added all files from git hub (html --> streamlit)

CNN_Model_acc_75.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4f17414ec703aa77db97a22766b6f4454f766e367148285d16cbbc729e69725c
+size 94225136

app.py

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+import streamlit as st
+import cv2
+import numpy as np
+import time
+# Larger title
+st.markdown("<h1 style='text-align: center;'>Emotion Detection</h1>", unsafe_allow_html=True)
+
+# Smaller subtitle
+st.markdown("<h3 style='text-align: center;'>angry, fear, happy, neutral, sad, surprise</h3>", unsafe_allow_html=True)
+start = time.time()
+from keras.models import load_model
+import tempfile
+from PIL import Image
+
+@st.cache_resource
+def load_emotion_model():
+    model = load_model('CNN_Model_acc_75.h5')
+    return model
+
+# Load the model
+model = load_emotion_model()
+print("time taken to load model : " , time.time() - start)
+img_shape = 48
+emotion_labels = ['angry', 'fear', 'happy', 'neutral', 'sad', 'surprise']
+face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
+
+
+def process_frame(frame):
+    gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+    faces = face_cascade.detectMultiScale(gray_frame, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
+
+    for (x, y, w, h) in faces:
+        roi_gray = gray_frame[y:y+h, x:x+w]
+        roi_color = frame[y:y+h, x:x+w]
+
+        face_roi = cv2.resize(roi_color, (img_shape, img_shape))
+        face_roi = np.expand_dims(face_roi, axis=0)
+        face_roi = face_roi / float(img_shape)
+        predictions = model.predict(face_roi)
+        emotion = emotion_labels[np.argmax(predictions[0])]
+
+        cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
+        cv2.putText(frame, emotion, (x, y+h), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
+
+    return frame
+
+# def video_feed(video_source):
+#     # Read and process video frames
+#     while True:
+#         ret, frame = video_source.read()
+#         if not ret:
+#             break
+#         frame = process_frame(frame)
+#         st.image(frame, channels="BGR")
+
+def video_feed(video_source):
+    # Create a placeholder to display the frames
+    frame_placeholder = st.empty()  # This placeholder will be used to replace frames in-place
+
+    while True:
+        ret, frame = video_source.read()
+        if not ret:
+            break
+
+        frame = process_frame(frame)
+
+        # Display the frame in the placeholder
+        frame_placeholder.image(frame, channels="BGR", use_column_width=True)
+
+
+
+# Sidebar for video or image upload
+upload_choice = st.sidebar.radio("Choose input source", [ "Upload Video", "Upload Image" ,"Camera"])
+
+if upload_choice == "Camera":
+    # Access camera
+    video_source = cv2.VideoCapture(0)
+    video_feed(video_source)
+
+elif upload_choice == "Upload Video":
+    uploaded_video = st.file_uploader("Upload Video", type=["mp4", "mov", "avi", "mkv", "webm"])
+    if uploaded_video:
+        # Temporarily save the video to disk
+        with tempfile.NamedTemporaryFile(delete=False) as tfile:
+            tfile.write(uploaded_video.read())
+            video_source = cv2.VideoCapture(tfile.name)
+            video_feed(video_source)
+
+elif upload_choice == "Upload Image":
+    uploaded_image = st.file_uploader("Upload Image", type=["png", "jpg", "jpeg", "gif"])
+    if uploaded_image:
+        image = Image.open(uploaded_image)
+        frame = np.array(image)
+        frame = process_frame(frame)
+        st.image(frame, caption='Processed Image', use_column_width=True)
+
+st.sidebar.write("Emotion Labels: Angry, Fear, Happy, Neutral, Sad, Surprise")

requirements.txt

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+numpy
+pandas
+flask
+opencv-python
+keras
+tensorflow