]+safe_history[-10:]+[{"role":"user","content":user_msg}]
+            r=llm_client.chat_completion(model=MODEL_NAME,messages=msgs,temperature=0.3,max_tokens=400,stream=False)
+            raw=r["choices"][0]["message"]["content"] if isinstance(r,dict) else r.choices[0].message.content
+            hit=re.search(r'```json\s*(\{.*?\})\s*```',raw,re.DOTALL)
+            if hit: chart_name=json.loads(hit.group(1)).get("chart","none")
+            reply=re.sub(r'```json.*?```','',raw,flags=re.DOTALL).strip()
+        except: reply,chart_name=_keyword_reply(user_msg)
+    else: reply,chart_name=_keyword_reply(user_msg)
+    chart_fn=CHART_MAP.get(chart_name)
+    chart_out=chart_fn() if chart_fn else None
+    return safe_history+[{"role":"user","content":user_msg},{"role":"assistant","content":reply}],"",chart_out
+
+def refresh_insights():
+    return (render_kpis(),build_edu_chart(),build_gender_chart(),
+            build_exp_chart(),build_tier_chart(),build_prog_chart(),
+            build_sentiment_chart(),build_scatter_chart())
+
+# =========================================================
+# PIPELINE RUNNER
+# =========================================================
+def run_notebook_safe(env_key,default):
+    nb_name=os.environ.get(env_key,default).strip()
+    nb_in=BASE_DIR/nb_name
+    if not nb_in.exists():
+        return f"❌  {nb_name} not found.\nUpload the notebook to your HuggingFace Space Files tab."
+    try:
+        import papermill as pm
+        out=BASE_DIR/"runs"/f"run_{time.strftime('%Y%m%d-%H%M%S')}_{nb_name}"
+        out.parent.mkdir(exist_ok=True)
+        pm.execute_notebook(str(nb_in),str(out),cwd=str(BASE_DIR),log_output=True,progress_bar=False,execution_timeout=1800)
+        _CACHE.clear()
+        return f"✅  {nb_name} completed.\nCSVs: {[p.name for p in BASE_DIR.glob('*.csv')]}"
+    except Exception as e:
+        return f"❌  FAILED: {e}\n\n{traceback.format_exc()[-1500:]}"
+
+def run_nb1(): return run_notebook_safe("NB1","F2_Data_Extraction_and_synthetic_enrichment.ipynb")
+def run_nb2(): return run_notebook_safe("NB2","F2_quantitative_and_qualitative_analysis___prediction.ipynb")
+def run_both():
+    return f"{'='*44}\nNOTEBOOK 1\n{'='*44}\n{run_nb1()}\n\n{'='*44}\nNOTEBOOK 2\n{'='*44}\n{run_nb2()}"
+
+def load_css():
+    p=BASE_DIR/"style.css"
+    extra="""
+    .gr-button-primary{background:rgb(40,9,109)!important;border:none!important;}
+    .tab-nav button{font-weight:700!important;font-size:14px!important;}
+    """
+    return (p.read_text(encoding="utf-8") if p.exists() else "")+extra
+
+# =========================================================
+# UI
+# =========================================================
+with gr.Blocks(title="F2 Salary Intelligence — ESCP") as demo:
+
+    gr.Markdown("# F2 Salary Intelligence Platform\n*AI-powered salary prediction, analysis & insights — ESCP Big Data Project*", elem_id="escp_title")
+
+    # ── TAB 1: SALARY PREDICTOR ─────────────────────────────
+    with gr.Tab("🎯 Salary Predictor"):
+        gr.Markdown("### Predict your salary and future trajectory\n*Enter your profile — our AI predicts your salary, adjusts for feedback sentiment via n8n, and forecasts your next 5 years.*")
+        with gr.Row():
+            with gr.Column(scale=1):
+                gr.Markdown("#### 👤 Your Profile")
+                p_name   = gr.Textbox(label="Name (optional)", placeholder="e.g. Jane Doe")
+                p_age    = gr.Slider(18,70,value=30,step=1,label="Age")
+                p_exp    = gr.Slider(0,40,value=5,step=1,label="Years of Experience")
+                p_edu    = gr.Dropdown(["Bachelor's","Master's","PhD"],value="Bachelor's",label="Education Level",interactive=True)
+                p_job    = gr.Textbox(label="Job Title",value="Data Analyst")
+                p_gender = gr.Dropdown(["Male","Female"],value="Male",label="Gender",interactive=True)
+                gr.Markdown("#### 💬 Feedback & Sentiment")
