Update app.py

app.py

@@ -1,7 +1,27 @@
 import gradio as gr
 from PIL import Image
+import random
+from datasets import load_dataset
+
 from app.model import predict, gradcam, CLASS_NAMES
 
+# Load HF dataset once at startup
+dataset = load_dataset("AIOmarRehan/Brain_Tumor_MRI_Dataset", split="train")
+
+# Convert any image to a usable PIL
+def to_pil(example):
+    if isinstance(example, Image.Image):
+        return example
+    return Image.fromarray(example)
+
+def get_random_image():
+    """Return a random image from the HF dataset."""
+    sample = random.choice(dataset)
+    img = sample["image"]  # dataset must have column "image"
+    return to_pil(img)
+
+
+# Prediction and Grad-CAM logic
 def predict_fn(img):
     label, confidence, probs = predict(img)
     probs_sorted = {k: float(v) for k, v in sorted(probs.items(), key=lambda x: x[1], reverse=True)}
@@ -15,13 +35,18 @@ def gradcam_fn(img, interpolant):
     heatmap = gradcam(img, interpolant=float(interpolant))
     return Image.fromarray(heatmap)
 
+
+# GRADIO UI
 with gr.Blocks(title="Brain Tumor MRI Classifier (InceptionV3 + Grad-CAM)") as demo:
     gr.Markdown("# Brain Tumor MRI Classifier (InceptionV3 + Grad-CAM)")
-    gr.Markdown("Upload an MRI image to classify and visualize Grad-CAM explanation.")
+    gr.Markdown("Upload an MRI image OR use a random sample from the dataset.")
 
     with gr.Row():
         with gr.Column():
             input_img = gr.Image(type="pil", label="Upload MRI Image")
+
+            random_btn = gr.Button("Use Random Dataset Image")
+
             interpolant_slider = gr.Slider(0, 1, value=0.5, label="Grad-CAM Intensity (interpolant)")
             submit_btn = gr.Button("Run Prediction + Grad-CAM")
 
@@ -29,6 +54,14 @@ with gr.Blocks(title="Brain Tumor MRI Classifier (InceptionV3 + Grad-CAM)") as d
             output_json = gr.JSON(label="Prediction Results")
             output_cam = gr.Image(label="Grad-CAM Overlay")
 
+    # Button: Load random dataset image
+    random_btn.click(
+        fn=lambda: get_random_image(),
+        inputs=[],
+        outputs=[input_img]
+    )
+
+    # Button: Run prediction
     submit_btn.click(
         fn=lambda img, interp: (predict_fn(img), gradcam_fn(img, interp)),
         inputs=[input_img, interpolant_slider],