Upload 3 files

app.py.py

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+# -*- coding: utf-8 -*-
+"""app.py
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/10HN0j7fMWCCHCZRup57O2eH-Acl7X0_5
+"""
+
+from google.colab import files
+uploaded = files.upload()  # Select your zip file
+
+import zipfile
+import os
+
+zip_path = "/content/archive (2).zip"  # Update if named differently
+with zipfile.ZipFile(zip_path, 'r') as zip_ref:
+    zip_ref.extractall("brain_tumor_dataset")
+
+os.listdir("brain_tumor_dataset")
+
+import os
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout
+from tensorflow.keras.optimizers import Adam
+
+# Set paths
+train_path = "brain_tumor_dataset"  # Unzipped folder
+
+# Preprocess data
+datagen = ImageDataGenerator(rescale=1./255, validation_split=0.2)
+
+train_gen = datagen.flow_from_directory(
+    train_path, target_size=(150, 150), batch_size=32, class_mode='binary', subset='training')
+
+val_gen = datagen.flow_from_directory(
+    train_path, target_size=(150, 150), batch_size=32, class_mode='binary', subset='validation')
+
+# Build CNN
+model = Sequential([
+    Conv2D(32, (3, 3), activation='relu', input_shape=(150, 150, 3)),
+    MaxPooling2D(2, 2),
+    Conv2D(64, (3, 3), activation='relu'),
+    MaxPooling2D(2, 2),
+    Flatten(),
+    Dense(128, activation='relu'),
+    Dropout(0.3),
+    Dense(1, activation='sigmoid')
+])
+
+model.compile(optimizer=Adam(), loss='binary_crossentropy', metrics=['accuracy'])
+model.fit(train_gen, validation_data=val_gen, epochs=10)
+
+# Save model
+model.save("brain_tumor_model.h5")
+
+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+
+model = tf.keras.models.load_model("brain_tumor_model.h5")
+
+def predict_tumor(img):
+    img = img.resize((150, 150))
+    img_array = np.expand_dims(np.array(img)/255.0, axis=0)
+    pred = model.predict(img_array)[0][0]
+    return "Tumor" if pred > 0.5 else "No Tumor"
+
+gr.Interface(fn=predict_tumor, inputs=gr.Image(type="pil"), outputs="text", title="Brain Tumor Classifier").launch()
+

brain_tumor_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:fc017582097ebf2b0ac5834431e679691d6242822939908cb20f4d48d9f37fb8
+size 127678168

requirements.txt

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+tensorflow
+gradio
+numpy
+Pillow