+                gr.Markdown("*Your feedback influences salary via **Automation 2 (HR Alert)***")
+                p_feedback = gr.Textbox(label="Performance Feedback",placeholder='e.g. "Consistently exceeds expectations and shows strong leadership..."',lines=3,interactive=True)
+                btn_pred = gr.Button("🚀 Predict My Salary", variant="primary", size="lg")
+
+            with gr.Column(scale=1):
+                gr.Markdown("#### 📊 Prediction Results")
+                out_gauge  = gr.Plot(label="Salary Estimate")
+                out_result = gr.HTML()
+
+        gr.Markdown("#### 📈 5-Year Salary Trajectory")
+        with gr.Row():
+            out_proj_chart = gr.Plot(label="Salary Projection")
+            out_proj_table = gr.HTML()
+
+        gr.Markdown("#### 🗂️ Example Profiles")
+        gr.Examples(
+            examples=[
+                ["",28,3,"Bachelor's","Junior Data Analyst","Female","Shows promise and responds well to coaching."],
+                ["",35,10,"Master's","Senior Data Scientist","Male","Consistently exceeds expectations with strong leadership."],
+                ["",45,20,"PhD","Director of Analytics","Female","Outstanding mentor who drives cross-team innovation."],
+                ["",22,1,"Bachelor's","Intern","Male","Still ramping up but shows enthusiasm."],
+                ["",50,25,"Master's","VP of Engineering","Male","Completely fails deadlines and shows poor leadership."],
+            ],
+            inputs=[p_name,p_age,p_exp,p_edu,p_job,p_gender,p_feedback],
+        )
+        btn_pred.click(run_predictor,inputs=[p_age,p_exp,p_edu,p_gender,p_job,p_name,p_feedback],outputs=[out_gauge,out_proj_chart,out_result,out_proj_table])
+
+    # ── TAB 2: SALARY ANALYZER ──────────────────────────────
+    with gr.Tab("🔍 Salary Analyzer"):
+        gr.Markdown("### Is your salary fair?\n*Enter your current salary and profile — **Automation 3** detects anomalies and compares you to market benchmarks.*")
+        with gr.Row():
+            with gr.Column(scale=1):
+                gr.Markdown("#### 💰 Your Salary & Profile")
+                a_salary = gr.Number(label="Your Current Annual Salary ($)",value=75000)
+                a_age    = gr.Slider(18,70,value=30,step=1,label="Age")
+                a_exp    = gr.Slider(0,40,value=5,step=1,label="Years of Experience")
+                a_edu    = gr.Dropdown(["Bachelor's","Master's","PhD"],value="Bachelor's",label="Education Level",interactive=True)
+                a_gender = gr.Dropdown(["Male","Female"],value="Male",label="Gender",interactive=True)
+                a_job    = gr.Textbox(label="Job Title",value="Data Analyst")
+                btn_ana  = gr.Button("🔍 Analyze My Salary", variant="primary", size="lg")
+
+            with gr.Column(scale=1):
+                gr.Markdown("#### 📊 Analysis Results")
+                out_dev     = gr.Plot(label="Deviation Gauge")
+                out_ana_res = gr.HTML()
+
+        gr.Markdown("#### 📊 Market Comparison")
+        out_compare = gr.Plot(label="Salary Comparison")
+
+        btn_ana.click(run_analyzer,inputs=[a_salary,a_age,a_exp,a_edu,a_gender,a_job],outputs=[out_dev,out_compare,out_ana_res])
+
+    # ── TAB 3: INSIGHTS ─────────────────────────────────────
+    with gr.Tab("📊 Insights"):
+        gr.Markdown("### Data Insights & AI Analysis\n*Explore our findings — interactive charts + AI assistant*")
+        kpi_html = gr.HTML(value=render_kpis)
+        ref_btn  = gr.Button("🔄 Refresh Data", variant="secondary", size="sm")
+
+        with gr.Row():
+            with gr.Column(scale=1):
+                gr.Markdown("#### 💬 Ask our AI")
+                chatbot    = gr.Chatbot(label="AI Analytics Assistant",height=380)
+                user_input = gr.Textbox(label="Ask about the data",placeholder='e.g. "How does education affect salary?"',lines=1)
+                gr.Markdown("**Quick questions:**")
+                with gr.Row():
