app.py

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)