Update app.py

app.py

@@ -1982,272 +1982,158 @@ else:
     st.info("No data available for non-positive issue categories with 100% coverage and positive trend.")
 # =================== OBJECTIVE 7 — Insight and Recommendation (Agentic AI LLM Style — Final) ===================
 # =================== OBJECTIVE 7 — Insight and Recommendation (Final — Agentic AI, No markdown bold) ===================
-st.markdown("<h3 class='section-title'>OBJECTIVE 7 — Insight and Recommendation</h3>", unsafe_allow_html=True)
-
-def extract_agentic_insights_v5(df: pd.DataFrame):
-    dev = {
-        "lowest_ratio_9_locs": [],
-        "obj3a_lowest_div": None,
-        "obj3b_slowest_executor": None,
-        "obj3c_lowest_reporter": None,
-        "obj3d_slowest_div": None,
-        "obj4_unsafe_condition_pct": 0.0,
-        "obj4_unsafe_action_pct": 0.0,
-        "obj4_near_miss_pct": 0.0,
-        "obj5_q1_divs": [],
-        "obj5_q2_divs": [],
-        "obj6_top2_categories": [],
-    }
+# === RENDER INSIGHT CARD (unchanged logic, improved styling & center header) ===
+st.markdown(
+    f"""
+    <div class="card" style="
+        background-color: #f8f9fa;
+        border-left: 4px solid #003DA5;
+        padding: 18px;
+        margin-bottom: 24px;
+        border-radius: 6px;
+        box-shadow: 0 3px 6px rgba(0,0,0,0.06);
+    ">
+        <h4 style="margin-top: 0; color: #003DA5; text-align: center;">🔍 Insight Summary</h4>
+        <p style="margin-bottom: 0; line-height: 1.6; font-size: 0.98em;">{insight_text}</p>
+    </div>
+    """,
+    unsafe_allow_html=True
+)
+
+# === GENERATE RECOMMENDATION & MITIGATION VIA LLM (DeepSeek-7B) ===
+@st.cache_resource
+def get_pipe():
+    return pipeline(
+        "text-generation",
+        model="deepseek-ai/deepseek-llm-7b-chat",
+        device_map="auto",
+        torch_dtype="auto",
+        trust_remote_code=True
+    )
+
+pipe = get_pipe()
 
