app.py

import streamlit as st
import cv2
import numpy as np
import time
import plotly.graph_objects as go
from transformers import pipeline
from PIL import Image
import torch
from collections import deque
import os
import tempfile

# Set page config
st.set_page_config(
    page_title="Real-Time Emotion Detection",
    page_icon="😀",
    layout="wide"
)

# --- App Title and Description ---
st.title("Advanced Real-Time Emotion Detection")
st.write("""
This app detects emotions in real-time using webcam, video files, or images. 
If your webcam isn't working, try the simulation mode or upload a video file.
""")

# --- Load Models ---
@st.cache_resource(show_spinner=False)
def load_emotion_detector(model_name="dima806/facial_emotions_image_detection"):
    """Load the emotion detection model."""
    with st.spinner(f"Loading emotion detection model ({model_name})..."):
        classifier = pipeline("image-classification", model=model_name)
    return classifier

@st.cache_resource(show_spinner=False)
def load_face_detector():
    """Load the face detector model."""
    with st.spinner("Loading face detection model..."):
        # Load OpenCV's face detector
        face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
    return face_cascade

# --- Sidebar: Model and Settings ---
st.sidebar.header("Settings")

# Model selection
model_options = {
    "Facial Emotions (Default)": "dima806/facial_emotions_image_detection",
    "Facial Expressions": "juliensimon/distilbert-emotion"
}
selected_model = st.sidebar.selectbox(
    "Choose Emotion Model",
    list(model_options.keys())
)

# Input method selection with addition of video upload and simulation
input_method = st.sidebar.radio(
    "Choose Input Method",
    ["Real-time Webcam", "Upload Video", "Simulation Mode", "Upload an Image", "Capture Image"]
)

# Confidence threshold
confidence_threshold = st.sidebar.slider(
    "Confidence Threshold",
    min_value=0.0,
    max_value=1.0,
    value=0.5,
    step=0.05
)

# Face detection toggle
use_face_detection = st.sidebar.checkbox("Enable Face Detection", value=True)

# Processing speed for video/simulation
if input_method in ["Upload Video", "Simulation Mode"]:
    processing_speed = st.sidebar.slider(
        "Processing Speed",
        min_value=0.1,
        max_value=2.0,
        value=1.0,
        step=0.1,
        help="Adjust the speed of video processing (higher is faster)"
    )

# History length for real-time tracking
if input_method in ["Real-time Webcam", "Upload Video", "Simulation Mode"]:
    history_length = st.sidebar.slider(
        "Emotion History Length (seconds)",
        min_value=5,
        max_value=60,
        value=10,
        step=5
    )

# Load the selected model
classifier = load_emotion_detector(model_options[selected_model])
face_detector = load_face_detector()

# --- Utility Functions ---
def detect_faces(image):
    """Detect faces in an image using OpenCV."""
    # Convert PIL Image to OpenCV format
    if isinstance(image, Image.Image):
        opencv_image = np.array(image)
        opencv_image = opencv_image[:, :, ::-1].copy()  # Convert RGB to BGR
    else:
        opencv_image = image
    
    # Convert to grayscale for face detection
    gray = cv2.cvtColor(opencv_image, cv2.COLOR_BGR2GRAY)
    
    # Detect faces
    faces = face_detector.detectMultiScale(
        gray,
        scaleFactor=1.1,
        minNeighbors=5,
        minSize=(30, 30)
    )
    
    return faces, opencv_image

def process_image_for_emotion(image, face=None):
    """Process image for emotion detection."""
    if isinstance(image, np.ndarray):
        # Convert OpenCV image to PIL
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
        image = Image.fromarray(image)
    
    if face is not None:
        # Crop to face region
        x, y, w, h = face
        image = image.crop((x, y, x+w, y+h))
    
    return image

def predict_emotion(image):
    """Predict emotion from an image."""
    try:
        results = classifier(image)
        return results[0]  # Return top prediction
    except Exception as e:
        st.error(f"Error during emotion prediction: {str(e)}")
        return {"label": "Error", "score": 0.0}

def draw_faces_with_emotions(image, faces, emotions):
    """Draw rectangles around faces and label with emotions."""
    img = image.copy()
    