+                    ex1=gr.Button("📚 Education",size="sm"); ex2=gr.Button("📈 Career tiers",size="sm"); ex3=gr.Button("⚧ Gender gap",size="sm")
+                with gr.Row():
+                    ex4=gr.Button("📅 Progression",size="sm"); ex5=gr.Button("💬 Sentiment",size="sm"); ex6=gr.Button("🔗 Scatter",size="sm")
+
+            with gr.Column(scale=1):
+                ai_chart = gr.Plot(label="Interactive Chart")
+
+        gr.Markdown("#### 📊 Key Charts")
+        with gr.Row():
+            c_edu=gr.Plot(label="Education"); c_gender=gr.Plot(label="Gender")
+        with gr.Row():
+            c_exp=gr.Plot(label="Experience"); c_tier=gr.Plot(label="Career Tier")
+        with gr.Row():
+            c_prog=gr.Plot(label="Progression"); c_sent=gr.Plot(label="Sentiment")
+        c_scatter=gr.Plot(label="Scatter")
+
+        ex1.click(lambda: "How does education level affect salary?",outputs=[user_input])
+        ex2.click(lambda: "Show career tier salary differences",outputs=[user_input])
+        ex3.click(lambda: "Is there a gender pay gap?",outputs=[user_input])
+        ex4.click(lambda: "Show salary progression over time",outputs=[user_input])
+        ex5.click(lambda: "Do senior employees get more positive feedback?",outputs=[user_input])
+        ex6.click(lambda: "Show experience vs salary scatter",outputs=[user_input])
+        user_input.submit(ai_chat,inputs=[user_input,chatbot],outputs=[chatbot,user_input,ai_chart])
+
+        def on_refresh():
+            kpi,edu,gender,exp,tier,prog,sent,scatter=refresh_insights()
+            return kpi,edu,gender,exp,tier,prog,sent,scatter
+        ref_btn.click(on_refresh,outputs=[kpi_html,c_edu,c_gender,c_exp,c_tier,c_prog,c_sent,c_scatter])
+
+    # ── TAB 4: PIPELINE (minimal) ───────────────────────────
+    with gr.Tab("⚙️ Pipeline"):
+        gr.Markdown("### Data Pipeline\n*Regenerate synthetic data and retrain the model.*")
+        with gr.Row():
+            btn_nb1=gr.Button("▶ Step 1: Data Creation",variant="secondary")
+            btn_nb2=gr.Button("▶ Step 2: Analysis & Training",variant="secondary")
+        btn_all=gr.Button("⚡ Run Full Pipeline",variant="primary")
+        pipe_log=gr.Textbox(label="Log",lines=15,interactive=False)
+        btn_nb1.click(run_nb1,outputs=[pipe_log])
+        btn_nb2.click(run_nb2,outputs=[pipe_log])
+        btn_all.click(run_both,outputs=[pipe_log])
+
+demo.launch(css=load_css(), allowed_paths=[str(BASE_DIR)])

style.css

@@ -0,0 +1,360 @@
+/* --- Target the Gradio app wrapper for backgrounds --- */
+gradio-app,
+.gradio-app,
+.main,
+#app,
+[data-testid="app"] {
+  background-color: #eaeefc !important;
+  background-image:
+    url('https://huggingface.co/spaces/ESCP/F2_Salary_Prediction_Model/resolve/main/background_top.png'),
+    url('https://huggingface.co/spaces/ESCP/F2_Salary_Prediction_Model/resolve/main/background_mid.png'),
+    url('https://huggingface.co/spaces/ESCP/F2_Salary_Prediction_Model/resolve/main/background_bottom.png') !important;
+  background-position:
+    top center,
+    0 440px,
+    bottom center !important;
+  background-repeat:
+    no-repeat,
+    repeat-y,
+    no-repeat !important;
+  background-size:
+    100% auto,
+    100% auto,
+    100% auto !important;
+  min-height: 100vh !important;
+}
+
+/* --- Fallback on html/body --- */
+html, body {
+  background-color: #eaeefc !important;
+  margin: 0 !important;
+  padding: 0 !important;
+  min-height: 100vh !important;
+}
+
+/* --- Main container --- */
+.gradio-container {
+  max-width: 100% !important;
+  width: 100% !important;
+  margin: 0 !important;
+  padding: 0 24px 150px 24px !important;
+  background: transparent !important;
+}
+
+/* --- Title in deep blue --- */
+#escp_title h1 {
+  color: #0d1b5e !important;
+  font-size: 3.2rem !important;
+  font-weight: 900 !important;
+  text-align: center !important;
+  margin: 0 0 10px 0 !important;
+  letter-spacing: -0.5px !important;
