Upload 4 files

app.py

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+import streamlit as st
+from fastai.vision.all import *
+import cv2
+import numpy as np
+import tempfile
+model=load_learner('fer_model.pkl')
+clf=cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
+st.set_page_config('Face Emotion Recognition')
+st.title('Face Emotion Recognition')
+image=st.file_uploader('Upload Image',type=['png','jpg','jpeg','mp4','mpeg'])
+if st.button('Predict'):
+    image=PILImage.create(image)
+    if image:
+        if image.name.split('.')[-1] in ['png','jpg','jpeg']:
+            image_ = PILImage.create(image)
+            prediction = model.predict(image_)
+            st.image(image, use_column_width='auto')
+            st.write(f'Image predicted as {prediction[0]}')
+        else:
+            tfile=tempfile.NamedTemporaryFile(delete=False)
+            tfile.write(image.read())
+            vf=cv2.VideoCapture(tfile.name)
+            stframe=st.empty()
+            while vf.isOpened():
+                ret,frame=vf.read()
+                if ret:
+                    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+                    faces = clf.detectMultiScale(gray, scaleFactor=1.3, minNeighbors=5)
+                    for (x, y, w, h) in faces:
+                        cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 0, 255), 2)
+                        face = frame[y:y + h, x:x + w]
+                        prediction=model.predict(face)
+                        frame = cv2.putText(frame,(prediction[0]), (x , y ),
+                                            cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2)
+                    stframe.image(frame)
+                else:
+                    break

fer_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b3513cfcdfee5a96fb28e2968a44c514857a252f0d30428f46e90760d80c314d
+size 88242874

requirements.txt

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+streamlit==1.29.0
+fastai==2.7.12
+opencv-python==4.7.0.72
+numpy==1.23.5