app.py

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

# Set the page config
st.set_page_config(page_title="Emotion Recognition App", layout="centered")

st.title("Emotion Recognition App")

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

# Load the ONNX model using onnxruntime
@st.cache_resource
def load_model():
    # Path to the uploaded ONNX model (should be the name of the model file you uploaded)
    model_path = "emotion_model.onnx"
    return ort.InferenceSession(model_path)

# Load the model
emotion_model = load_model()

# Class labels for facial emotions (based on the training dataset)
emotion_labels = ['Anger', 'Disgust', 'Fear', 'Happiness', 'Sadness', 'Surprise', 'Neutral']

# Preprocess image to match model input requirements
def preprocess_image(image):
    # Convert image to grayscale and resize to match the input size expected by the model
    image_gray = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2GRAY)
    image_resized = cv2.resize(image_gray, (64, 64))  # The model expects 64x64 input
    image_normalized = image_resized / 255.0  # Normalize to [0, 1] range
    image_reshaped = np.expand_dims(image_normalized, axis=0)  # Add batch dimension
    image_reshaped = np.expand_dims(image_reshaped, axis=0)  # Add channel dimension (1 channel for grayscale)
    return image_reshaped.astype(np.float32)

# Process the uploaded image
if uploaded_file is not None:
    # Open and preprocess the image
    image = Image.open(uploaded_file).convert("RGB")
    processed_image = preprocess_image(image)

    # Perform inference
    input_name = emotion_model.get_inputs()[0].name
    outputs = emotion_model.run(None, {input_name: processed_image})
    predicted_class = np.argmax(outputs[0], axis=1)[0]  # Get the index of the highest probability
    predicted_emotion = emotion_labels[predicted_class]

    # Display the results
    st.image(image, caption=f"Detected Emotion: {predicted_emotion}", use_column_width=True)