Update app.py

app.py

@@ -1,5 +1,7 @@
 import gradio as gr
-from risk_model import predict_risk, retrain_model, get_history_df
+from risk_model import predict_risk, retrain_model, get_history_df, get_ist_time
+import pandas as pd
+import matplotlib.pyplot as plt
 
 with gr.Blocks() as demo:
     gr.Markdown("## 🔥 Heating Mantle Safety Risk Predictor")
@@ -14,18 +16,52 @@ with gr.Blocks() as demo:
 
     result = gr.Textbox(label="Risk Prediction")
     score = gr.Textbox(label="Confidence (%)")
+    alert = gr.Textbox(label="🚨 Alert Message")
+    ist_time = gr.Textbox(label="Timestamp (IST)")
     retrain_output = gr.Textbox(label="Retrain Status")
 
-    history_table = gr.Dataframe(headers=["Temperature", "Duration", "Risk", "Confidence"], label="📈 Prediction History")
+    summary = gr.Markdown()
+    history_table = gr.Dataframe(headers=["Temperature", "Duration", "Risk", "Confidence", "Timestamp"], label="📈 Prediction History")
+
+    plot = gr.Plot(label="📊 Risk Distribution Chart")
 
     def classify(temp, duration):
         if temp <= 0 or duration <= 0:
-            return "❌ Invalid Input", "Use values > 0", get_history_df()
-        risk, confidence = predict_risk(temp, duration)
-        emoji = "🟢" if risk == "Low" else "🟠" if risk == "Moderate" else "🔴"
-        return f"{emoji} {risk}", f"{confidence}%", get_history_df()
+            return "❌ Invalid Input", "Use values > 0", "N/A", "N/A", "", pd.DataFrame(), plt.figure()
+
+        risk, confidence, 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"
+
+        summary_md = f"""
+### 🔎 Summary
+- **Max Temp**: {temp} °C  
+- **Duration**: {duration} min  
+- **Risk**: {risk}  
+- **Confidence**: {confidence:.2f}%  
+- **Timestamp**: {timestamp}  
+- **Alert**: {alert_msg}
+"""
+
+        df = get_history_df()
+
+        # Plot chart
+        fig, ax = plt.subplots()
+        risk_counts = df["Risk"].value_counts()
+        risk_counts.plot(kind="bar", ax=ax, color=["green", "orange", "red"])
+        ax.set_title("Risk Level Distribution")
+        ax.set_ylabel("Count")
+
+        return f"{risk}", f"{confidence:.2f}", alert_msg, timestamp, summary_md, df, fig
+
+    predict_btn.click(classify, inputs=[temp, duration],
+                      outputs=[result, score, alert, ist_time, summary, history_table, plot])
 
-    predict_btn.click(classify, inputs=[temp, duration], outputs=[result, score, history_table])
     retrain_btn.click(retrain_model, outputs=[retrain_output])
 
 demo.launch()