Upload 3 files

app.py

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+import streamlit as st
+from tensorflow.keras.preprocessing import image
+import numpy as np
+from PIL import Image
+import tensorflow as tf
+
+# Load the model
+@st.cache_resource
+def load_model():
+    model = tf.keras.models.load_model('catdogmodel.h5')  # Path to your model
+    return model
+
+model = load_model()
+
+# Title
+st.title("🐱🐶 Cat vs. Dog Classifier")
+
+# Image Upload
+st.header("Upload an Image")
+uploaded_file = st.file_uploader("Please upload a cat or dog image...", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    # Open and display the image
+    img = Image.open(uploaded_file)
+    st.image(img, caption='Uploaded Image', use_column_width=True)
+    st.write("🔍 **Analyzing the image...**")
+
+    # Preprocess the image
+    img = img.resize((128, 128))
+    img_array = image.img_to_array(img)
+    img_array = np.expand_dims(img_array, axis=0)
+    img_array /= 255.0
+
+    # Predict
+    prediction = model.predict(img_array)
+
+    # Display the result
+    if prediction < 0.5:
+        st.write("🐶 **It's a Dog!**")
+    else:
+        st.write("🐱 **It's a Cat!**")
+else:
+    st.write("👈 Upload an image to get started!")

catdogmodel.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e18cf37afdcd0bd4fe20ae2f5f861e9cf1dd7152f97256f68b0e121a890b904f
+size 155322664

requirements.txt

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+tensorflow
+streamlit