Update app.py

app.py

@@ -1,20 +1,16 @@
-from huggingface_hub import hf_hub_download
-from tensorflow.keras.models import load_model
-from tensorflow.keras.utils import img_to_array
 import gradio as gr
 import numpy as np
 from PIL import Image
+from tensorflow.keras.models import load_model
+from tensorflow.keras.utils import img_to_array
 
-model_path = hf_hub_download(
-    repo_id="keshavsingh2003/Brain-Tumor-Model-Keras",
-    filename="clean_model.keras",
-    repo_type="model"
-)
-model = load_model(model_path)
-
+# ✅ Direct load since model file is in same directory
+model = load_model("clean_model.keras")
 
+# ✅ Class labels as per your training
 class_labels = ['pituitary', 'glioma', 'notumor', 'meningioma']
 
+# ✅ Prediction function
 def predict_image(img):
     img = img.convert("RGB")
     img = img.resize((128, 128))
@@ -23,17 +19,23 @@ def predict_image(img):
 
     predictions = model.predict(img_array)
     idx = np.argmax(predictions)
-    conf = np.max(predictions)
+    confidence = np.max(predictions)
 
     label = class_labels[idx]
-    return (f"🟢 No Tumor Detected" if label=='notumor'
-            else f"🔴 Tumor: {label.capitalize()}") \
-           + f"\nConfidence: {conf*100:.2f}%"
+    if label == 'notumor':
+        result = "🟢 No Tumor Detected"
+    else:
+        result = f"🔴 Tumor Type: {label.capitalize()}"
 
+    return f"{result}\nConfidence: {confidence * 100:.2f}%"
+
+# ✅ Gradio Interface
 interface = gr.Interface(
     fn=predict_image,
-    inputs=gr.Image(type="pil", label="Upload MRI"),
-    outputs="textbox",
-    title="Brain Tumor Detection"
+    inputs=gr.Image(type="pil", label="Upload MRI Image"),
+    outputs=gr.Textbox(label="Prediction Result"),
+    title="🧠 Brain Tumor Detection",
+    description="Upload an MRI scan to detect brain tumor type using a trained CNN model."
 )
+
 interface.launch()