Create app.py

app.py

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+import streamlit as st
+import gradio as gr
+import cv2
+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
+from werkzeug.utils import secure_filename
+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 allowed_file(filename):
+    return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS
+
+def preprocess_imgs(set_name, img_size):
+    set_new = []
+    for img in set_name:
+        img = cv2.resize(img, dsize=img_size, interpolation=cv2.INTER_CUBIC)
+        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
+
+def predict_braintumor(img_path):
+    img_gray = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE)
+
+    # Crop and preprocess the grayscale image
+    img_processed = crop_imgs([img_gray])
+    img_processed = preprocess_imgs(img_processed, (224, 224))
+
+    # Make prediction
+    pred = braintumor_model.predict(img_processed)
+
+    # Handle binary decision
+    confidence = pred[0][0]
+
+    if confidence >= 0.5:
+        return "Brain Tumor Not Found!"
+    else:
+        return "Brain Tumor Found!"
+
+def main():
+    st.title("Brain Tumor Prediction App")
+
+    uploaded_file = st.file_uploader("Choose an image...", type=["png", "jpg", "jpeg"])
+
+    if uploaded_file is not None:
+        st.image(uploaded_file, caption="Uploaded Image.", use_column_width=True)
+        st.write("")
+        st.write("Classifying...")
+
+        # Read the contents of the uploaded file
+        file_contents = uploaded_file.read()
+
+        # Save the uploaded file
+        filename = secure_filename(uploaded_file.name)
+        file_path = os.path.join(UPLOAD_FOLDER, filename)
+
+        with open(file_path, "wb") as f:
+            f.write(file_contents)
+
+        # Make prediction
+        result = predict_braintumor(file_path)
+
+        # Display prediction
+        st.subheader("Prediction:")
+        st.success(result)
+
+if __name__ == "__main__":
+    main()
+    gr.Interface(fn=predict_braintumor, inputs="image", outputs="text").launch()