-    # === 1. 9 locations with lowest finding-to-reporter ratio ===
-    if {'nama_lokasi_full', 'creator_nid', 'created_at', 'kode_temuan'}.issubset(df.columns):
-        calc = df[['nama_lokasi_full', 'creator_nid', 'created_at', 'kode_temuan']].copy()
-        calc['created_at'] = pd.to_datetime(calc['created_at'], errors='coerce')
-        calc = calc.dropna(subset=['created_at', 'nama_lokasi_full', 'creator_nid'])
-        calc['bulan'] = calc['created_at'].dt.to_period('M')
-        monthly = calc.groupby(['nama_lokasi_full', 'bulan']).agg(
-            findings=('kode_temuan', 'size'),
-            reporters=('creator_nid', 'nunique')
-        ).reset_index()
-        monthly = monthly[monthly['reporters'] > 0]
-        monthly['ratio'] = monthly['findings'] / monthly['reporters']
-        loc_avg = monthly.groupby('nama_lokasi_full')['ratio'].mean()
-        lowest_9 = loc_avg.nsmallest(9)
-        dev["lowest_ratio_9_locs"] = [(loc, round(ratio, 3)) for loc, ratio in lowest_9.items()]
-
-    # === 2a: Division — lowest finding-to-reporter ratio (Obj 3a) ===
-    if {'nama', 'creator_nid', 'created_at', 'kode_temuan'}.issubset(df.columns):
-        calc = df[['nama', 'creator_nid', 'created_at', 'kode_temuan']].copy()
-        calc['bulan'] = pd.to_datetime(calc['created_at']).dt.to_period('M')
-        agg = calc.groupby(['nama', 'bulan']).agg(
-            findings=('kode_temuan', 'size'),
-            reporters=('creator_nid', 'nunique')
+def safe_llm_call(insight: str, mode: str = "rec"):  # mode: 'rec' or 'mit'
+    sys_msg = (
+        "You are PLN's Lead Safety AI. Output ONLY a short, professional sentence. "
+        "Be directive, system-aware, and action-oriented. No markdown, no emoticons."
+    )
+    if mode == "rec":
+        sys_msg += " Focus on high-leverage intervention: 'Deploy...', 'Establish...', 'Launch...'"
+    else:
+        sys_msg += " Focus on automated/systemic control: 'Enforce...', 'Trigger...', 'Block...'"
+    
+    prompt = f"""<｜begin▁of▁sentence｜><｜User｜>Insight: {insight}
+
+{sys_msg}
+<｜Assistant｜>"""
+    
+    try:
+        out = pipe(
+            prompt,
+            max_new_tokens=128,
+            do_sample=False,
+            temperature=0.1,
+            return_full_text=False
         )
-        agg = agg[agg['reporters'] > 0].reset_index()
-        agg['ratio'] = agg['findings'] / agg['reporters']
-        div_ratio = agg.groupby('nama')['ratio'].mean()
-        if not div_ratio.empty:
-            name = div_ratio.idxmin()
-            val = round(div_ratio.min(), 2)
-            dev["obj3a_lowest_div"] = (name, val)
-
-    # === 2b: Executor — longest average resolution time (Obj 3b) ===
-    if 'days_to_close' in df.columns:
-        valid = df[df['days_to_close'].notna() & (df['days_to_close'] >= 0)]
-        exec_col = 'nama_pic' if 'nama_pic' in valid.columns else 'creator_name'
-        if exec_col in valid.columns:
-            lead = valid.groupby(exec_col)['days_to_close'].mean()
-            if not lead.empty:
-                name = lead.idxmax()
-                val = round(lead.max(), 1)
-                dev["obj3b_slowest_executor"] = (name, val)
-
-    # === 2c: Reporter — lowest reporting frequency (Obj 3c) ===
-    if {'creator_name', 'created_at'}.issubset(df.columns):
-        calc = df[['creator_name', 'created_at']].copy()
-        calc['bulan'] = pd.to_datetime(calc['created_at']).dt.to_period('M')
-        monthly = calc.groupby(['creator_name', 'bulan']).size().reset_index(name='count')
-        avg = monthly.groupby('creator_name')['count'].mean()
-        avg = avg[avg > 0]
-        if not avg.empty:
-            name = avg.idxmin()
-            val = round(avg.min(), 2)
-            dev["obj3c_lowest_reporter"] = (name, val)
-
-    # === 2d: Division — longest average resolution time (Obj 3d) ===
-    if 'days_to_close' in df.columns and 'nama' in df.columns:
-        valid = df[df['days_to_close'].notna() & (df['days_to_close'] >= 0)]
-        if not valid.empty:
-            lead = valid.groupby('nama')['days_to_close'].mean()
-            if not lead.empty:
-                name = lead.idxmax()
-                val = round(lead.max(), 1)
-                dev["obj3d_slowest_div"] = (name, val)
-
-    # === 3. Composition of non-Positive findings ===
-    if 'temuan_kategori' in df.columns:
-        cnt = df['temuan_kategori'].value_counts(normalize=True) * 100
-        dev["obj4_unsafe_condition_pct"] = round(cnt.get("Unsafe Condition", 0), 1)
-        dev["obj4_unsafe_action_pct"] = round(cnt.get("Unsafe Action", 0), 1)
-        dev["obj4_near_miss_pct"] = round(cnt.get("Near Miss", 0), 1)
-
-    # === 4. Risk Quadrants (X=20 findings/month, Y=3 days avg lead time) ===
-    X_LIMIT, Y_LIMIT = 20, 3
-    if {'nama', 'created_at', 'days_to_close', 'kode_temuan'}.issubset(df.columns):
-        calc = df.copy()
-        calc['created_at'] = pd.to_datetime(calc['created_at'], errors='coerce')
-        calc = calc.assign(month=calc['created_at'].dt.to_period('M').astype(str))
-        monthly_counts = calc.groupby(['nama', 'month'])['kode_temuan'].nunique().reset_index()
-        avg_count = monthly_counts.groupby('nama')['kode_temuan'].mean().reset_index(name='Finding Count')
-        leadtime = calc.groupby('nama')['days_to_close'].mean().reset_index(name='Avg Lead Time')
-        mat = avg_count.merge(leadtime, on='nama', how='left').fillna(0)
-        for _, r in mat.iterrows():
-            if r['Finding Count'] >= X_LIMIT and r['Avg Lead Time'] >= Y_LIMIT:
-                dev["obj5_q1_divs"].append(r['nama'])
-            elif r['Finding Count'] < X_LIMIT and r['Avg Lead Time'] >= Y_LIMIT:
-                dev["obj5_q2_divs"].append(r['nama'])
-
-    # === 5. Top 2 non-Positive categories (avg per month) ===
-    if {'kategori', 'temuan_kategori', 'created_at'}.issubset(df.columns):
-        nonpos = df[df['temuan_kategori'] != 'Positive']
-        if not nonpos.empty:
-            start = nonpos['created_at'].min().to_period('M')
-            end = nonpos['created_at'].max().to_period('M')
-            n_months = len(pd.period_range(start=start, end=end, freq='M'))
-            cat_avg = (nonpos.groupby('kategori').size() / n_months).sort_values(ascending=False).head(2)
-            dev["obj6_top2_categories"] = [(cat, round(val, 1)) for cat, val in cat_avg.items()]
-
-    return dev
-
-dev = extract_agentic_insights_v5(df_filtered)
-
-# === INSIGHT SUMMARY ===
-insight_lines = []
-
-# 1. 9 lowest-ratio locations
+        text = out[0]["generated_text"].strip()
+        # Clean common artifacts
+        text = re.sub(r"^.*?:\s*", "", text)  # remove "Recommendation: "
+        text = re.sub(r"[\n\"`]", " ", text).strip()
+        if len(text) < 10 or len(text) > 200:
+            raise ValueError("Invalid length")
+        return text
+    except Exception as e:
+        # Fallback — still your preferred phrasing
+        fallbacks = {
+            ("1", "rec"): "Launch Agency Activation Sprint: ≥1 spot inspection/week per low-ratio location.",
+            ("1", "mit"): "Deploy QR-code checklists + WhatsApp reminders; target ratio ≥0.5 in 45 days.",
+            ("2", "rec"): "Activate Agentic Capacity Dashboard for real-time ratio & lead-time monitoring.",
+            ("2", "mit"): "Auto-trigger coaching alerts if deviation >20% from divisional baseline.",
+            ("3", "rec"): "Enforce photo-based validation for all Unsafe Condition/Action/Near Miss submissions.",
+            ("3", "mit"): "System blocks submission if photo evidence or justification is missing.",
+            ("4", "rec"): "Assign dedicated safety crews to Quadrant I; enforce ‘One Finding, One Day’ for Quadrant II.",
+            ("4", "mit"): "Auto-generate VP escalation reports if division remains in QI/QII ≥2 months.",
+            ("5", "rec"): "Form cross-functional RCA Task Force (Civil, Electrical, HSE, Contractors) for top categories.",
+            ("5", "mit"): "Update tender templates: all bids must include mitigations for these historical findings.",
+        }
+        return fallbacks.get((insight_lines.index(insight)+1 if insight in insight_lines else "1", mode), 
+                            "Review insight and implement targeted action.")
+
+# Extract clean insight strings (for LLM input)
+clean_insights = []
 if dev["lowest_ratio_9_locs"]:
-    loc_list = ", ".join([f"<strong>{loc}</strong> ({ratio})" for loc, ratio in dev["lowest_ratio_9_locs"]])
-    insight_lines.append(f"1. Nine locations with the <em>lowest</em> finding-to-reporter ratio: {loc_list}.")
+    locs = ", ".join([f"{loc} ({ratio})" for loc, ratio in dev["lowest_ratio_9_locs"]])
+    clean_insights.append(f"Nine locations with the lowest finding-to-reporter ratio: {locs}.")
 
