Create App.py

App.py

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+import streamlit as st
+from keras.models import load_model
+from PIL import Image
+import numpy as np
+import cv2
+
+# Load model once
+@st.cache_resource
+def load_expression_model():
+    return load_model("expression_model.h5")
+
+model = load_expression_model()
+
+# Define class labels (update based on your training)
+class_names = ['Angry', 'Disgust', 'Fear', 'Happy', 'Sad', 'Surprise', 'Neutral']
+
+# Resize and preprocess image
+def preprocess_image(img):
+    img = img.convert('L')  # convert to grayscale
+    img = img.resize((48, 48))
+    img_array = np.array(img)
+    img_array = img_array / 255.0  # normalize
+    img_array = np.expand_dims(img_array, axis=0)
+    img_array = np.expand_dims(img_array, axis=-1)
+    return img_array
+
+# Streamlit UI
+st.title("Facial Expression Classifier 😊😢😠")
+st.write("Upload an image and the model will predict the facial expression.")
+
+uploaded_file = st.file_uploader("Upload an image...", type=["jpg", "png", "jpeg"])
+
+if uploaded_file is not None:
+    img = Image.open(uploaded_file)
+    st.image(img, caption="Uploaded Image", use_column_width=True)
+
+    with st.spinner('Analyzing...'):
+        processed_img = preprocess_image(img)
+        prediction = model.predict(processed_img)
+        class_index = np.argmax(prediction)
+        st.success(f"Predicted Expression: **{class_names[class_index]}**")