ui update

app.py

@@ -5,9 +5,17 @@ run: `gradio main.py` to run the application
 
 import sys
 
+import gradio as gr
 from loguru import logger
 
-from app.ui import demo
+from app.service import (
+    analyze_trends,
+    connect_database,
+    detect_anomalies,
+    generate_analysis_report,
+    list_available_metrics,
+    query_timeseries,
+)
 
 logger.add(
     sys.stdout,
@@ -16,7 +24,118 @@ logger.add(
     level="INFO",
     colorize=True,
 )
+example_sensor = "temperature"
+example_start = "2019-06-15T02:54:00"
+example_end = "2019-06-17T02:54:00"
 
+with gr.Blocks() as demo:
+    gr.Markdown("# TimescaleDB Time Series Analyzer API (Gradio)")
+    with gr.Tab("Connect DB"):
+        connect_btn = gr.Button("Connect to TimescaleDB")
+        connect_out = gr.Textbox(label="Connection Result")
+        connect_btn.click(
+            fn=connect_database,
+            inputs=[],
+            outputs=connect_out,
+        )
+    with gr.Tab("List Metrics"):
+        list_btn = gr.Button("List Available Metrics")
+        list_out = gr.Textbox(label="Metrics")
+        list_btn.click(
+            fn=list_available_metrics,
+            inputs=[],
+            outputs=list_out,
+        )
+    with gr.Tab("Query Timeseries"):
+        sensor_id = gr.Textbox(label="Sensor ID", value=example_sensor)
+        start_time = gr.Textbox(label="Start Time (ISO)", value=example_start)
+        end_time = gr.Textbox(label="End Time (ISO)", value=example_end)
+        query_btn = gr.Button("Query")
+        query_out = gr.Textbox(label="Query Result")
+        query_btn.click(
+            fn=query_timeseries,
+            inputs=[sensor_id, start_time, end_time],
+            outputs=query_out,
+        )
+    with gr.Tab("Detect Anomalies"):
+        sensor_id2 = gr.Textbox(label="Sensor ID", value=example_sensor)
+        start_time2 = gr.Textbox(label="Start Time (ISO)", value=example_start)
+        end_time2 = gr.Textbox(label="End Time (ISO)", value=example_end)
+        algorithm = gr.Radio(
+            label="Algorithm",
+            choices=["zscore", "isolation_forest"],
+            value="zscore",
+        )
+        threshold = gr.Number(label="Z-Score Threshold", value=2.0)
+        contamination = gr.Number(
+            label="Isolation Forest Contamination",
+            value=0.1,
+            minimum=0.01,
+            maximum=0.5,
+            step=0.01,
+        )
+        anomaly_btn = gr.Button("Detect")
+        anomaly_out = gr.Textbox(label="Anomaly Result")
+        anomaly_btn.click(
+            fn=detect_anomalies,
+            inputs=[
+                sensor_id2,
+                start_time2,
+                end_time2,
+                threshold,
+                algorithm,
+                contamination,
+            ],
+            outputs=anomaly_out,
+        )
+    with gr.Tab("Analyze Trends"):
+        sensor_id3 = gr.Textbox(label="Sensor ID", value=example_sensor)
+        start_time3 = gr.Textbox(label="Start Time (ISO)", value=example_start)
+        end_time3 = gr.Textbox(label="End Time (ISO)", value=example_end)
+        trend_btn = gr.Button("Analyze")
+        trend_out = gr.Textbox(label="Trend Result")
+        trend_btn.click(
+            fn=analyze_trends,
+            inputs=[sensor_id3, start_time3, end_time3],
+            outputs=trend_out,
+        )
+    with gr.Tab("Generate Report"):
+        sensor_id4 = gr.Textbox(label="Sensor ID", value=example_sensor)
+        start_time4 = gr.Textbox(label="Start Time (ISO)", value=example_start)
+        end_time4 = gr.Textbox(label="End Time (ISO)", value=example_end)
+        include_anomalies = gr.Checkbox(label="Include Anomalies", value=True)
+        include_trends = gr.Checkbox(label="Include Trends", value=True)
+        user_question = gr.Textbox(label="User Question", value="")
+        anomaly_algorithm = gr.Radio(
+            label="Anomaly Detection Algorithm",
+            choices=["zscore", "isolation_forest"],
+            value="zscore",
+        )
+        anomaly_threshold = gr.Number(label="Z-Score Threshold", value=2.0)
+        anomaly_contamination = gr.Number(
+            label="Isolation Forest Contamination",
+            value=0.1,
+            minimum=0.01,
+            maximum=0.5,
+            step=0.01,
+        )
+        report_btn = gr.Button("Generate Report")
+        report_out = gr.Markdown(label="Report")
+        report_btn.click(
+            fn=generate_analysis_report,
+            inputs=[
+                sensor_id4,
+                start_time4,
+                end_time4,
+                include_anomalies,
+                include_trends,
+                user_question,
+                anomaly_algorithm,
+                anomaly_threshold,
+                anomaly_contamination,
+            ],
+            outputs=report_out,
+        )
 