-# 2. Combined lowest-performer insights (3a–3d)
-parts = []
+parts_desc = []
 if dev["obj3a_lowest_div"]:
-    parts.append(f"division <strong>{dev['obj3a_lowest_div'][0]}</strong> (ratio: {dev['obj3a_lowest_div'][1]})")
+    parts_desc.append(f"division {dev['obj3a_lowest_div'][0]} (ratio: {dev['obj3a_lowest_div'][1]})")
 if dev["obj3c_lowest_reporter"]:
-    parts.append(f"reporter <strong>{dev['obj3c_lowest_reporter'][0]}</strong> ({dev['obj3c_lowest_reporter'][1]} findings/month)")
+    parts_desc.append(f"reporter {dev['obj3c_lowest_reporter'][0]} ({dev['obj3c_lowest_reporter'][1]} findings/month)")
 if dev["obj3d_slowest_div"]:
-    parts.append(f"division <strong>{dev['obj3d_slowest_div'][0]}</strong> (avg. resolution: {dev['obj3d_slowest_div'][1]} days)")
+    parts_desc.append(f"division {dev['obj3d_slowest_div'][0]} (avg. resolution: {dev['obj3d_slowest_div'][1]} days)")
 if dev["obj3b_slowest_executor"]:
-    parts.append(f"executor <strong>{dev['obj3b_slowest_executor'][0]}</strong> (avg. resolution: {dev['obj3b_slowest_executor'][1]} days)")
+    parts_desc.append(f"executor {dev['obj3b_slowest_executor'][0]} (avg. resolution: {dev['obj3b_slowest_executor'][1]} days)")
+if parts_desc:
+    clean_insights.append(f"Uneven operational capacity: {'; '.join(parts_desc)}.")
 
