Update app.py

app.py

@@ -5,14 +5,11 @@ import numpy as np
 from tensorflow.keras.models import load_model
 from tensorflow.keras.applications.vgg16 import preprocess_input
 from tensorflow.keras.preprocessing import image
-import os
 
 # Loading Models
 braintumor_model = load_model('models/brain_tumor_binary.h5')
 
 # Configuring Streamlit
-UPLOAD_FOLDER = 'static/uploads'
-ALLOWED_EXTENSIONS = {'png', 'jpg', 'jpeg'}
 st.set_page_config(page_title="Brain Tumor Prediction App", page_icon=":brain:")
 
 def preprocess_imgs(set_name, img_size):
@@ -22,59 +19,26 @@ def preprocess_imgs(set_name, img_size):
         set_new.append(preprocess_input(img))
     return np.array(set_new)
 
-def crop_imgs(set_name, add_pixels_value=0):
-    set_new = []
-    for img in set_name:
-        gray = cv2.GaussianBlur(img, (5, 5), 0)
-        thresh = cv2.threshold(gray, 45, 255, cv2.THRESH_BINARY)[1]
-        thresh = cv2.erode(thresh, None, iterations=2)
-        thresh = cv2.dilate(thresh, None, iterations=2)
-        cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-        cnts = cnts[0] if len(cnts) == 2 else cnts[1]
-        c = max(cnts, key=cv2.contourArea)
-        extLeft = tuple(c[c[:, :, 0].argmin()][0])
-        extRight = tuple(c[c[:, :, 0].argmax()][0])
-        extTop = tuple(c[c[:, :, 1].argmin()][0])
-        extBot = tuple(c[c[:, :, 1].argmax()][0])
-        ADD_PIXELS = add_pixels_value
-        new_img = img[extTop[1] - ADD_PIXELS:extBot[1] + ADD_PIXELS,
-                      extLeft[0] - ADD_PIXELS:extRight[0] + ADD_PIXELS].copy()
-        set_new.append(new_img)
-    return np.array(set_new)
-
-# Function to preprocess the image
-def preprocess_image(file_path):
-    img = image.load_img(file_path, target_size=(200, 200))
-    img_array = image.img_to_array(img)
-    img_array = np.expand_dims(img_array, axis=0)
-    img_array /= 255.0  # Normalize the image
-    return img_array
-
 # Handle binary decision
 def binary_decision(confidence):
     return 1 if confidence >= 0.5 else 0
 
 def predict_braintumor(img):
-    # Save the uploaded file if it's a file-like object
-    if hasattr(img, 'read'):
-        filename = "temp_image.png"
-        file_path = os.path.join(UPLOAD_FOLDER, filename)
-
+    # If it's a NumPy array, use it directly
+    if isinstance(img, np.ndarray):
+        img_gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
+    else:
         # Convert Gradio image data to bytes
         img_bytes = img.read()
 
-        with open(file_path, "wb") as f:
-            f.write(img_bytes)
-
-        img_gray = cv2.imread(file_path, cv2.IMREAD_GRAYSCALE)
+        # Convert to NumPy array
+        nparr = np.frombuffer(img_bytes, np.uint8)
 
-    # If it's a NumPy array, use it directly
-    elif isinstance(img, np.ndarray):
-        img_gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
+        # Decode image
+        img_gray = cv2.imdecode(nparr, cv2.IMREAD_GRAYSCALE)
 
     # Crop and preprocess the grayscale image
-    img_processed = crop_imgs([img_gray])
-    img_processed = preprocess_imgs(img_processed, (224, 224))
+    img_processed = preprocess_imgs([img_gray], (224, 224))
 
     # Make prediction
     pred = braintumor_model.predict(img_processed)
@@ -83,7 +47,6 @@ def predict_braintumor(img):
     confidence = pred[0][0]
     return "Brain Tumor Found!" if binary_decision(confidence) == 1 else "Brain Tumor Not Found!"
 
-
 def main():
     st.title("Brain Tumor Prediction App")