+  text-shadow: 0 2px 12px rgba(74,110,224,0.15) !important;
+}
+
+/* --- Subtitle in blue --- */
+#escp_title p, #escp_title em {
+  color: #3a4888 !important;
+  text-align: center !important;
+  font-size: 1.05rem !important;
+  font-weight: 500 !important;
+}
+
+/* --- Tab bar background --- */
+.tabs > .tab-nav,
+.tab-nav,
+div[role="tablist"],
+.svelte-tabs > .tab-nav {
+  background: rgba(26,40,104,0.75) !important;
+  border-radius: 10px 10px 0 0 !important;
+  padding: 4px !important;
+  backdrop-filter: blur(8px) !important;
+}
+
+/* --- ALL tab buttons: bigger, bolder --- */
+.tabs > .tab-nav button,
+.tab-nav button,
+div[role="tablist"] button,
+button[role="tab"],
+.svelte-tabs button,
+.tab-nav > button,
+.tabs button {
+  color: #ffffff !important;
+  font-weight: 700 !important;
+  font-size: 1rem !important;
+  border: none !important;
+  background: transparent !important;
+  padding: 14px 28px !important;
+  border-radius: 8px 8px 0 0 !important;
+  opacity: 1 !important;
+  letter-spacing: 0.3px !important;
+}
+
+/* --- Selected tab: ESCP gold --- */
+.tabs > .tab-nav button.selected,
+.tab-nav button.selected,
+button[role="tab"][aria-selected="true"],
+button[role="tab"].selected,
+div[role="tablist"] button[aria-selected="true"],
+.svelte-tabs button.selected {
+  color: rgb(242,198,55) !important;
+  background: rgba(255,255,255,0.15) !important;
+}
+
+/* --- Unselected tabs: ensure visibility --- */
+.tabs > .tab-nav button:not(.selected),
+.tab-nav button:not(.selected),
+button[role="tab"][aria-selected="false"],
+button[role="tab"]:not(.selected),
+div[role="tablist"] button:not([aria-selected="true"]) {
+  color: #ffffff !important;
+  opacity: 1 !important;
+}
+
+/* --- White card panels --- */
+.gradio-container .gr-block,
+.gradio-container .gr-box,
+.gradio-container .gr-panel,
+.gradio-container .gr-group {
+  background: #ffffff !important;
+  border-radius: 10px !important;
+}
+
+/* --- Tab content area --- */
+.tabitem {
+  background: rgba(255,255,255,0.96) !important;
+  border-radius: 0 0 10px 10px !important;
+  padding: 16px !important;
+}
+
+/* --- Inputs --- */
+.gradio-container input,
+.gradio-container textarea,
+.gradio-container select {
+  background: #ffffff !important;
+  border: 1px solid #d1d5db !important;
+  border-radius: 8px !important;
+}
+
+/* --- Buttons: ESCP purple primary --- */
+.gradio-container button:not([role="tab"]) {
+  font-weight: 600 !important;
+  padding: 10px 16px !important;
+  border-radius: 10px !important;
+}
+
+button.primary {
+  background-color: rgb(40,9,109) !important;
+  color: #ffffff !important;
+  border: none !important;
+}
+
+button.primary:hover {
+  background-color: rgb(60,20,140) !important;
+}
+
+button.secondary {
+  background-color: #ffffff !important;
+  color: rgb(40,9,109) !important;
+  border: 2px solid rgb(40,9,109) !important;
+}
+
+button.secondary:hover {
+  background-color: rgb(240,238,250) !important;
+}
+
+/* --- Dataframes --- */
+[data-testid="dataframe"] {
+  background-color: #ffffff !important;
+  border-radius: 10px !important;
+}
+
+table {
+  font-size: 0.85rem !important;
+}
+
+/* --- Chatbot --- */
+.gr-chatbot {
+  min-height: 380px !important;
+  background-color: #ffffff !important;
+  border-radius: 12px !important;
+}
+
+.gr-chatbot .message.user {
+  background-color: rgb(232,225,250) !important;
+  border-radius: 12px !important;
+}
+
+.gr-chatbot .message.bot {
+  background-color: #f3f4f6 !important;
+  border-radius: 12px !important;
+}
+
+/* --- Gallery --- */
+.gallery {
+  background: #ffffff !important;
+  border-radius: 10px !important;
+}
+
+/* --- Log textbox --- */
+textarea {
+  font-family: monospace !important;
+  font-size: 0.8rem !important;
+}
+
+/* --- Markdown headings inside tabs --- */
+.tabitem h3 {
+  color: rgb(40,9,109) !important;
+  font-weight: 700 !important;
+}
+
+.tabitem h4 {
+  color: #374151 !important;
+}
+
+/* --- Examples row --- */
