Update app.py

app.py

@@ -1,101 +1,65 @@
-import streamlit as st
-import os
-from PIL import Image
-import numpy as np
-from tensorflow.keras.preprocessing.image import ImageDataGenerator, img_to_array
-from tensorflow.keras.models import Sequential
-from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout
-from tensorflow.keras.utils import to_categorical
-from tensorflow.keras.optimizers import Adam
-from sklearn.preprocessing import LabelEncoder
-from sklearn.model_selection import train_test_split
-
-# Page config
-st.set_page_config(page_title="Animal Classifier DL", layout="centered")
-
-st.title("🧠 Animal Classifier (Deep Learning)")
-st.markdown("Train from folders like `Animals/cat`, `Animals/dog`, etc. and predict uploaded images.")
-
-IMAGE_SIZE = (128, 128)
-DATA_DIR = "Animals"  # Folder with subfolders
-
-# 🔁 Load dataset and preprocess
-@st.cache_data
-def load_dataset():
-    X = []
-    y = []
-
-    for label in os.listdir(DATA_DIR):
-        folder = os.path.join(DATA_DIR, label)
-        if os.path.isdir(folder):
-            for file in os.listdir(folder):
-                if file.lower().endswith(('.png', '.jpg', '.jpeg')):
-                    try:
-                        path = os.path.join(folder, file)
-                        img = Image.open(path).convert("RGB").resize(IMAGE_SIZE)
-                        arr = img_to_array(img) / 255.0
-                        X.append(arr)
-                        y.append(label)
-                    except Exception as e:
-                        print(f"⚠️ Skipped {file}: {e}")
-    return np.array(X), np.array(y)
-
-X, y = load_dataset()
-
-# Stop if no data
-if len(X) == 0:
-    st.error("❌ No images found in 'Animals' folder.")
-    st.stop()
-
-# Encode labels
-le = LabelEncoder()
-y_encoded = le.fit_transform(y)
-y_cat = to_categorical(y_encoded)
-
-# 🧠 Train/test split
-X_train, X_test, y_train, y_test = train_test_split(X, y_cat, test_size=0.2, random_state=42)
-
-# 🧠 Build model
-@st.cache_resource
-def build_model():
-    model = Sequential([
-        Conv2D(32, (3, 3), activation='relu', input_shape=(*IMAGE_SIZE, 3)),
-        MaxPooling2D(2, 2),
-        Conv2D(64, (3, 3), activation='relu'),
-        MaxPooling2D(2, 2),
-        Flatten(),
-        Dense(128, activation='relu'),
-        Dropout(0.3),
-        Dense(len(np.unique(y_encoded)), activation='softmax')
-    ])
-    model.compile(optimizer=Adam(), loss='categorical_crossentropy', metrics=['accuracy'])
-    return model
-
-model = build_model()
-
-# 👨‍🏫 Train the model
-with st.spinner("Training model..."):
-    model.fit(X_train, y_train, epochs=5, batch_size=32, validation_split=0.1, verbose=0)
-
-st.success("✅ Model trained!")
-
-# 📤 Upload images for prediction
-uploaded_files = st.file_uploader("Upload animal images", type=["jpg", "jpeg", "png"], accept_multiple_files=True)
-
-if uploaded_files:
-    st.markdown("### Predictions")
-    cols = st.columns(len(uploaded_files[:3]))
-
-    for i, file in enumerate(uploaded_files[:3]):
-        with cols[i]:
-            img = Image.open(file).convert("RGB")
-            st.image(img, caption="Uploaded", use_container_width=True)
-
-            img_resized = img.resize(IMAGE_SIZE)
-            arr = img_to_array(img_resized) / 255.0
-            arr = np.expand_dims(arr, axis=0)
-
-            pred = model.predict(arr, verbose=0)[0]
-            top_idx = np.argmax(pred)
-            label = le.inverse_transform([top_idx])[0]
-            st.success(f"🔍 Prediction: **{label.capitalize()}**")
+import streamlit as st
+import numpy as np
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing.image import img_to_array
+from PIL import Image
+
+# 🔧 Configure Streamlit
+st.set_page_config(
+    page_title="Animal Classifier",
+    layout="centered",
+    initial_sidebar_state="auto"
+)
+
+# 📦 Load model and class labels
+@st.cache_resource
+def load_model_once():
+    return load_model("animal_model.keras")
+
+@st.cache_data
+def load_labels():
+    return np.load("class_labels.npy", allow_pickle=True).item()
+
+model = load_model_once()
+class_indices = load_labels()
+class_labels = list(class_indices.keys())
+
+# 🏷 App Title
+st.title("🐾 Animal Image Classifier")
+st.markdown("Upload up to **3 images** and get predictions")
+
+# 📤 Upload Images
+uploaded_files = st.file_uploader(
+    "Upload animal images",
+    type=["jpg", "jpeg", "png"],
+    accept_multiple_files=True
+)
+
+if uploaded_files:
+    # Limit to 3 images for clarity
+    uploaded_files = uploaded_files[:3]
+    cols = st.columns(len(uploaded_files))  
+
+    for idx, uploaded_file in enumerate(uploaded_files):
+        with cols[idx]:
+            st.markdown(f"### Image {idx+1}")
+
+            # 🖼️ Load & visually shrink image
+            image = Image.open(uploaded_file).convert("RGB")
+            preview = image.copy()
+            preview.thumbnail((150, 150))  
+            st.image(preview, caption="Preview", use_container_width=True)
+
+            # 🧪 Preprocess image for prediction
+            resized = image.resize((128, 128))  
+            img_array = img_to_array(resized) / 255.0
+            img_array = np.expand_dims(img_array, axis=0)
+
+            # 🔮 Model Prediction
+            preds = model.predict(img_array)[0]
+            top_indices = preds.argsort()[-3:][::-1]
+            top_labels = [class_labels[i] for i in top_indices]
+            top_scores = [preds[i] for i in top_indices]
+
+            # ✅ Display Top Prediction
+            st.success(f"**🔮Prediction:** {top_labels[0].capitalize()}")