app.py

app.py

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+import cv2
+import numpy as np
+import streamlit as st
+from sklearn.cluster import KMeans
+
+def get_dominant_color(image, k=1):
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    image = image.reshape((-1, 3))
+    
+    kmeans = KMeans(n_clusters=k, random_state=0, n_init=10)
+    kmeans.fit(image)
+    
+    dominant_color = kmeans.cluster_centers_[0].astype(int)
+    return tuple(dominant_color)
+
+def is_warm_or_cool(color):
+    r, g, b = color
+    warm = (r > g and r > b)
+    return "Warm" if warm else "Cool"
+
+def complementary_color(color):
+    r, g, b = color
+    return (255 - r, 255 - g, 255 - b)
+
+st.title("VQA for Colors and Color Theory")
+uploaded_file = st.file_uploader("Upload an image", type=["jpg", "png", "jpeg"])
+
+if uploaded_file is not None:
+    file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
+    image = cv2.imdecode(file_bytes, cv2.IMREAD_COLOR)
+    
+    dominant = get_dominant_color(image)
+    comp_color = complementary_color(dominant)
+    
+    st.image(cv2.cvtColor(image, cv2.COLOR_BGR2RGB), caption="Uploaded Image", use_column_width=True)
+    st.write(f"**Dominant Color:** {dominant}")
+    st.write(f"**Temperature:** This is a {is_warm_or_cool(dominant)} color.")
+    st.write(f"**Complementary Color:** {comp_color}")