Delete app.py

app.py

@@ -1,108 +0,0 @@
-import gradio as gr
-import numpy as np
-import tensorflow as tf
-from tensorflow.keras.models import Model, load_model
-from tensorflow.keras.layers import GlobalAveragePooling2D, Dense, Dropout
-from tensorflow.keras.preprocessing.image import ImageDataGenerator
-import os
-
-# Settings
-MODEL_FILE = "brain_tumor_mobilenet.h5"
-IMG_SIZE = (224, 224)
-BATCH_SIZE = 16
-EPOCHS = 5  # change/fine-tune more if you have real data
-
-# Utility: prepare data
-def get_dataset(data_dir="data"):
-    """
-    Expect data_dir with two subfolders: `tumor` and `no_tumor`, containing images.
-    """
-    datagen = ImageDataGenerator(
-        rescale=1./255,
-        validation_split=0.2
-    )
-    train_gen = datagen.flow_from_directory(
-        data_dir,
-        target_size=IMG_SIZE,
-        batch_size=BATCH_SIZE,
-        class_mode="binary",
-        subset="training"
-    )
-    val_gen = datagen.flow_from_directory(
-        data_dir,
-        target_size=IMG_SIZE,
-        batch_size=BATCH_SIZE,
-        class_mode="binary",
-        subset="validation"
-    )
-    return train_gen, val_gen
-
-# 1. Build or Load model
-if not os.path.exists(MODEL_FILE):
-    # if you have dataset
-    has_data = os.path.isdir("data")
-    base_model = tf.keras.applications.MobileNetV2(
-        weights="imagenet",
-        include_top=False,
-        input_shape=(IMG_SIZE[0], IMG_SIZE[1], 3)
-    )
-    x = base_model.output
-    x = GlobalAveragePooling2D()(x)
-    x = Dropout(0.5)(x)
-    output = Dense(1, activation="sigmoid")(x)
-    model = Model(inputs=base_model.input, outputs=output)
-
-    # Freeze base layers first
-    for layer in base_model.layers:
-        layer.trainable = False
-
-    model.compile(optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"])
-
-    if has_data:
-        train_gen, val_gen = get_dataset("data")
-        model.fit(train_gen, validation_data=val_gen, epochs=EPOCHS)
-        # Optionally unfreeze some layers and fine‐tune
-    else:
-        # Dummy training if no dataset, just random noise (NOT for real use)
-        dummy_x = np.random.rand(20, IMG_SIZE[0], IMG_SIZE[1], 3)
-        dummy_y = np.random.randint(0, 2, size=(20,))
-        model.fit(dummy_x, dummy_y, epochs=2, batch_size=4)
-
-    model.save(MODEL_FILE)
-else:
-    model = load_model(MODEL_FILE)
-
-# 2. Prediction function
-def predict_brain_tumor(image):
-    """
-    Input: PIL image via Gradio upload
-    Output: prediction string + probability
-    """
-    if image is None:
-        return "No image provided"
-    img = image.resize(IMG_SIZE)
-    img_arr = np.array(img) / 255.0
-    if img_arr.shape[-1] == 1:
-        img_arr = np.stack([img_arr]*3, axis=-1)  # grayscale → 3 channels
-    elif img_arr.shape[-1] == 4:
-        # drop alpha
-        img_arr = img_arr[..., :3]
-    img_batch = np.expand_dims(img_arr, axis=0)
-    prob = model.predict(img_batch)[0][0]
-    if prob > 0.5:
-        return f"🧠 Tumor Detected (confidence {prob:.2f})"
-    else:
-        return f"✅ No Tumor Detected (confidence {1-prob:.2f})"
-
-# 3. Gradio UI
-with gr.Blocks() as demo:
-    gr.Markdown("# Brain Tumor Detection via Transfer Learning (MobileNetV2)")
-    gr.Markdown("Upload an MRI brain scan to detect presence of tumor vs no tumor.")
-
-    image_input = gr.Image(type="pil", label="Upload MRI Image")
-    output_text = gr.Textbox(label="Prediction")
-
-    predict_btn = gr.Button("Predict")
-    predict_btn.click(fn=predict_brain_tumor, inputs=image_input, outputs=output_text)
-
-demo.launch()