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import streamlit as st
from PIL import Image, ImageOps
import numpy as np
import requests
# Function for image preprocessing
def preprocess_image(image):
# Convert image to grayscale
gray_image = ImageOps.grayscale(image)
# Resize the image to 48x48 (common for emotion recognition models)
resized_image = gray_image.resize((48, 48))
# Convert the image to a numpy array
image_array = np.array(resized_image)
# Normalize the image array (values between 0 and 1)
normalized_image = image_array / 255.0
return normalized_image
# Title and description
st.title("Emotion Recognition for Autism Support")
st.write("Upload an image, and the app will help identify emotions.")
# Upload image section
uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
if uploaded_file:
# Load the image using PIL
image = Image.open(uploaded_file)
st.image(image, caption="Uploaded Image", use_column_width=True)
st.success("Image uploaded successfully!")
# Preprocess the image
st.write("Preprocessing the image for emotion recognition...")
preprocessed_image = preprocess_image(image)
st.write("Preprocessing complete. Ready for emotion analysis.")
# Display preprocessed image
st.image(Image.fromarray((preprocessed_image * 255).astype('uint8')), caption="Preprocessed Image")
# Placeholder for emotion recognition (to be integrated with a model later)
st.info("Emotion recognition will be added in the next step.")
# Footer
st.write("---")
st.write("Developed to assist children with autism in recognizing emotions.")
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