    # Define colors for different emotions (BGR format)
    emotion_colors = {
        "happy": (0, 255, 0),      # Green
        "sad": (255, 0, 0),        # Blue
        "neutral": (255, 255, 0),  # Cyan
        "angry": (0, 0, 255),      # Red
        "surprise": (255, 165, 0), # Orange
        "fear": (128, 0, 128),     # Purple
        "disgust": (0, 128, 128)   # Brown
    }
    
    # Default color for unknown emotions
    default_color = (255, 255, 255)  # White
    
    for (x, y, w, h), emotion in zip(faces, emotions):
        # Get color based on emotion (lowercase and remove any prefix)
        emotion_key = emotion["label"].lower().split("_")[-1]
        color = emotion_colors.get(emotion_key, default_color)
        
        # Draw rectangle around face
        cv2.rectangle(img, (x, y), (x+w, y+h), color, 2)
        
        # Add emotion label and confidence
        label = f"{emotion['label']} ({emotion['score']:.2f})"
        cv2.putText(img, label, (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
    
    return img

def generate_simulated_face(frame_num, canvas_size=(640, 480)):
    """Generate a simulated face with changing expressions."""
    # Create a blank canvas
    canvas = np.ones((canvas_size[1], canvas_size[0], 3), dtype=np.uint8) * 230
    
    # Calculate center position and face size
    center_x, center_y = canvas_size[0] // 2, canvas_size[1] // 2
    face_radius = min(canvas_size) // 4
    
    # Face movement based on frame number
    movement_x = int(np.sin(frame_num * 0.02) * 50)
    movement_y = int(np.cos(frame_num * 0.03) * 30)
    
    face_x = center_x + movement_x
    face_y = center_y + movement_y
    
    # Draw face circle
    cv2.circle(canvas, (face_x, face_y), face_radius, (220, 210, 180), -1)
    
    # Draw eyes
    eye_y = face_y - int(face_radius * 0.2)
    left_eye_x = face_x - int(face_radius * 0.5)
    right_eye_x = face_x + int(face_radius * 0.5)
    eye_size = max(5, face_radius // 8)
    
    # Blink occasionally
    if frame_num % 50 > 45:  # Blink every 50 frames for 5 frames
        cv2.ellipse(canvas, (left_eye_x, eye_y), (eye_size, 1), 0, 0, 360, (30, 30, 30), -1)
        cv2.ellipse(canvas, (right_eye_x, eye_y), (eye_size, 1), 0, 0, 360, (30, 30, 30), -1)
    else:
        cv2.circle(canvas, (left_eye_x, eye_y), eye_size, (255, 255, 255), -1)
        cv2.circle(canvas, (right_eye_x, eye_y), eye_size, (255, 255, 255), -1)
        cv2.circle(canvas, (left_eye_x, eye_y), eye_size-2, (70, 70, 70), -1)
        cv2.circle(canvas, (right_eye_x, eye_y), eye_size-2, (70, 70, 70), -1)
    
    # Draw mouth - change shape based on frame number to simulate different emotions
    mouth_y = face_y + int(face_radius * 0.3)
    mouth_width = int(face_radius * 0.6)
    mouth_height = int(face_radius * 0.2)
    
    # Cycle through different emotions based on frame number
    emotion_cycle = (frame_num // 100) % 4
    
    if emotion_cycle == 0:  # Happy
        # Smile
        cv2.ellipse(canvas, (face_x, mouth_y), (mouth_width, mouth_height), 
                    0, 0, 180, (50, 50, 50), 2)
    elif emotion_cycle == 1:  # Sad
        # Frown
        cv2.ellipse(canvas, (face_x, mouth_y + mouth_height), 
                    (mouth_width, mouth_height), 0, 180, 360, (50, 50, 50), 2)
    elif emotion_cycle == 2:  # Surprised
        # O mouth
        cv2.circle(canvas, (face_x, mouth_y), mouth_height, (50, 50, 50), 2)
    else:  # Neutral
        # Straight line
        cv2.line(canvas, (face_x - mouth_width//2, mouth_y), 
                (face_x + mouth_width//2, mouth_y), (50, 50, 50), 2)
    