 if __name__ == "__main__":
     demo.launch(mcp_server=True)

app/ui.py

@@ -1,90 +0,0 @@
-"""Definition of the Gradio UI."""
-
-import gradio as gr
-
-from app.service import (
-    analyze_trends,
-    connect_database,
-    detect_anomalies,
-    generate_analysis_report,
-    list_available_metrics,
-    query_timeseries,
-)
-
-example_sensor = "temperature"
-example_start = "2019-06-15T02:54:00"
-example_end = "2019-06-17T02:54:00"
-
-with gr.Blocks() as demo:
-    gr.Markdown("# TimescaleDB Time Series Analyzer API (Gradio)")
-    with gr.Tab("Connect DB"):
-        connect_btn = gr.Button("Connect to TimescaleDB")
-        connect_out = gr.Textbox(label="Connection Result")
-        connect_btn.click(
-            fn=connect_database,
-            inputs=[],
-            outputs=connect_out,
-        )
-    with gr.Tab("List Metrics"):
-        list_btn = gr.Button("List Available Metrics")
-        list_out = gr.Textbox(label="Metrics")
-        list_btn.click(
-            fn=list_available_metrics,
-            inputs=[],
-            outputs=list_out,
-        )
-    with gr.Tab("Query Timeseries"):
-        sensor_id = gr.Textbox(label="Sensor ID", value=example_sensor)
-        start_time = gr.Textbox(label="Start Time (ISO)", value=example_start)
-        end_time = gr.Textbox(label="End Time (ISO)", value=example_end)
-        query_btn = gr.Button("Query")
-        query_out = gr.Textbox(label="Query Result")
-        query_btn.click(
-            fn=query_timeseries,
-            inputs=[sensor_id, start_time, end_time],
-            outputs=query_out,
-        )
-    with gr.Tab("Detect Anomalies"):
-        sensor_id2 = gr.Textbox(label="Sensor ID", value=example_sensor)
-        start_time2 = gr.Textbox(label="Start Time (ISO)", value=example_start)
-        end_time2 = gr.Textbox(label="End Time (ISO)", value=example_end)
-        threshold = gr.Number(label="Threshold", value=2.0)
-        anomaly_btn = gr.Button("Detect")
-        anomaly_out = gr.Textbox(label="Anomaly Result")
-        anomaly_btn.click(
-            fn=detect_anomalies,
-            inputs=[sensor_id2, start_time2, end_time2, threshold],
-            outputs=anomaly_out,
-        )
-    with gr.Tab("Analyze Trends"):
-        sensor_id3 = gr.Textbox(label="Sensor ID", value=example_sensor)
-        start_time3 = gr.Textbox(label="Start Time (ISO)", value=example_start)
-        end_time3 = gr.Textbox(label="End Time (ISO)", value=example_end)
-        trend_btn = gr.Button("Analyze")
-        trend_out = gr.Textbox(label="Trend Result")
-        trend_btn.click(
-            fn=analyze_trends,
-            inputs=[sensor_id3, start_time3, end_time3],
-            outputs=trend_out,
-        )
-    with gr.Tab("Generate Report"):
-        sensor_id4 = gr.Textbox(label="Sensor ID", value=example_sensor)
-        start_time4 = gr.Textbox(label="Start Time (ISO)", value=example_start)
-        end_time4 = gr.Textbox(label="End Time (ISO)", value=example_end)
-        include_anomalies = gr.Checkbox(label="Include Anomalies", value=True)
-        include_trends = gr.Checkbox(label="Include Trends", value=True)
-        user_question = gr.Textbox(label="User Question", value="")
-        report_btn = gr.Button("Generate Report")
-        report_out = gr.Markdown(label="Report")
-        report_btn.click(
-            fn=generate_analysis_report,
-            inputs=[
-                sensor_id4,
-                start_time4,
-                end_time4,
-                include_anomalies,
-                include_trends,
-                user_question,
-            ],
-            outputs=report_out,
-        )