Update app.py

app.py

@@ -1,8 +1,8 @@
 import gradio as gr
-import matplotlib.pyplot as plt
 import numpy as np
 import json
 import warnings
+import plotly.graph_objects as go
 
 # Silence irrelevant HF warnings
 warnings.filterwarnings(
@@ -12,38 +12,50 @@ warnings.filterwarnings(
 )
 
 # ----------------------------
-# Core callback (SAFE VERSION)
+# Core callback (SAFE + HF READY)
 # ----------------------------
 def run_curvopt(model_name, hardware, acc_budget):
     try:
         # ----------------------------
-        # ENERGY PLOT (dummy but valid)
+        # ENERGY PLOT
         # ----------------------------
-        fig_energy = plt.figure()
         x = np.arange(1, 6)
         y = np.random.uniform(10, 50, size=5)
-        plt.plot(x, y, marker="o")
-        plt.xlabel("Layer Index")
-        plt.ylabel("Energy (mJ)")
-        plt.title("Layerwise Energy Consumption")
-        plt.tight_layout()
-        plt.close(fig_energy)
+
+        fig_energy = go.Figure(
+            data=go.Scatter(
+                x=x,
+                y=y,
+                mode="lines+markers"
+            )
+        )
+        fig_energy.update_layout(
+            title="Layerwise Energy Consumption",
+            xaxis_title="Layer Index",
+            yaxis_title="Energy (mJ)"
+        )
 
         # ----------------------------
-        # PARETO PLOT (dummy but valid)
+        # PARETO PLOT
         # ----------------------------
-        fig_pareto = plt.figure()
         acc = np.array([0.82, 0.85, 0.88, 0.90])
         energy = np.array([55, 48, 40, 34])
-        plt.plot(acc, energy, marker="o")
-        plt.xlabel("Accuracy")
-        plt.ylabel("Energy (mJ)")
-        plt.title("Energy–Accuracy Pareto Frontier")
-        plt.tight_layout()
-        plt.close(fig_pareto)
+
+        fig_pareto = go.Figure(
+            data=go.Scatter(
+                x=acc,
+                y=energy,
+                mode="lines+markers"
+            )
+        )
+        fig_pareto.update_layout(
+            title="Energy–Accuracy Pareto Frontier",
+            xaxis_title="Accuracy",
+            yaxis_title="Energy (mJ)"
+        )
 
         # ----------------------------
-        # POLICY JSON (always serializable)
+        # POLICY JSON
         # ----------------------------
         policy = {
             "model": model_name,
@@ -64,7 +76,7 @@ def run_curvopt(model_name, hardware, acc_budget):
         import traceback
         print(traceback.format_exc())
 
-        # IMPORTANT: must return SAME NUMBER of outputs
+        # Must return SAME NUMBER of outputs
         return None, None, f"ERROR:\n{str(e)}"
 
 
@@ -76,6 +88,8 @@ with gr.Blocks() as demo:
         """
         # ⚡ CurvOpt  
         **Energy-Efficient Inference via Curvature & Information**
+
+        A systems-oriented ML demo focusing on **lower energy and compute footprint**.
         """
     )
 
@@ -89,7 +103,7 @@ with gr.Blocks() as demo:
         hardware_radio = gr.Radio(
             choices=["CPU", "GPU", "EDGE"],
             value="CPU",
-            label="Hardware"
+            label="Target Hardware"
         )
 
         acc_slider = gr.Slider(
@@ -118,7 +132,7 @@ with gr.Blocks() as demo:
     )
 
 # ----------------------------
-# Launch (HF-safe)
+# Launch (HF-SAFE)
 # ----------------------------
 demo.launch(
     theme=gr.themes.Soft(),