update

app.py

@@ -33,6 +33,7 @@ def parse_row_selection(df, rows_text: str):
 
 with gr.Blocks() as demo:
     gr.Markdown("# OMS Analyze — Prototype")
+    gr.Markdown("> Created by PEACE, Powered by AI, Version 0.0.1")
     with gr.Tabs():
         # Upload & Preview tab
         with gr.TabItem('Upload & Preview'):
@@ -74,54 +75,6 @@ with gr.Blocks() as demo:
             csv_up.change(fn=initial_preview, inputs=csv_up, outputs=[original_preview, cleansed_preview, clean_status])
             apply_clean.click(fn=apply_cleansing, inputs=[csv_up, remove_dup, missing_handling], outputs=[cleansed_preview, clean_status, download_cleansed])
 
-        # Recommendation tab
-        with gr.TabItem('Recommendation'):
-            gr.Markdown("**Usecase Scenario — Recommendation**: สร้างสรุปเหตุการณ์ (เช่น สรุปเหตุการณ์ไฟฟ้าล้ม) สำหรับแถวที่เลือก ปรับระดับรายละเอียด และเลือกใช้ Generative AI เพื่อเพิ่มความชัดเจน 및 ดาวน์โหลดไฟล์สรุป")
-            csv_in = gr.File(label='Upload CSV (data.csv)')
-            with gr.Row():
-                rows = gr.Textbox(label='Rows (comma-separated indexes) or empty = all', placeholder='e.g. 0,1,2')
-                use_hf = gr.Checkbox(label='Use Generative AI', value=False)
-                verbosity = gr.Radio(choices=['analyze','recommend'], value='analyze', label='Summary Type', interactive=True)
-                run_btn = gr.Button('Generate Summaries', interactive=True)
-            with gr.Row():
-                model_selector = gr.Dropdown(
-                    choices=[
-                        'meta-llama/Llama-3.1-8B-Instruct:novita',
-                        'meta-llama/Llama-4-Scout-17B-16E-Instruct:novita',
-                        'Qwen/Qwen3-VL-235B-A22B-Instruct:novita',
-                        'deepseek-ai/DeepSeek-R1:novita'
-                    ],
-                    value='meta-llama/Llama-3.1-8B-Instruct:novita',
-                    label='GenAI Model',
-                    interactive=True,
-                    visible=False
-                )
-            out = gr.Dataframe(headers=['EventNumber','OutageDateTime','Summary'])
-            status = gr.Textbox(label='Status', interactive=False)
-            download = gr.File(label='Download summaries')
-
-            def run_summarize(file, rows_text, use_hf_flag, verbosity_level):
-                print(f"Debug: file={file}, rows_text={rows_text}, use_hf_flag={use_hf_flag}, verbosity_level={verbosity_level}")
-                if file is None:
-                    return pd.DataFrame([], columns=['EventNumber','OutageDateTime','Summary']), 'No file provided', None
-                df = pd.read_csv(file.name, dtype=str)
-                df_sel = parse_row_selection(df, rows_text)
-                model = 'meta-llama/Llama-3.1-8B-Instruct:novita'  # default
-                res = summarize_events(df_sel, use_hf=use_hf_flag, verbosity=verbosity_level, model=model)
-                out_df = pd.DataFrame(res)
-                out_file = ROOT / 'outputs' / 'summaries_from_ui.csv'
-                out_file.parent.mkdir(exist_ok=True)
-                out_df.to_csv(out_file, index=False, encoding='utf-8-sig')
-                status_text = f"Summaries generated: {len(out_df)} rows. HF used: {use_hf_flag}"
-                return out_df, status_text, str(out_file)
-
-            def update_model_visibility(use_hf_flag):
-                return gr.update(visible=use_hf_flag, interactive=use_hf_flag)
-            
-            use_hf.change(fn=update_model_visibility, inputs=use_hf, outputs=model_selector)
-            
-            run_btn.click(fn=run_summarize, inputs=[csv_in, rows, use_hf, verbosity], outputs=[out, status, download])
-
         # Summary tab
         with gr.TabItem('Summary'):
             gr.Markdown("**Usecase Scenario — Summary**: สร้างสรุปภาพรวมของชุดข้อมูลทั้งหมด รวมสถิติพื้นฐาน และคำนวณดัชนีความน่าเชื่อถือ (เช่น SAIFI, SAIDI, CAIDI) พร้อมตัวเลือกใช้ Generative AI ในการขยายความ")
@@ -136,7 +89,8 @@ with gr.Blocks() as demo:
                         'meta-llama/Llama-3.1-8B-Instruct:novita',
                         'meta-llama/Llama-4-Scout-17B-16E-Instruct:novita',
                         'Qwen/Qwen3-VL-235B-A22B-Instruct:novita',
-                        'deepseek-ai/DeepSeek-R1:novita'
+                        'deepseek-ai/DeepSeek-R1:novita',
+                        'moonshotai/Kimi-K2-Instruct-0905:novita'
                     ],
                     value='meta-llama/Llama-3.1-8B-Instruct:novita',
                     label='GenAI Model',
@@ -188,11 +142,60 @@ with gr.Blocks() as demo:
             
