Upload folder using huggingface_hub

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+my_model.keras filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,12 +1,6 @@
 ---
-title: Mri Tumor Classifier Space
-emoji: 💻
-colorFrom: red
-colorTo: purple
-sdk: gradio
-sdk_version: 6.0.1
+title: mri_tumor_classifier_space
 app_file: app.py
-pinned: false
+sdk: gradio
+sdk_version: 5.50.0
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,50 @@
+
+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+import os
+
+# Load the trained model
+model = tf.keras.models.load_model('my_model.keras')
+
+# These should be defined if not coming from `train.class_indices`
+# Make sure idx_to_class is available or manually define it if `train` is not accessible
+idx_to_class = {0: 'glioma', 1: 'meningioma', 2: 'notumor', 3: 'pituitary'} # Replace with your actual mapping
+class_labels = list(idx_to_class.values())
+
+# Define the image size used for training
+img_size = 224
+
+def predict_image(image):
+    img = Image.fromarray(image)
+    img = img.resize((img_size, img_size))
+    img_array = np.array(img)
+    img_array = np.expand_dims(img_array, axis=0)
+    img_array = tf.keras.applications.efficientnet.preprocess_input(img_array)
+
+    predictions = model.predict(img_array)[0]
+    predicted_class_idx = np.argmax(predictions)
+    predicted_class_label = class_labels[predicted_class_idx]
+    confidence = predictions[predicted_class_idx] * 100
+
+    return predicted_class_label, f"{confidence:.2f}%"
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=predict_image,
+    inputs=gr.Image(type="numpy", label="Upload MRI Scan"),
+    outputs=[
+        gr.Textbox(label="Predicted Class"),
+        gr.Textbox(label="Confidence")
+    ],
+    title="Brain Tumor MRI Classification",
+    description="Upload an MRI scan to get a prediction for brain tumor type and confidence.",
+    examples=[
+        os.path.join("/kaggle/input/brain-tumor-mri-dataset/Testing/notumor/Te-no_0015.jpg"),
+        os.path.join("/kaggle/input/brain-tumor-mri-dataset/Testing/glioma/Te-gl_0010.jpg")
+    ]
+)
+
+# This launch is for local testing, not for deployment via `gradio deploy`
+# iface.launch(debug=True, allowed_paths=[test_dir])

my_model.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:02ca582a67a6ced9e99cf91846b948945506bf9eb81c0b5e845c41834a3abd6c
+size 39315228