Update app.py

app.py

@@ -1,69 +1,68 @@
 import gradio as gr
-import tensorflow as tf
-from tensorflow.keras import layers, models
 import numpy as np
+import tensorflow as tf
+from tensorflow.keras.models import Sequential, load_model
+from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout
 import os
-import joblib
 
 MODEL_FILE = "brain_tumor_cnn.h5"
+IMG_SIZE = (128, 128)
 
-# ---------------------------
-# 1. Build or Load Model
-# ---------------------------
+# -------------------------
+# 1. Train Model (if not exists)
+# -------------------------
 if not os.path.exists(MODEL_FILE):
-    # Simple CNN for demo (not trained on real data here!)
-    model = models.Sequential([
-        layers.Input(shape=(128, 128, 3)),
-        layers.Conv2D(16, (3,3), activation='relu'),
-        layers.MaxPooling2D((2,2)),
-        layers.Conv2D(32, (3,3), activation='relu'),
-        layers.MaxPooling2D((2,2)),
-        layers.Flatten(),
-        layers.Dense(64, activation='relu'),
-        layers.Dense(1, activation='sigmoid')  # binary: tumor / no tumor
+    # ⚠️ Dummy training with random data (replace with actual dataset for real use)
+    X_train = np.random.rand(100, IMG_SIZE[0], IMG_SIZE[1], 3)
+    y_train = np.random.randint(0, 2, 100)
+
+    model = Sequential([
+        Conv2D(32, (3,3), activation="relu", input_shape=(IMG_SIZE[0], IMG_SIZE[1], 3)),
+        MaxPooling2D(2,2),
+        Conv2D(64, (3,3), activation="relu"),
+        MaxPooling2D(2,2),
+        Flatten(),
+        Dense(128, activation="relu"),
+        Dropout(0.5),
+        Dense(1, activation="sigmoid")
     ])
-    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
-    
-    # Fake training with random data (for demo purposes only!)
-    X_fake = np.random.rand(50, 128, 128, 3)
-    y_fake = np.random.randint(0, 2, 50)
-    model.fit(X_fake, y_fake, epochs=1, verbose=1)
-    
+
+    model.compile(optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"])
+    model.fit(X_train, y_train, epochs=2, batch_size=8, verbose=1)
+
     model.save(MODEL_FILE)
-else:
-    model = tf.keras.models.load_model(MODEL_FILE)
 
+# -------------------------
+# 2. Load model
+# -------------------------
+model = load_model(MODEL_FILE)
 
-# ---------------------------
-# 2. Prediction Function
-# ---------------------------
-def predict_tumor(image):
-    # Resize & normalize
-    img = image.resize((128, 128))
-    img_array = np.array(img) / 255.0
-    img_array = np.expand_dims(img_array, axis=0)
+# -------------------------
+# 3. Prediction Function
+# -------------------------
+def predict_brain_tumor(image):
+    image = image.resize(IMG_SIZE)
+    img_array = np.array(image) / 255.0
+    img_array = np.expand_dims(img_array, axis=0)  # (1, 128, 128, 3)
 
-    pred = model.predict(img_array)[0][0]
+    prediction = model.predict(img_array)[0][0]
 
-    if pred > 0.5:
-        return "🧠 Tumor Detected (Probability: {:.2f})".format(pred)
+    if prediction > 0.5:
+        return "🧠 Tumor Detected"
     else:
-        return "✅ No Tumor Detected (Probability: {:.2f})".format(1 - pred)
-
+        return "✅ No Tumor Detected"
 
-# ---------------------------
-# 3. Gradio UI
-# ---------------------------
+# -------------------------
+# 4. Gradio UI
+# -------------------------
 with gr.Blocks() as demo:
-    gr.Markdown("# 🧠 Brain Tumor Detection from MRI Image")
-    gr.Markdown("Upload an MRI scan to predict whether a brain tumor is present (demo with fake training).")
+    gr.Markdown("# 🧠 Brain Tumor Detection (CNN on MRI Images)")
+    gr.Markdown("Upload an MRI image to check if a brain tumor is detected.")
 
-    with gr.Row():
-        input_img = gr.Image(type="pil", label="Upload MRI Image")
-    
+    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_tumor, inputs=input_img, outputs=output_text)
+    predict_btn.click(fn=predict_brain_tumor, inputs=image_input, outputs=output_text)
 
 demo.launch()