Update app.py

app.py

@@ -1,14 +1,7 @@
 import gradio as gr
-from fastapi import FastAPI
 from risk_model import predict_risk, retrain_model, get_history_df
 
-# Create FastAPI instance
-fastapi_app = FastAPI()
-
-# Gradio UI blocks
-gradio_app = gr.Blocks()
-
-with gradio_app:
+with gr.Blocks() as demo:
     gr.Markdown("## 🔥 Heating Mantle Safety Risk Predictor")
 
     with gr.Row():
@@ -23,14 +16,11 @@ with gradio_app:
     score = gr.Textbox(label="Confidence (%)")
     retrain_output = gr.Textbox(label="Retrain Status")
 
-    history_table = gr.Dataframe(
-        headers=["Temperature", "Duration", "Risk", "Confidence"],
-        label="📈 Prediction History"
-    )
+    history_table = gr.Dataframe(headers=["Temperature", "Duration", "Risk", "Confidence"], label="📈 Prediction History")
 
     def classify(temp, duration):
         if temp <= 0 or duration <= 0:
-            return "Invalid Input", "Use values > 0", get_history_df()
+            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()
@@ -38,10 +28,4 @@ with gradio_app:
     predict_btn.click(classify, inputs=[temp, duration], outputs=[result, score, history_table])
     retrain_btn.click(retrain_model, outputs=[retrain_output])
 
-# Optional test route
-@fastapi_app.get("/")
-def root():
-    return {"message": "Heating Mantle FastAPI is working"}
-
-# ✅ Hugging Face requires this:
-app = gr.mount_gradio_app(fastapi_app, gradio_app, path="/predict-ui")
+demo.launch()