Create app.py

app.py

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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.")