Create app.py

app.py

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+import streamlit as st
+from PIL import Image
+import numpy as np
+import cv2
+from tensorflow.keras.models import load_model
+import os
+# Ensure the 'upload' directory exists
+upload_folder = 'uploads'
+if not os.path.exists(upload_folder):
+    os.makedirs(upload_folder)
+    
+# Load the pre-trained model
+model = load_model("emotion_detector.keras")
+
+def get_result(img_path):
+    img = cv2.imread(img_path)
+    img_resize = cv2.resize(img, (224, 224))
+    img_resize = np.array(img_resize, dtype=np.float32)
+    img_resize /= 255.0
+    img_input = img_resize.reshape(1, 224, 224, 3)
+    prediction = model.predict(img_input)
+    emotion_dict = {0: 'angry 😡',
+                    1: 'disgust 🤢',
+                    2: 'fear 😱',
+                    3: 'happy 😀',
+                    4: 'neutral 😐',
+                    5: 'sad 😢',
+                    6: 'surprise 😲'}
+    
+    max_index = np.argmax(np.array(prediction[0]))
+    pred=int(np.round(prediction[0][max_index]))
+    emotion = emotion_dict[max_index]
+
+    return f"He/she is feeling {emotion}"
+    
+        
+st.title("Let\'s detect the Emotion 😀 😢 😡 😱 🤢 😲 😐 ")
+
+uploaded_image = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+if uploaded_image is not None:
+
+    image = Image.open(uploaded_image)
+    
+    image_path = os.path.join(upload_folder, uploaded_image.name)
+    image.save(image_path)
+    output = get_result(image_path)
+    
+    st.write(output)
+    st.image(image, use_container_width=True)