Upload 3 files

app.py

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+import streamlit as st
+from PIL import Image
+import numpy as np
+import tensorflow as tf
+
+
+
+def load_model():
+    model = tf.keras.models.load_model('cnn_tumor2.h5')
+    return model
+
+model = load_model()
+
+
+def preprocess_image(image):
+    image = image.resize((128,128)) 
+    image = np.array(image) / 255.0  
+    image = np.expand_dims(image, axis=0)  
+    return image
+
+
+def predict_tumor(image):
+    predictions = model.predict(image)
+    if predictions[0] > 0.5:  
+        return "Tumorous"
+    else:
+        return "Non-tumorous"
+
+with open("styles.css") as f:
+        st.markdown(f"<style>{f.read()}</style>", unsafe_allow_html=True)
+st.title("Tumor Detection using CNN")
+
+
+uploaded_file = st.file_uploader("Upload an image...", type=["jpg", "jpeg", "png"])
+
+
+if uploaded_file is not None:
+    image = Image.open(uploaded_file)
+    resized_image = image.resize((300, 200))
+    st.image(resized_image, caption="Uploaded Image")
+    
+    processed_image = preprocess_image(image)
+    result = predict_tumor(processed_image)
+    st.info(f"Prediction: {result}")

cnn_tumor2.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:79887365bf9fbb0506200b86a18ae7d473132944f31662c61e192bea3da19220
+size 391807368

styles.css

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+.stApp {
+    background-image: url('https://i.postimg.cc/9FY4zK10/Fatehput-Sikiri-Buland-Darwaza-gate-2010.jpg'); 
+    background-size: cover;
+    background-position: center;
+    background-repeat: no-repeat;
+    .glass {
+        background: rgba(255, 255, 255, 0.1); /* Translucent background */
+        backdrop-filter: blur(10px); /* Blurring effect */
+        border-radius: 10px; /* Rounded corners */
+        padding: 10px;
+        color: #ffffff; /* Text color */
+        font-weight: bold; /* Bold text */
+    }
+   
+
+    
+    
+}