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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+emotion_detector/variables/variables.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text

app.py

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+# import libraries
+import streamlit as st
+from streamlit_image_select import image_select
+import tensorflow as tf
+import numpy as np
+import cv2
+from PIL import Image
+
+# Project title
+st.title('Emotion Detector App')
+
+# display banner image
+st.image("banner_emotions.jpg")
+
+img = image_select(
+    label = "Select an image to run model",
+    images = ["test_images/angry1.jpg",
+              "test_images/angry2.jpg",
+              "test_images/happy1.jpg",
+              "test_images/happy2.jpg",
+              "test_images/sad1.jpg",
+              "test_images/sad2.jpg"]
+)
+
+uploaded_img = st.file_uploader("Upload an image file",
+                                type  = ["png", "jpg", "jpeg"])
+
+
+# load model
+@st.cache_resource
+def cache_model(model_add):
+    model = tf.keras.models.load_model(model_add)
+    return model
+
+model = cache_model("emotion_detector")
+
+# creating predict button
+predict = st.button("Predict")
+
+# defining harcascade classifier and class_names
+face_detector=cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
+class_names = ["Angry", "Happy", "Sad"]
+
+def model_pred(model, image):
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    results = face_detector.detectMultiScale(gray,scaleFactor=1.05,
+                                             minNeighbors=10,
+                                               minSize=(100, 100))
+    if len(results) != 0:
+        for x,y,w,h in results:
+            img_crp = image[y:y+h, x:x+w]
+            img_crp = cv2.resize(img_crp,(350,350))
+            y_pred_prob = model.predict(tf.expand_dims(img_crp,
+                                                        axis = 0))
+            y_pred = np.argmax(y_pred_prob, axis = -1)
+            # print(y_pred_prob)
+            label = class_names[int(y_pred)]
+            cv2.rectangle(image, (x,y), (x+w, y+h),
+                           color=(0, 255, 0),
+                             thickness = 10)
+            cv2.putText(image,f"{label},{round(np.max(y_pred_prob),2)}%",
+                        (x,y+h),cv2.FONT_HERSHEY_COMPLEX,2,
+                        (0,255,255),2)
+    return image
+
+if predict:
+    if uploaded_img:
+        # img_array = np.array(uploaded_img)
+        img_array = np.array(Image.open(uploaded_img))
+        result_img = model_pred(model,img_array)
+        st.image(result_img)
+
+    else:
+        st.write("Please upload a valid image")
+     
+else:
+    image_array = np.array(Image.open(img))
+    result_img = model_pred(model, image_array)
+    st.image(result_img)

emotion_detector/fingerprint.pb

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+oid sha256:1240ba33fd6858d6055483ccd1a0f72df74f12f2cf32c2ef9f9477d7bd3d77e6
+size 58

emotion_detector/keras_metadata.pb

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+size 897946

emotion_detector/saved_model.pb

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+oid sha256:6b7b5b2aa1b77ad3e1b66d04c83c75de5c9b438a6284d8e5c5298632c7fa0654
+size 4927861

emotion_detector/variables/variables.data-00000-of-00001

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