    # Add some text showing what emotion is being simulated
    emotions = ["Happy", "Sad", "Surprised", "Neutral"]
    cv2.putText(canvas, f"Simulating: {emotions[emotion_cycle]}", 
                (20, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (50, 50, 50), 2)
    cv2.putText(canvas, "Simulation Mode - No webcam required", 
                (20, canvas_size[1] - 20), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (100, 100, 100), 1)
    
    return canvas

def process_video_feed(feed_source, is_simulation=False):
    """Process video feed (webcam, video file, or simulation)."""
    # Create placeholders
    video_placeholder = st.empty()
    metrics_placeholder = st.empty()
    chart_placeholder = st.empty()
    
    # Initialize session state for tracking emotions over time
    if 'emotion_history' not in st.session_state:
        st.session_state.emotion_history = {}
        st.session_state.last_update_time = time.time()
        st.session_state.frame_count = 0
        st.session_state.simulation_frame = 0
    
    # Start/Stop button
    start_button = st.button("Start" if 'running' not in st.session_state or not st.session_state.running else "Stop")
    
    if start_button:
        st.session_state.running = not st.session_state.get('running', False)
    
    # If running, capture and process video feed
    if st.session_state.get('running', False):
        try:
            # Initialize video source
            if is_simulation:
                # No need to open a video source for simulation
                pass
            else:
                cap = feed_source
                
                # Check if video source opened successfully
                if not cap.isOpened():
                    st.error("Could not open video source. Please check your settings.")
                    st.session_state.running = False
                    return
            
            # Create deques for tracking emotions
            emotion_deques = {}
            timestamp_deque = deque(maxlen=30*history_length)  # Store timestamps for X seconds at 30fps
            
            while st.session_state.get('running', False):
                # Get frame
                if is_simulation:
                    # Generate a simulated frame
                    frame = generate_simulated_face(st.session_state.simulation_frame)
                    st.session_state.simulation_frame += 1
                    ret = True
                else:
                    # Read from video source
                    ret, frame = cap.read()
                
                if not ret:
                    if is_simulation:
                        st.error("Simulation error")
                    elif input_method == "Upload Video":
                        # For video files, loop back to the beginning
                        cap.set(cv2.CAP_PROP_POS_FRAMES, 0)
                        continue
                    else:
                        st.error("Failed to capture frame from video source")
                    break
                
                # For webcam, flip horizontally for a more natural view
                if input_method == "Real-time Webcam" and not is_simulation:
                    frame = cv2.flip(frame, 1)
                
                # Increment frame count for FPS calculation
                st.session_state.frame_count += 1
                
                # Detect faces
                if use_face_detection:
                    faces, _ = detect_faces(frame)
                    
                    if len(faces) > 0:
                        # Process each face
                        emotions = []
                        for face in faces:
                            face_img = process_image_for_emotion(frame, face)
                            emotions.append(predict_emotion(face_img))
                        
                        # Draw faces with emotions
                        frame = draw_faces_with_emotions(frame, faces, emotions)
                        
                        # Update emotion history
                        current_time = time.time()
                        timestamp_deque.append(current_time)
                        
                        for i, emotion in enumerate(emotions):
                            if emotion["score"] >= confidence_threshold:
                                face_id = f"Face {i+1}"
                                if face_id not in emotion_deques:
                                    emotion_deques[face_id] = deque(maxlen=30*history_length)
                                
                                emotion_deques[face_id].append({
                                    "emotion": emotion["label"],
                                    "confidence": emotion["score"],
                                    "time": current_time
                                })
                    else:
                        # No faces detected
                        pass
                else:
                    # Process the whole frame
                    pil_image = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
                    emotion = predict_emotion(pil_image)
                    
                    # Display emotion on frame
                    cv2.putText(
                        frame,
                        f"{emotion['label']} ({emotion['score']:.2f})",
                        (10, 30),
                        cv2.FONT_HERSHEY_SIMPLEX,
                        1,
                        (0, 255, 0),
                        2
                    )
                    
                    # Update emotion history
                    current_time = time.time()
                    timestamp_deque.append(current_time)
                    
                    if "Frame" not in emotion_deques:
                        emotion_deques["Frame"] = deque(maxlen=30*history_length)
                    
                    emotion_deques["Frame"].append({
                        "emotion": emotion["label"],
                        "confidence": emotion["score"],
                        "time": current_time
                    })
                
