Update app.py

app.py

@@ -1,5 +1,5 @@
 import gradio as gr
-from risk_model import predict_risk, retrain_model, get_history_df, get_ist_time
+from risk_model import predict_risk, retrain_model, get_history_df
 import pandas as pd
 import matplotlib.pyplot as plt
 
@@ -15,21 +15,20 @@ with gr.Blocks() as demo:
         retrain_btn = gr.Button("🔁 Retrain Model")
 
     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")
 
     summary = gr.Markdown()
-    history_table = gr.Dataframe(headers=["Temperature", "Duration", "Risk", "Confidence", "Timestamp"], label="📈 Prediction History")
+    history_table = gr.Dataframe(headers=["Temperature", "Duration", "Risk", "Timestamp"], label="📈 Prediction History")
 
-    plot = gr.Plot(label="📊 Risk Distribution Chart")
+    plot = gr.Plot(label="📊 Risk Trend Chart")
 
     def classify(temp, duration):
         if temp <= 0 or duration <= 0:
-            return "❌ Invalid Input", "Use values > 0", "N/A", "N/A", "", pd.DataFrame(), plt.figure()
+            return "❌ Invalid", "Invalid", "Invalid", "", pd.DataFrame(), plt.figure()
 
-        risk, confidence, timestamp = predict_risk(temp, duration)
+        risk, timestamp = predict_risk(temp, duration)
 
         if risk == "Low":
             alert_msg = "✅ SAFE - No action needed"
@@ -43,25 +42,16 @@ with gr.Blocks() as demo:
 - **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")
+        # Convert Risk to numeric for plotting
+        risk_map = {'Low': 1, 'Moderate': 2, 'High': 3}
+        df["Risk_Num"] = df["Risk"].map(risk_map)
 
-        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])
-
-    retrain_btn.click(retrain_model, outputs=[retrain_output])
-
-demo.launch()
+        fig, ax = plt.subplots(figsize=(6, 3))
+        ax.plot(df["Timestamp"], df["Risk_Num"], marker="o", linestyle="-", color="red")
+        a