moved gradcam inside app

app.py

@@ -5,36 +5,36 @@ from app.gradcam import generate_gradcam
 
 
 def predict(image):
-    # 1️⃣ Disease prediction
+
+    # 1️⃣ Prediction
     label, confidence, tensor, original = predict_disease(image)
 
-    # 2️⃣ GradCAM heatmap
+    # 2️⃣ GradCAM
     heatmap = generate_gradcam(tensor, original)
 
-    # Safety check (VERY IMPORTANT for Gradio image output)
-    heatmap = np.array(heatmap)
-
-    # Normalize if needed
-    if heatmap.max() <= 1:
-        heatmap = heatmap * 255
+    # Ensure proper format (IMPORTANT FIX)
+    if heatmap.dtype != np.uint8:
+        heatmap = np.clip(heatmap, 0, 1)
+        heatmap = (heatmap * 255).astype(np.uint8)
 
-    heatmap = heatmap.astype(np.uint8)
+    # If grayscale → convert to RGB (prevents Gradio schema crash sometimes)
+    if len(heatmap.shape) == 2:
+        heatmap = np.stack([heatmap]*3, axis=-1)
 
-    # 3️⃣ Risk prediction
+    # 3️⃣ Risk
     risk = compute_ai_risk(label, confidence, heatmap)
 
-    report = f"""
-🫀 AI Clinical Report
-
-Disease: {label}
-Confidence: {round(confidence * 100, 2)} %
-Risk Level: {risk}
-"""
+    report = (
+        f"🫀 AI Clinical Report\n\n"
+        f"Disease: {label}\n"
+        f"Confidence: {confidence*100:.2f} %\n"
+        f"Risk Level: {risk}"
+    )
 
     return report, heatmap
 
 
-# 🔴 UI
+# UI
 with gr.Blocks(title="CardioGuard AI") as demo:
 
     gr.Markdown("# 🫀 CardioGuard AI")
@@ -54,10 +54,9 @@ with gr.Blocks(title="CardioGuard AI") as demo:
         outputs=[report_output, heatmap_output]
     )
 
-
-# ✅ IMPORTANT FIX FOR SPACES / CLOUD
+# IMPORTANT FIX FOR HF / CLOUD ENV
 demo.launch(
     server_name="0.0.0.0",
     server_port=7860,
-    share=True
+    share=False
 )