Upload 2 files

app (2).py

@@ -0,0 +1,126 @@
+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
+
+# Loading Models
+braintumor_model = load_model('models/brain_tumor_binary.h5')
+
+# Configuring Streamlit
+st.set_page_config(
+    page_title="Brain Tumor Prediction App",
+    page_icon=":brain:",
+    layout="centered",
+)
+
+# Streamlit app title and description
+st.title("Brain Tumor Prediction App")
+st.write(
+    "Upload an image, and the app will predict whether a brain tumor is present or not."
+)
+
+# Add Streamlit sidebar for additional information or options
+st.sidebar.title("About")
+st.sidebar.info(
+    "This app uses a trained model to predict brain tumors from images. "
+    "The model is based on VGG16 architecture."
+)
+
+# Function to preprocess the image
+def preprocess_image(img):
+    # 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()
+
+        # Convert to NumPy array
+        nparr = np.frombuffer(img_bytes, np.uint8)
+
+        # Decode image
+        img_gray = cv2.imdecode(nparr, cv2.IMREAD_GRAYSCALE)
+
+    # Crop and preprocess the grayscale image
+    img_processed = preprocess_imgs([img_gray], (224, 224))
+
+    return img_processed
+
+# Handle binary decision
+def binary_decision(confidence):
+    return 1 if confidence >= 0.5 else 0
+
+def predict_braintumor(img):
+    # Preprocess the image
+    img_processed = preprocess_image(img)
+
+    # Make prediction
+    pred = braintumor_model.predict(img_processed)
+
+    # Handle binary decision
+    confidence = pred[0][0]
+    return "Brain Tumor Not Found!" if binary_decision(confidence) == 1 else "Brain Tumor Found!"
+
+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)
+
+# Gradio interface
+iface = gr.Interface(
+    fn=predict_braintumor,
+    inputs="image",
+    outputs="text",
+    examples=[["examples/1_no.jpeg"], ["examples/2_no.jpeg"], ["examples/3_no.jpg"], ["examples/Y1.jpg"], ["examples/Y2.jpg"], ["examples/Y3.jpg"]],
+    live=True  # Allows real-time updates without restarting the app
+)
+
+# Display Gradio interface
+iface.launch()
+
+# Streamlit components below the Gradio interface
+uploaded_file = st.file_uploader("Choose an MRI image", type=["jpg", "jpeg"])
+
+if uploaded_file is not None:
+    # Display the uploaded image
+    st.image(uploaded_file, caption="Uploaded MRI Image.", use_column_width=True)
+    
+    # Perform prediction when the "Predict" button is clicked
+    if st.button("Predict"):
+        # Preprocess the image
+        img_array = preprocess_image(uploaded_file)
+
+        # Make prediction
+        pred = braintumor_model.predict(img_array)
+
+        # Handle binary decision
+        confidence = pred[0][0]
+        result = "Brain Tumor Not Found!" if binary_decision(confidence) == 1 else "Brain Tumor Found!"
+
+        # Display the prediction result
+        st.write(result)

requirements (1).txt

@@ -0,0 +1,6 @@
+streamlit
+opencv-python
+numpy
+tensorflow
+pillow
+gradio