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import streamlit as st
import cv2
import torch
from PIL import Image
from transformers import AutoImageProcessor, AutoModelForImageClassification
# Load processor and model
processor = AutoImageProcessor.from_pretrained("RickyIG/emotion_face_image_classification")
model = AutoModelForImageClassification.from_pretrained("RickyIG/emotion_face_image_classification")
# Title of the Streamlit app
st.title("Emotion Detection App")
# Option to choose between uploading image, video, or using live camera
option = st.radio("Select an option", ("Upload Image", "Upload Video", "Use Live Camera"))
if option == "Upload Image":
# Upload image
uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
if uploaded_file is not None:
# Display the uploaded image
image = Image.open(uploaded_file)
st.image(image, caption="Uploaded Image", use_container_width=True)
# Preprocess the image
inputs = processor(images=image, return_tensors="pt")
# Make predictions
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits # raw model outputs (before softmax)
predicted_class_idx = logits.argmax(-1).item() # predicted class index
# Get the label of the predicted class
label = model.config.id2label[predicted_class_idx]
# Display the result
st.write(f"Predicted Emotion: {label}")
elif option == "Upload Video":
# Upload video file
uploaded_video = st.file_uploader("Choose a video...", type=["mp4", "avi", "mov"])
if uploaded_video is not None:
# Save video to a temporary path
temp_video_path = "/tmp/uploaded_video.mp4"
with open(temp_video_path, "wb") as f:
f.write(uploaded_video.read())
# Open the video using OpenCV
cap = cv2.VideoCapture(temp_video_path)
if not cap.isOpened():
st.error("Error: Could not open video.")
else:
stframe = st.empty() # Placeholder to display live video feed
while True:
# Capture frame-by-frame
ret, frame = cap.read()
if not ret:
break
# Convert frame (BGR) to RGB (PIL format)
image = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
# Preprocess the image
inputs = processor(images=image, return_tensors="pt")
# Make predictions
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
predicted_class_idx = logits.argmax(-1).item()
label = model.config.id2label[predicted_class_idx]
# Display the result
cv2.putText(frame, f"Emotion: {label}", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2, cv2.LINE_AA)
# Convert the frame to RGB for Streamlit
frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
stframe.image(frame_rgb, channels="RGB", use_container_width=True)
cap.release()
elif option == "Use Live Camera":
cap = cv2.VideoCapture(0)
if not cap.isOpened():
st.error("Error: Could not open webcam.")
else:
stframe = st.empty()
while True:
ret, frame = cap.read()
if not ret:
break
# Convert frame (BGR) to RGB (PIL format)
image = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
# Preprocess the image
inputs = processor(images=image, return_tensors="pt")
# Make predictions
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
predicted_class_idx = logits.argmax(-1).item()
label = model.config.id2label[predicted_class_idx]
# Display the result
cv2.putText(frame, f"Emotion: {label}", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2, cv2.LINE_AA)
# Convert the frame to RGB for Streamlit
frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
stframe.image(frame_rgb, channels="RGB", use_container_width=True)
cap.release()
# Add a dynamic signature with Bastliga font
st.markdown("<br><br><h5 style='text-align: center;'>Developed by M.Nabeel</h5>", unsafe_allow_html=True)
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