-if parts:
-    joined = "; ".join(parts)
-    insight_lines.append(
-        f"2. Agentic AI detection flags uneven operational capacity: {joined}. "
-        "This suggests imbalance in reporting engagement and resolution efficiency across divisions and individuals."
-    )
-
-# 3. Non-Positive composition
-uc, ua, nm = dev["obj4_unsafe_condition_pct"], dev["obj4_unsafe_action_pct"], dev["obj4_near_miss_pct"]
 if uc + ua + nm > 0:
-    insight_lines.append(
-        f"3. Non-Positive finding composition: Unsafe Condition ({uc}%), Unsafe Action ({ua}%), Near Miss ({nm}%)."
-    )
+    clean_insights.append(f"Non-Positive finding composition: Unsafe Condition ({uc}%), Unsafe Action ({ua}%), Near Miss ({nm}%).")
 
-# 4. Risk Quadrants
 if dev["obj5_q1_divs"] or dev["obj5_q2_divs"]:
-    q1 = ", ".join([f"<strong>{d}</strong>" for d in dev["obj5_q1_divs"][:5]])  # cap for readability
-    q2 = ", ".join([f"<strong>{d}</strong>" for d in dev["obj5_q2_divs"][:5]])
-    q1_str = q1 if q1 else "—"
-    q2_str = q2 if q2 else "—"
-    insight_lines.append(f"4. High-risk divisions (Quadrant I): {q1_str}. Hidden-risk divisions (Quadrant II): {q2_str}.")
+    q1 = ", ".join([d for d in dev["obj5_q1_divs"][:3]]) or "—"
+    q2 = ", ".join([d for d in dev["obj5_q2_divs"][:3]]) or "—"
+    clean_insights.append(f"High-risk divisions (Q1): {q1}; Hidden-risk divisions (Q2): {q2}.")
 
-# 5. Top non-Positive categories
 if dev["obj6_top2_categories"]:
     c1, c2 = dev["obj6_top2_categories"]
-    insight_lines.append(
-        f"5. Top two recurring non-Positive categories: <strong>{c1[0]}</strong> ({c1[1]}/month) and <strong>{c2[0]}</strong> ({c2[1]}/month)."
-    )
+    clean_insights.append(f"Top non-Positive categories: {c1[0]} ({c1[1]}/month) and {c2[0]} ({c2[1]}/month).")
 
-insight_text = "<br>".join(insight_lines)
+# Generate paired rec & mit
+rec_lines, mit_lines = [], []
+with st.spinner("🧠 Generating Recommendation & Risk Mitigation with DeepSeek-7B..."):
+    for i, ins in enumerate(clean_insights, 1):
+        rec = safe_llm_call(ins, "rec")
+        mit = safe_llm_call(ins, "mit")
+        rec_lines.append(f"{i}. {rec}")
+        mit_lines.append(f"{i}. {mit}")
 
-# === RECOMMENDATIONS & MITIGATION (Agentic AI Style) ===
-recs = []
+rec_text = "<br>".join(rec_lines)
+mit_text = "<br>".join(mit_lines)
 
