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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('model_butter.h5')  # Path to your model
+    return model
+
+model = load_model()
+
+# Class labels
+class_labels = [
+    'SOUTHERN DOGFACE', 'ADONIS', 'BROWN SIPROETA', 'MONARCH', 'GREEN CELLED CATTLEHEART', 
+    'CAIRNS BIRDWING', 'EASTERN DAPPLE WHITE', 'RED POSTMAN', 'MANGROVE SKIPPER', 
+    'BLACK HAIRSTREAK', 'CABBAGE WHITE', 'RED ADMIRAL', 'PAINTED LADY', 'PAPER KITE', 
+    'SOOTYWING', 'PINE WHITE', 'PEACOCK', 'CHECQUERED SKIPPER', 'JULIA', 
+    'COMMON WOOD-NYMPH', 'BLUE MORPHO', 'CLOUDED SULPHUR', 'STRAITED QUEEN', 
+    'ORANGE OAKLEAF', 'PURPLISH COPPER', 'ATALA', 'IPHICLUS SISTER', 'DANAID EGGFLY', 
+    'LARGE MARBLE', 'PIPEVINE SWALLOW', 'BLUE SPOTTED CROW', 'RED CRACKER', 
+    'QUESTION MARK', 'CRIMSON PATCH', 'BANDED PEACOCK', 'SCARCE SWALLOW', 'COPPER TAIL', 
+    'GREAT JAY', 'INDRA SWALLOW', 'VICEROY', 'MALACHITE', 'APPOLLO', 
+    'TWO BARRED FLASHER', 'MOURNING CLOAK', 'TROPICAL LEAFWING', 'POPINJAY', 
+    'ORANGE TIP', 'GOLD BANDED', 'BECKERS WHITE', 'RED SPOTTED PURPLE', 
+    'MILBERTS TORTOISESHELL', 'SILVER SPOT SKIPPER', 'AMERICAN SNOOT', 'AN 88', 
+    'ULYSES', 'COMMON BANDED AWL', 'CRECENT', 'METALMARK', 'SLEEPY ORANGE', 
+    'PURPLE HAIRSTREAK', 'ELBOWED PIERROT', 'GREAT EGGFLY', 'ORCHARD SWALLOW', 
+    'ZEBRA LONG WING', 'WOOD SATYR', 'MESTRA', 'EASTERN PINE ELFIN', 
+    'EASTERN COMA', 'YELLOW SWALLOW TAIL', 'CLEOPATRA', 'GREY HAIRSTREAK', 
+    'BANDED ORANGE HELICONIAN', 'AFRICAN GIANT SWALLOWTAIL', 'CHESTNUT', 
+    'CLODIUS PARNASSIAN'
+]
+
+# Title
+st.title("🦋 Butterfly Classifier")
+
+# Image Upload
+st.header("Upload an Image")
+uploaded_file = st.file_uploader("Please upload a butterfly 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))  # Resize according to how your model was trained
+    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)
+    predicted_class = np.argmax(prediction, axis=1)[0]
+
+    # Display the result
+    st.write(f"🦋 **It's a {class_labels[predicted_class]}!**")
+else:
+    st.write("👈 Upload an image to get started!")

model_butter.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0d6e9c5d2331719a9a2c6d89b1657de8be1101c9bb50c49a3d7575f05989e4a3
+size 45063260

requirements.txt

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+tensorflow
+streamlit