Create app.py

app.py

@@ -0,0 +1,40 @@
+import gradio as gr
+from fastapi import FastAPI
+from risk_model import predict_risk, retrain_model, get_history_df
+
+app = FastAPI()
+gradio_app = gr.Blocks()
+
+with gradio_app:
+    gr.Markdown("## 🔥 Heating Mantle Safety Risk Predictor")
+
+    with gr.Row():
+        temp = gr.Number(label="Max Temperature (°C)", value=100)
+        duration = gr.Number(label="Duration (min)", value=30)
+
+    with gr.Row():
+        predict_btn = gr.Button("🔍 Predict")
+        retrain_btn = gr.Button("🔁 Retrain Model")
+
+    result = gr.Textbox(label="Risk Prediction")
+    score = gr.Textbox(label="Confidence (%)")
+    retrain_output = gr.Textbox(label="Retrain Status")
+
+    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()
+        risk, confidence = predict_risk(temp, duration)
+        emoji = "🟢" if risk == "Low" else "🟠" if risk == "Moderate" else "🔴"
+        return f"{emoji} {risk}", f"{confidence}%", get_history_df()
+
+    predict_btn.click(classify, inputs=[temp, duration], outputs=[result, score, history_table])
+    retrain_btn.click(retrain_model, outputs=[retrain_output])
+
+# Mount Gradio onto FastAPI
+@app.get("/")
+def read_root():
+    return {"message": "Heating Mantle Risk API is running!"}
+
+app = gr.mount_gradio_app(app, gradio_app, path="/predict-ui")