Create classifier.py

classifier.py

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+# Import library
+import streamlit as st
+import pandas as pd
+import numpy as np
+from PIL import Image
+import pickle
+import json
+import matplotlib.pyplot as plt
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing import image
+from tensorflow.keras.applications.efficientnet import preprocess_input
+
+# Load trained model
+model = load_model('emotion_detection.h5', compile=False)
+# Define class labels
+class_labels = ['Contempt', 'angry', 'disgust','fear','happy','neutral','sad','surprised']
+  
+def predict_and_display(uploaded_file, model, class_labels):
+    img = Image.open(uploaded_file)
+    img = img.resize((224, 224))
+    img_array = np.array(img)
+    img_array = np.expand_dims(img_array, axis=0)
+    img_array = preprocess_input(img_array)
+
+    prediction = model.predict(img_array)
+    predicted_class_index = np.argmax(prediction)
+    predicted_class_label = class_labels[predicted_class_index]
+
+    st.image(img, use_column_width=True)
+    st.write(f"Detected Emoption of the Facial Expression is: {predicted_class_label}")
+
+def run():
+    st.write('##### Facial Emotions/Expressions Detection')
+    # Making Form
+    # Create a Streamlit form
+    with st.form(key='Facial Emotions/Expressions Detection'):
+        # Add a file uploader to the form
+        uploaded_files = st.file_uploader("Upload a file of one of these format .JPEG/.JPG/.PNG file", accept_multiple_files=True)
+
+        # Check if any file is uploaded
+        if uploaded_files:
+            for uploaded_file in uploaded_files:
+                st.write("filename:", uploaded_file.name)
+        # Close the form
+        submitted = st.form_submit_button('Predict')
+    
+    if submitted:
+        for uploaded_file in uploaded_files:
+            # Use the predict_and_display function with the uploaded image data
+            predict_and_display(uploaded_file, model, class_labels)
+
+        
+if __name__ == '__main__':
+    run()