app.py

import cv2
import numpy as np
import os
import onnxruntime as ort
import streamlit as st
from PIL import Image

# Preprocess image to match model input requirements
def preprocess_image(image, face_landmarks=None):
    image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2GRAY)  # Convert image to grayscale
    image_resized = cv2.resize(image, (48, 48))  # Resize image to 48x48
    image_input = np.expand_dims(image_resized, axis=0)  # Add batch dimension
    image_input = np.expand_dims(image_input, axis=0)  # Add channel dimension (for grayscale)
    image_input = image_input.astype(np.float32) / 255.0  # Normalize the image
    return image_input

# Check if smile is present in the facial landmarks
def check_for_smile(face_landmarks):
    """Simple rule to check for smile based on landmarks"""
    mouth = face_landmarks['bottom_lip'] + face_landmarks['top_lip']
    mouth_distance = np.linalg.norm(np.array(mouth[0]) - np.array(mouth[-1]))
    if mouth_distance > 30:  # This threshold might need adjustment
        return True
    return False

# Display emotion with post-processing to check for smiles
def display_emotion(emotion):
    """Map predicted emotion index to a label"""
    emotion_map = {
        0: "Anger",
        1: "Disgust",
        2: "Fear",
        3: "Happiness",
        4: "Sadness",
        5: "Surprise",
        6: "Neutral"
    }
    return emotion_map.get(emotion, "Unknown")

# Display emotion with smile detection
def display_emotion_with_smile(emotion, face_landmarks=None):
    if emotion == 6 and face_landmarks:  # 'Neutral' is typically 6 in the emotion_map
        if check_for_smile(face_landmarks):
            return "Happiness"  # Override neutral with happiness if a smile is detected
    return display_emotion(emotion)  # Otherwise return the normal emotion

# Predict emotion with smile detection
def predict_emotion_with_smile(image_input, face_landmarks=None):
    """Run inference and predict the emotion, considering smile detection"""
    emotion = predict_emotion(image_input)  # Normal emotion prediction
    emotion_label = display_emotion_with_smile(emotion, face_landmarks)
    return emotion_label

# Load ONNX model
def load_model():
    model_path = 'onnx_model.onnx'  # Make sure this is the correct path
    if not os.path.exists(model_path):
        raise FileNotFoundError(f"Model file {model_path} not found!")
    emotion_model = ort.InferenceSession(model_path)
    return emotion_model

# Predict emotion using the ONNX model
def predict_emotion(image_input):
    emotion_model = load_model()
    input_name = emotion_model.get_inputs()[0].name
    output_name = emotion_model.get_outputs()[0].name
    prediction = emotion_model.run([output_name], {input_name: image_input})
    return np.argmax(prediction[0])

# Streamlit app code
st.title("Emotion Recognition App")

# Upload an image
uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])

# If an image is uploaded
if uploaded_file is not None:
    # Open and display the uploaded image
    image = Image.open(uploaded_file)
    st.image(image, caption="Uploaded Image", use_column_width=True)

    # Preprocess the image
    image_input = preprocess_image(image)

    # Predict the emotion
    emotion_label = predict_emotion_with_smile(image_input)

    # Display the predicted emotion
    st.write(f"Detected Emotion: {emotion_label}")