             run_sum.click(fn=run_overall_summary, inputs=[csv_in_sum, use_hf_sum, total_customers, model_selector_sum], outputs=[ai_summary_out, basic_stats_out, reliability_out, sum_status])
 
+        # Recommendation tab
+        with gr.TabItem('Recommendation'):
+            gr.Markdown("**Usecase Scenario — Recommendation**: สร้างสรุปเหตุการณ์ (เช่น สรุปเหตุการณ์ไฟฟ้าขัอข้องหรือบำรุงรักษา) สำหรับแถวที่เลือก ปรับระดับรายละเอียด และเลือกใช้ Generative AI เพื่อเพิ่มความชัดเจน พร้อมดาวน์โหลดไฟล์สรุป")
+            csv_in = gr.File(label='Upload CSV (data.csv)')
+            with gr.Row():
+                rows = gr.Textbox(label='Rows (comma-separated indexes) or empty = all', placeholder='e.g. 0,1,2')
+                use_hf = gr.Checkbox(label='Use Generative AI', value=False)
+                verbosity = gr.Radio(choices=['analyze','recommend'], value='analyze', label='Summary Type', interactive=True)
+                run_btn = gr.Button('Generate Summaries', interactive=True)
+            with gr.Row():
+                model_selector = gr.Dropdown(
+                    choices=[
+                        'meta-llama/Llama-3.1-8B-Instruct:novita',
+                        'meta-llama/Llama-4-Scout-17B-16E-Instruct:novita',
+                        'Qwen/Qwen3-VL-235B-A22B-Instruct:novita',
+                        'deepseek-ai/DeepSeek-R1:novita',
+                        'moonshotai/Kimi-K2-Instruct-0905:novita'
+                    ],
+                    value='meta-llama/Llama-3.1-8B-Instruct:novita',
+                    label='GenAI Model',
+                    interactive=True,
+                    visible=False
+                )
+            out = gr.Dataframe(headers=['EventNumber','OutageDateTime','Summary'])
+            status = gr.Textbox(label='Status', interactive=False)
+            download = gr.File(label='Download summaries')
+
+            def run_summarize(file, rows_text, use_hf_flag, verbosity_level, model):
+                print(f"Debug: file={file}, rows_text={rows_text}, use_hf_flag={use_hf_flag}, verbosity_level={verbosity_level}, model={model}")
+                if file is None:
+                    return pd.DataFrame([], columns=['EventNumber','OutageDateTime','Summary']), 'No file provided', None
+                df = pd.read_csv(file.name, dtype=str)
+                df_sel = parse_row_selection(df, rows_text)
+                res = summarize_events(df_sel, use_hf=use_hf_flag, verbosity=verbosity_level, model=model)
+                out_df = pd.DataFrame(res)
+                out_file = ROOT / 'outputs' / 'summaries_from_ui.csv'
+                out_file.parent.mkdir(exist_ok=True)
+                out_df.to_csv(out_file, index=False, encoding='utf-8-sig')
+                status_text = f"Summaries generated: {len(out_df)} rows. HF used: {use_hf_flag}"
+                return out_df, status_text, str(out_file)
+
+            def update_model_visibility(use_hf_flag):
+                return gr.update(visible=use_hf_flag, interactive=use_hf_flag)
+            
+            use_hf.change(fn=update_model_visibility, inputs=use_hf, outputs=model_selector)
+            
+            run_btn.click(fn=run_summarize, inputs=[csv_in, rows, use_hf, verbosity, model_selector], outputs=[out, status, download])
+
+        # Anomaly Detection tab
         with gr.TabItem('Anomaly Detection'):
             gr.Markdown("**Usecase Scenario — Anomaly Detection**: ตรวจจับเหตุการณ์ที่มีพฤติกรรมผิดปกติในชุดข้อมูล (เช่น เหตุการณ์ที่มีค่าสูง/ต่ำผิดปกติ) โดยใช้หลาย algorithm ปรับระดับ contamination และส่งออกผลลัพธ์พร้อมธงความผิดปกติ")
             csv_in_anom = gr.File(label='Upload CSV for Anomaly')
             with gr.Row():
-                alg = gr.Radio(choices=['both','iso','lof','autoencoder'], value='both', label='Algorithm')
+                alg = gr.Radio(choices=['iso+lof','iso','lof','autoencoder'], value='iso+lof', label='Algorithm')
                 contamination = gr.Slider(minimum=0.01, maximum=0.2, value=0.05, step=0.01, label='Contamination')
                 run_anom = gr.Button('Run Anomaly Detection')
             anom_out = gr.Dataframe()
@@ -205,6 +208,9 @@ with gr.Blocks() as demo:
                 from scripts.anomaly import detect_anomalies
                 df = pd.read_csv(file.name, dtype=str)
                 res = detect_anomalies(df, contamination=contamination, algorithm=algorithm)
+                # Reorder columns to put ensemble_flag and final_flag at the end
+                cols = [c for c in res.columns if c not in ['ensemble_flag', 'final_flag']] + ['ensemble_flag', 'final_flag']
+                res = res[cols]
                 out_file = ROOT / 'outputs' / 'anomalies_from_ui.csv'
                 out_file.parent.mkdir(exist_ok=True)
                 res.to_csv(out_file, index=False, encoding='utf-8-sig')