                # Calculate FPS
                current_time = time.time()
                time_diff = current_time - st.session_state.last_update_time
                if time_diff >= 1.0:  # Update every second
                    fps = st.session_state.frame_count / time_diff
                    st.session_state.last_update_time = current_time
                    st.session_state.frame_count = 0
                    
                    # Update metrics
                    with metrics_placeholder.container():
                        cols = st.columns(3)
                        cols[0].metric("FPS", f"{fps:.1f}")
                        cols[1].metric("Faces Detected", len(faces) if use_face_detection else "N/A")
                
                # Display the frame
                video_placeholder.image(frame, channels="BGR", use_column_width=True)
                
                # Update emotion history chart periodically
                if len(timestamp_deque) > 0 and time_diff >= 0.5:  # Update chart every 0.5 seconds
                    with chart_placeholder.container():
                        # Create tabs for each face
                        if len(emotion_deques) > 0:
                            tabs = st.tabs(list(emotion_deques.keys()))
                            
                            for i, (face_id, emotion_data) in enumerate(emotion_deques.items()):
                                with tabs[i]:
                                    if len(emotion_data) > 0:
                                        # Count occurrences of each emotion
                                        emotion_counts = {}
                                        for entry in emotion_data:
                                            emotion = entry["emotion"]
                                            if emotion not in emotion_counts:
                                                emotion_counts[emotion] = 0
                                            emotion_counts[emotion] += 1
                                        
                                        # Create pie chart for emotion distribution
                                        fig = go.Figure(data=[go.Pie(
                                            labels=list(emotion_counts.keys()),
                                            values=list(emotion_counts.values()),
                                            hole=.3
                                        )])
                                        fig.update_layout(title=f"Emotion Distribution - {face_id}")
                                        st.plotly_chart(fig, use_container_width=True)
                                        
                                        # Create line chart for emotion confidence over time
                                        emotions = list(emotion_data)[-20:]  # Get the last 20 entries
                                        times = [(e["time"] - emotions[0]["time"]) for e in emotions]
                                        confidences = [e["confidence"] for e in emotions]
                                        emotion_labels = [e["emotion"] for e in emotions]
                                        
                                        fig = go.Figure()
                                        fig.add_trace(go.Scatter(
                                            x=times,
                                            y=confidences,
                                            mode='lines+markers',
                                            text=emotion_labels,
                                            hoverinfo='text+y'
                                        ))
                                        fig.update_layout(
                                            title=f"Emotion Confidence Over Time - {face_id}",
                                            xaxis_title="Time (seconds)",
                                            yaxis_title="Confidence",
                                            yaxis=dict(range=[0, 1])
                                        )
                                        st.plotly_chart(fig, use_container_width=True)
                                    else:
                                        st.info(f"No emotion data available for {face_id} yet.")
                        else:
                            st.info("No emotion data available yet.")
                
                # Control processing speed for videos and simulation
                if input_method in ["Upload Video", "Simulation Mode"]:
                    time.sleep(0.03 / processing_speed)  # Adjust delay based on processing_speed
            
            # Release resources when done
            if not is_simulation and cap.isOpened():
                cap.release()
        
        except Exception as e:
            st.error(f"Error during processing: {str(e)}")
            st.session_state.running = False
    else:
        # Display a placeholder image when not running
        placeholder_img = np.zeros((300, 500, 3), dtype=np.uint8)
        cv2.putText(
            placeholder_img,
            "Click 'Start' to begin",
            (80, 150),
            cv2.FONT_HERSHEY_SIMPLEX,
            1,
            (255, 255, 255),
            2
        )
        video_placeholder.image(placeholder_img, channels="BGR", use_column_width=True)

# --- Process uploaded image ---
def process_static_image(image):
    col1, col2 = st.columns(2)
    with col1:
        st.image(image, caption="Image", use_column_width=True)
    
    # Process image
    if use_face_detection:
        faces, opencv_image = detect_faces(image)
        
        if len(faces) > 0:
            emotions = []
            for face in faces:
                face_img = process_image_for_emotion(image, face)
                emotions.append(predict_emotion(face_img))
            
