Update app.py

app.py

@@ -1,27 +1,30 @@
 import gradio as gr
-from risk_model import predict_risk, retrain_model, get_history_df
+from risk_model import predict_risk, get_history_df
 import pandas as pd
 import matplotlib.pyplot as plt
 
 with gr.Blocks() as demo:
     gr.Markdown("## 🔥 Heating Mantle Safety Risk Predictor")
 
+    # 🔄 Reset button at the top
+    with gr.Row():
+        reset_btn = gr.Button("🔄 Reset All Fields")
+
+    # Input fields
     with gr.Row():
         temp = gr.Number(label="Max Temperature (°C)", value=100)
         duration = gr.Number(label="Duration (min)", value=30)
 
+    # Predict button
     with gr.Row():
         predict_btn = gr.Button("🔍 Predict")
-        retrain_btn = gr.Button("🔁 Retrain Model")
 
+    # Output fields
     result = gr.Textbox(label="Risk Prediction")
     alert = gr.Textbox(label="🚨 Alert Message")
     ist_time = gr.Textbox(label="Timestamp (IST)")
-    retrain_output = gr.Textbox(label="Retrain Status")
-
     summary = gr.Markdown()
     history_table = gr.Dataframe(headers=["Temperature", "Duration", "Risk", "Timestamp"], label="📈 Prediction History")
-
     plot = gr.Plot(label="📊 Risk Trend Chart")
 
     def classify(temp, duration):
@@ -30,12 +33,11 @@ with gr.Blocks() as demo:
 
         risk, timestamp = predict_risk(temp, duration)
 
-        if risk == "Low":
-            alert_msg = "✅ SAFE - No action needed"
-        elif risk == "Moderate":
-            alert_msg = "⚠️ SAFE - Monitor closely"
-        else:
-            alert_msg = "🔥 SHUTDOWN - Immediate attention needed"
+        alert_msg = {
+            "Low": "✅ SAFE - No action needed",
+            "Moderate": "⚠️ SAFE - Monitor closely",
+            "High": "🔥 SHUTDOWN - Immediate attention needed"
+        }.get(risk, "Unknown")
 
         summary_md = f"""
 ### 🔎 Summary
@@ -47,8 +49,6 @@ with gr.Blocks() as demo:
 """
 
         df = get_history_df()
-
-        # Convert Risk to numeric for plotting
         risk_map = {'Low': 1, 'Moderate': 2, 'High': 3}
         df["Risk_Num"] = df["Risk"].map(risk_map)
 
@@ -67,9 +67,22 @@ with gr.Blocks() as demo:
 
         return risk, alert_msg, timestamp, summary_md, df_display, fig
 
-    predict_btn.click(classify, inputs=[temp, duration],
-                      outputs=[result, alert, ist_time, summary, history_table, plot])
-
-    retrain_btn.click(retrain_model, outputs=[retrain_output])
+    # Function to reset all fields
+    def reset_all():
+        return (
+            100, 30, "", "", "", "", pd.DataFrame(columns=["Temperature", "Duration", "Risk", "Timestamp"]), plt.figure()
+        )
+
+    predict_btn.click(
+        classify,
+        inputs=[temp, duration],
+        outputs=[result, alert, ist_time, summary, history_table, plot]
+    )
+
+    reset_btn.click(
+        reset_all,
+        inputs=[],
+        outputs=[temp, duration, result, alert, ist_time, summary, history_table, plot]
+    )
 
 demo.launch()