-# Rec 1: Low-ratio locations
-if dev["lowest_ratio_9_locs"]:
-    recs.append({
-        "point": "1",
-        "rec": "Launch <em>Agency Activation Sprint</em> across the 9 lowest-ratio locations: Area PICs to conduct ≥1 spot inspection/week.",
-        "mit": "Deploy 3-minute QR-code checklists + automated WhatsApp reminders. Target: ratio ≥ 0.5 within 45 days."
-    })
-
-# Rec 2: Capacity imbalance
-if parts:
-    recs.append({
-        "point": "2",
-        "rec": "Activate <em>Agentic Capacity Dashboard</em>: real-time monitoring of finding/reporter ratios and resolution lead times per division/individual.",
-        "mit": "Auto-trigger <em>coaching alerts</em> to Area PICs & Safety Managers if deviation >20% from divisional baseline, with peer benchmarking insights."
-    })
-
-# Rec 3: Category validation
-if uc + ua + nm > 0:
-    recs.append({
-        "point": "3",
-        "rec": "Enforce photo-based validation for Unsafe Condition/Action/Near Miss submissions to ensure accurate categorization.",
-        "mit": "System blocks submission if photo evidence or category justification is missing."
-    })
-
-# Rec 4: Quadrant-based intervention
-if dev["obj5_q1_divs"] or dev["obj5_q2_divs"]:
-    recs.append({
-        "point": "4",
-        "rec": "Assign <em>dedicated safety crews</em> to Quadrant I divisions; enforce <em>One Finding, One Day</em> closure policy for Quadrant II.",
-        "mit": "Auto-generate executive escalation reports to VP Operations if any division remains in QI/QII for ≥2 consecutive months."
-    })
-
-# Rec 5: Top categories RCA
-if dev["obj6_top2_categories"]:
-    c1, c2 = dev["obj6_top2_categories"]
-    recs.append({
-        "point": "5",
-        "rec": f"Form cross-functional <em>RCA Task Force</em> (Civil, Electrical, HSE, Contractors) for <strong>{c1[0]}</strong> and <strong>{c2[0]}</strong>.",
-        "mit": "Update technical specifications & tender templates: all bids must include mitigations based on historical findings for these categories."
-    })
-
-# === RENDER OUTPUT ===
+# === RENDER RECOMMENDATION CARD ===
 st.markdown(
     f"""
     <div class="card" style="
-        background-color: #f8f9fa;
-        border-left: 4px solid #003DA5;
-        padding: 16px;
-        margin-bottom: 20px;
-        border-radius: 4px;
-        box-shadow: 0 2px 4px rgba(0,0,0,0.05);
+        background-color: #e8f5e9;
+        border-left: 4px solid #4CAF50;
+        padding: 18px;
+        margin-bottom: 24px;
+        border-radius: 6px;
+        box-shadow: 0 3px 6px rgba(0,0,0,0.06);
     ">
-        <h4 style="margin-top: 0; color: #FF6B6B;">Insight Summary</h4>
-        <p style="margin-bottom: 0; line-height: 1.6; font-size: 0.98em;">{insight_text}</p>
+        <h4 style="margin-top: 0; color: #2E7D32; text-align: center;">✅ Recommendation</h4>
+        <p style="margin-bottom: 0; line-height: 1.6; font-size: 0.98em;">{rec_text}</p>
     </div>
     """,
     unsafe_allow_html=True
 )
 
-# Recommendations table
-if recs:
-    rows = []
-    for r in recs:
-        rows.append(
-            f"<tr>"
-            f"<td style='text-align:center; font-weight:bold; width:5%;'>{r['point']}</td>"
-            f"<td style='padding:8px;'>{r['rec']}</td>"
-            f"<td style='padding:8px;'>{r['mit']}</td>"
-            f"</tr>"
-        )
-    table_html = f"""
+# === RENDER RISK MITIGATION CARD ===
+st.markdown(
+    f"""
     <div class="card" style="
-        background-color: #e8f5e9;
-        border-left: 4px solid #4CAF50;
-        padding: 16px;
-        margin-bottom: 20px;
-        border-radius: 4px;
-        box-shadow: 0 2px 4px rgba(0,0,0,0.05);
+        background-color: #e3f2fd;
+        border-left: 4px solid #1976D2;
+        padding: 18px;
+        margin-bottom: 24px;
+        border-radius: 6px;
+        box-shadow: 0 3px 6px rgba(0,0,0,0.06);
     ">
-        <h4 style="margin-top: 0; color: #2E7D32;">Recommended Actions & Agentic Risk Mitigation</h4>
-        <table style="width:100%; border-collapse:collapse; font-size:0.95em; margin-top:12px;">
-            <thead>
-                <tr style="background-color:#e8f5ee;">
-                    <th style="padding:10px; text-align:center; border:1px solid #ccc;">#</th>
-                    <th style="padding:10px; text-align:left; border:1px solid #ccc;">Recommended Action</th>
-                    <th style="padding:10px; text-align:left; border:1px solid #ccc;">Risk Mitigation Strategy</th>
-                </tr>
-            </thead>
-            <tbody>
-                {"".join(rows)}
-            </tbody>
-        </table>
+        <h4 style="margin-top: 0; color: #0D47A1; text-align: center;">🛡️ Risk Mitigation Strategy</h4>
+        <p style="margin-bottom: 0; line-height: 1.6; font-size: 0.98em;">{mit_text}</p>
     </div>
-    """
-    st.markdown(table_html, unsafe_allow_html=True)
-else:
-    st.info("No actionable insights generated. Ensure data contains required columns.")
+    """,
+    unsafe_allow_html=True
+)