Update app.py

app.py

@@ -1,11 +1,9 @@
 import streamlit as st
-from fer import FER
-import cv2
-import numpy as np
+from gradio_client import Client, file
+import tempfile
 import random
-import moviepy.editor
 
-# Example ayahs
+# --- Your Qur’an ayahs ---
 ayahs = {
     "sad": [
         {
@@ -33,43 +31,57 @@ ayahs = {
     ]
 }
 
-st.title("🌙 Qur’an Healing Soul - FER Version")
+# --- Streamlit UI ---
+st.set_page_config(page_title="Qur’an Healing Soul - Image Emotion", page_icon="🌙")
 
-uploaded_file = st.file_uploader("Upload your selfie", type=["jpg", "png", "jpeg"])
+st.title("🌙 Qur’an Healing Soul - Emotion from Image")
 
-if uploaded_file is not None:
-    # Convert to OpenCV image
-    file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
-    image = cv2.imdecode(file_bytes, 1)
-    st.image(image, channels="BGR")
+uploaded_file = st.file_uploader("Upload your selfie", type=["jpg", "jpeg", "png"])
 
-    # Detect emotion
-    detector = FER(mtcnn=True)
-    result = detector.top_emotion(image)
-    st.write(f"Detected: {result}")
+if uploaded_file:
+    st.image(uploaded_file, caption="Uploaded Image")
 
-    if result is not None:
-        emotion, score = result
-        st.write(f"Dominant Emotion: **{emotion}** ({round(score*100, 2)}%)")
+    with tempfile.NamedTemporaryFile(delete=False) as tmp_file:
+        tmp_file.write(uploaded_file.read())
+        tmp_file_path = tmp_file.name
 
-        if emotion in ["sad", "fear"]:
-            key = "sad"
-        elif emotion in ["happy", "surprise"]:
-            key = "happy"
-        elif emotion in ["angry", "disgust"]:
-            key = "angry"
-        else:
-            key = "sad"
+    st.info("Detecting emotion... please wait...")
 
-        ayah = random.choice(ayahs[key])
-        st.markdown(f"""
-        **📖 Ayah ({ayah['ayah']})**
+    # Call the remote FER model
+    client = Client("ElenaRyumina/Facial_Expression_Recognition")
 
-        Arabic: *{ayah['arabic']}*
+    result = client.predict(
+        inp=file(tmp_file_path),
+        api_name="/preprocess_image_and_predict"
+    )
 
-        Translation: {ayah['translation']}
+    # Unpack returned values
+    _, _, confidences = result
 
-        Tafsir: {ayah['tafsir']}
-        """)
+    st.subheader("Emotion Probabilities")
+    st.json(confidences)
+
+    # Find dominant emotion
+    dominant = max(confidences, key=confidences.get)
+    st.success(f"Dominant Emotion: **{dominant}**")
+
+    # Map emotion
+    if dominant.lower() in ["sad", "fear"]:
+        key = "sad"
+    elif dominant.lower() in ["happy", "surprise"]:
+        key = "happy"
+    elif dominant.lower() in ["angry", "disgust"]:
+        key = "angry"
     else:
-        st.warning("No face detected.")
+        key = "sad"
+
+    ayah = random.choice(ayahs[key])
+    st.markdown(f"""
+    ### 📖 Ayah ({ayah['ayah']})
+
+    **Arabic:** *{ayah['arabic']}*
+
+    **Translation:** {ayah['translation']}
+
+    **Tafsir:** {ayah['tafsir']}
+    """)