app update

app.py

@@ -0,0 +1,65 @@
+import streamlit as st
+import cv2
+import numpy as np
+from PIL import Image
+import tensorflow as tf
+face_detector = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
+# Load the Keras model
+model = tf.keras.models.load_model("/home/anil/Documents/College Projects/EmotionClassifierHF/EmotionClassifier/affectnet_CNN_VGG_FIVEEMO_FINE_FINAL.h5")
+
+# Mapping of emotion labels to their indices
+emotion_label_dict = {
+    0: 'neutral',
+    1: 'happiness',
+    2: 'sadness',
+    3: 'surprise',
+    4: 'fear',
+}
+
+# Function to detect faces in an image
+def detect_face(image):
+    img  =image
+    face = face_detector.detectMultiScale(img, 1.1, 5, minSize=(40, 40))
+
+    if len(face) > 0:
+        x, y, w, h = face[0]
+        crop_img = img[y:y+h, x:x+w]
+        cropped = cv2.resize(crop_img, (224, 224))
+        img_rgb = cv2.cvtColor(cropped, cv2.COLOR_BGR2RGB)
+        return img_rgb
+    else:
+        print("No face detected.")
+        return None
+
+# Function to classify emotion using the loaded model
+def classify_emotion(image):
+    # Preprocess the image
+    image = np.expand_dims(image, axis=0)
+    image = image / 255.0
+
+    # Make prediction using the model
+    predictions = model.predict(image)
+    emotion_index = np.argmax(predictions)
+    emotion_name = emotion_label_dict[emotion_index]
+
+    return emotion_name
+
+# Streamlit app
+def main():
+    st.title("Emotion Prediction App")
+
+    uploaded_file = st.file_uploader("Upload Image", type=["jpg", "png", "jpeg"])
+
+    if uploaded_file is not None:
+        image = Image.open(uploaded_file)
+        st.image(image, caption='Uploaded Image', use_column_width=True)
+
+        image_array = np.array(image)
+        detected_face = detect_face(image_array)
+
+        if detected_face is not None:
+            predicted_emotion = classify_emotion(detected_face)
+            st.write('Predicted Emotion:', predicted_emotion)
+
+if __name__ == '__main__':
+    main()