            # Draw faces with emotions
            result_image = draw_faces_with_emotions(opencv_image, faces, emotions)
            
            with col2:
                st.image(result_image, caption="Detected Emotions", channels="BGR", use_column_width=True)
            
            # Display predictions
            st.subheader("Detected Emotions:")
            for i, (emotion, face) in enumerate(zip(emotions, faces)):
                if emotion["score"] >= confidence_threshold:
                    st.write(f"Face {i+1}: **{emotion['label']}** (Confidence: {emotion['score']:.2f})")
                    
                    # Show confidence bars
                    top_emotions = classifier(process_image_for_emotion(image, face))
                    labels = [item["label"] for item in top_emotions]
                    scores = [item["score"] for item in top_emotions]
                    
                    fig = go.Figure(go.Bar(
                        x=scores,
                        y=labels,
                        orientation='h'
                    ))
                    fig.update_layout(
                        title=f"Emotion Confidence - Face {i+1}",
                        xaxis_title="Confidence",
                        yaxis_title="Emotion",
                        height=300
                    )
                    st.plotly_chart(fig, use_container_width=True)
        else:
            st.warning("No faces detected in the image. Try another image or disable face detection.")
    else:
        # Process the whole image
        prediction = predict_emotion(image)
        st.subheader("Prediction:")
        st.write(f"**Emotion:** {prediction['label']}")
        st.write(f"**Confidence:** {prediction['score']:.2f}")

# --- Main App Logic ---
if input_method == "Upload an Image":
    uploaded_file = st.file_uploader("Choose an image file", type=["jpg", "jpeg", "png"])
    
    if uploaded_file is not None:
        # Load and display image
        image = Image.open(uploaded_file).convert("RGB")
        process_static_image(image)

elif input_method == "Capture Image":
    picture = st.camera_input("Capture an Image")
    
    if picture is not None:
        image = Image.open(picture).convert("RGB")
        process_static_image(image)

elif input_method == "Upload Video":
    uploaded_video = st.file_uploader("Upload a video file", type=["mp4", "avi", "mov", "mkv"])
    
    if uploaded_video is not None:
        # Save the uploaded video to a temporary file
        tfile = tempfile.NamedTemporaryFile(delete=False)
        tfile.write(uploaded_video.read())
        
        # Open the video file
        cap = cv2.VideoCapture(tfile.name)
        
        # Process the video
        process_video_feed(cap)
        
        # Clean up the temporary file
        os.unlink(tfile.name)

elif input_method == "Simulation Mode":
    st.info("Simulation mode uses a generated animated face. No webcam required!")
    process_video_feed(None, is_simulation=True)

elif input_method == "Real-time Webcam":
    try:
        # First check if we can access the webcam
        cap = cv2.VideoCapture(0)
        if not cap.isOpened():
            st.error("Could not open webcam. Please try the Simulation Mode instead.")
            st.info("If you're using Streamlit in a browser, make sure you've granted camera permissions.")
            
            # Show troubleshooting tips
            with st.expander("Webcam Troubleshooting Tips"):
                st.markdown("""
                1. **Check Browser Permissions**: Make sure your browser has permission to access your camera.
                2. **Close Other Applications**: Other applications might be using your webcam.
                3. **Refresh the Page**: Sometimes simply refreshing can resolve the issue.
                4. **Try a Different Browser**: Some browsers handle webcam access better than others.
                5. **Use Simulation Mode**: If you cannot get the webcam working, use the Simulation Mode.
                """)
        else:
            # Webcam available, process it
            process_video_feed(cap)
    except Exception as e:
        st.error(f"Error accessing webcam: {str(e)}")
        st.info("Please try the Simulation Mode instead, which doesn't require webcam access.")

# --- Footer ---
st.markdown("---")
st.markdown("""
**Tips for Best Results:**
- If webcam doesn't work, try "Simulation Mode" or "Upload Video" options
- Ensure good lighting for accurate face detection
- Position faces clearly in the frame
- Try different emotion models for comparison
- Adjust the confidence threshold if emotions aren't being detected correctly
""")