Create app.py

app.py

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+import os
+os.environ["KERAS_BACKEND"] = "jax"
+
+import streamlit as st
+import keras
+import numpy as np
+import cv2
+from PIL import Image
+
+labels = ["angry","disgust","fear","happy","neutral","sad","surprise"]
+
+@st.cache_resource
+def load_model():
+    return keras.saving.load_model(
+        "hf://fdfddfdsaassd/vgg19-emotion-recognition-ckplus-rafdb"
+    )
+
+model = load_model()
+
+# UI DESIGN
+st.set_page_config(page_title="Emotion AI", page_icon="🧠", layout="centered")
+
+st.markdown("""
+    <h1 style='text-align:center; color:#6C63FF;'>🧠 Emotion AI Detector</h1>
+    <p style='text-align:center;'>Upload a face image and detect emotion using VGG19 (RAF-DB + CK+)</p>
+""", unsafe_allow_html=True)
+
+file = st.file_uploader("📤 Upload image", type=["jpg","png","jpeg"])
+
+if file:
+    img = Image.open(file)
+    st.image(img, caption="Uploaded Image", use_container_width=True)
+
+    img = np.array(img)
+
+    if img.shape[-1] == 4:
+        img = cv2.cvtColor(img, cv2.COLOR_RGBA2RGB)
+
+    img = cv2.resize(img, (224,224))
+    img = img / 255.0
+    img = np.expand_dims(img, axis=0)
+
+    pred = model.predict(img)[0]
+    idx = np.argmax(pred)
+
+    st.markdown("---")
+    st.markdown(f"### 😶 Prediction: **{labels[idx]}**")
+
+    st.bar_chart(pred)