Update app.py

app.py

@@ -1,48 +1,28 @@
 import gradio as gr
 import tensorflow as tf
-import cv2
+from tensorflow.keras.models import load_model
 import numpy as np
+from tensorflow.keras.preprocessing import image
 
-def preprocess_image(img):
-    # Resize the image to the target size (256x256)
-    img = cv2.resize(img, (256, 256))
-    
-    # Center crop to 224x224
-    h, w, _ = img.shape
-    crop_start_x = (w - 224) // 2
-    crop_start_y = (h - 224) // 2
-    img = img[crop_start_y:crop_start_y + 224, crop_start_x:crop_start_x + 224]
 
-    # Normalize the image
-    img = img / 255.0
-    
-    # Convert BGR to RGB
-    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-    
-    # Expand dimensions to match model input shape
-    img = np.expand_dims(img, axis=0)
-    
-    return img
 
 def predict_input_image(img):
-    # Preprocess the input image
-    img = preprocess_image(img)
     img = tf.image.resize(img, [224,224])
     img = np.expand_dims(img, axis = 0)
-
-    # Load the pre-trained model
-    model = tf.keras.models.load_model('Tumor_Model.h5')
-
+    
     # Make predictions
+    model = tf.keras.models.load_model('Tumor_Model.h5')
     prediction = model.predict(img)
-    result = 'No Tumor Detected' if prediction[0][0] > 0.5 else 'Tumor detected'
+    result = 'No Tumor Detected' if prediction[0][0] > 0.4 else 'Tumor detected'
 
     return f"Prediction: {result}"
 
+    
+
 # Define Gradio interface
 iface = gr.Interface(
     fn=predict_input_image,
-    inputs=gr.Image(type="numpy"),
+    inputs= 'image',
     outputs="text",
 )