+.examples-row button {
+  background: rgb(240,238,250) !important;
+  color: rgb(40,9,109) !important;
+  border: 1px solid rgb(40,9,109) !important;
+  border-radius: 8px !important;
+  font-size: 0.85rem !important;
+}
+
+.examples-row button:hover {
+  background: rgb(232,225,250) !important;
+}
+
+/* --- Header / footer: transparent over banner --- */
+header, header *,
+footer, footer * {
+  background: transparent !important;
+  box-shadow: none !important;
+}
+
+footer a, footer button,
+header a, header button {
+  background: transparent !important;
+  border: none !important;
+  box-shadow: none !important;
+}
+
+#footer, #footer *,
+[class*="footer"], [class*="footer"] *,
+[class*="chip"], [class*="pill"], [class*="chip"] *, [class*="pill"] * {
+  background: transparent !important;
+  border: none !important;
+  box-shadow: none !important;
+}
+
+section footer {
+  background: transparent !important;
+}
+
+section footer button,
+section footer a {
+  background: transparent !important;
+  background-color: transparent !important;
+  border: none !important;
+  box-shadow: none !important;
+  color: white !important;
+}
+
+section footer button,
+section footer button * {
+  background: transparent !important;
+  background-color: transparent !important;
+  background-image: none !important;
+  box-shadow: none !important;
+  filter: none !important;
+}
+
+section footer button::before,
+section footer button::after {
+  background: transparent !important;
+  background-color: transparent !important;
+  background-image: none !important;
+  box-shadow: none !important;
+  filter: none !important;
+}
+
+section footer a,
+section footer a * {
+  background: transparent !important;
+  background-color: transparent !important;
+  box-shadow: none !important;
+}
+
+.gradio-container footer button,
+.gradio-container footer button *,
+.gradio-container .footer button,
+.gradio-container .footer button * {
+  background: transparent !important;
+  background-color: transparent !important;
+  background-image: none !important;
+  box-shadow: none !important;
+}
+
+.gradio-container footer button::before,
+.gradio-container footer button::after,
+.gradio-container .footer button::before,
+.gradio-container .footer button::after {
+  background: transparent !important;
+  background-color: transparent !important;
+  background-image: none !important;
+  box-shadow: none !important;
+}
+
+/* --- Blue slider thumbs & tracks --- */
+input[type="range"] {
+  accent-color: #2a4fd4 !important;
+}
+input[type="range"]::-webkit-slider-thumb {
+  background: #2a4fd4 !important;
+  border: 2px solid #ffffff !important;
+  box-shadow: 0 0 0 3px rgba(42,79,212,0.2) !important;
+}
+input[type="range"]::-moz-range-thumb {
+  background: #2a4fd4 !important;
+  border: 2px solid #ffffff !important;
+}
+input[type="range"]::-webkit-slider-runnable-track {
+  background: linear-gradient(to right, #2a4fd4, #6a8ef8) !important;
+}
+
+/* --- Bigger section headings inside tabs --- */
+.tabitem h3 {
+  color: #0d1b5e !important;
+  font-weight: 800 !important;
+  font-size: 1.25rem !important;
+}
+.tabitem h4 {
+  color: #1a2868 !important;
+  font-weight: 700 !important;
+  font-size: 1.05rem !important;
+}
+
+/* --- Bold markdown content headings --- */
+.tabitem p strong,
+.tabitem label {
+  font-weight: 700 !important;
+  color: #1a2868 !important;
+}
+
+/* --- Primary button bigger --- */
+button.primary,
+.gradio-container button.primary {
+  background-color: #1a2868 !important;
+  color: #ffffff !important;
+  font-size: 1.05rem !important;
+  padding: 14px 24px !important;
+  border-radius: 12px !important;
+  font-weight: 700 !important;
+  letter-spacing: 0.3px !important;
+  box-shadow: 0 4px 14px rgba(26,40,104,0.25) !important;
+}
+button.primary:hover,
+.gradio-container button.primary:hover {
+  background-color: #2a3d8e !important;
+  box-shadow: 0 6px 20px rgba(26,40,104,0.35) !important;
+}