scripts/anomaly.py

@@ -154,7 +154,7 @@ def explain_anomalies(df_fe: pd.DataFrame, explain_features=None):
     return z, explanations
 
 
-def detect_anomalies(df: pd.DataFrame, contamination: float = 0.05, algorithm: str = 'both') -> pd.DataFrame:
+def detect_anomalies(df: pd.DataFrame, contamination: float = 0.05, algorithm: str = 'iso+lof') -> pd.DataFrame:
     Xs, features, df_fe, scaler = build_feature_matrix(df)
     
     if algorithm == 'autoencoder':
@@ -175,13 +175,15 @@ def detect_anomalies(df: pd.DataFrame, contamination: float = 0.05, algorithm: s
         # ensemble: flag if both mark as outlier (-1)
         res['ensemble_flag'] = ((res['iso_pred'] == -1) & (res['lof_pred'] == -1))
 
-        # algorithm filter: if algorithm == 'iso' or 'lof' or 'both', compute final_flag
+        # algorithm filter: if algorithm == 'iso' or 'lof' or 'iso+lof', compute final_flag
         if algorithm == 'iso':
             res['final_flag'] = res['iso_pred'] == -1
         elif algorithm == 'lof':
             res['final_flag'] = res['lof_pred'] == -1
-        else:
+        elif algorithm == 'iso+lof':
             res['final_flag'] = res['ensemble_flag']
+        else:
+            raise ValueError(f"Unknown algorithm: {algorithm}")
 
     # explainability (same for all)
     z_df, explanations = explain_anomalies(df_fe)