Update app.py

app.py

@@ -13,18 +13,22 @@ emotion_classifier = pipeline("text-classification", model="j-hartmann/emotion-e
 # Function to analyze colors in an image
 def analyze_colors(image):
     try:
-        # Convert image to RGB if not already in that mode
+        # Convert image to RGB if not already
         if image.mode != "RGB":
             image = image.convert("RGB")
-        # Resize image for faster processing
+        # Resize the image for faster processing (small but still sufficient for color analysis)
         image = image.resize((150, 150))
         # Convert to numpy array
         img_array = np.array(image)
         # Reshape image to be a list of pixels
         pixels = img_array.reshape((-1, 3))
 
+        # Check if there are enough pixels to perform KMeans
+        if len(pixels) < 5:
+            return "Image has too few pixels for analysis"
+
         # Use KMeans to find the dominant colors
-        kmeans = KMeans(n_clusters=5)
+        kmeans = KMeans(n_clusters=5, random_state=0)
         kmeans.fit(pixels)
         dominant_colors = kmeans.cluster_centers_
 
@@ -35,10 +39,12 @@ def analyze_colors(image):
         plt.show()
 
         return dominant_colors
+
     except Exception as e:
         print(f"Error in analyze_colors: {e}")
         return None
 
+
 # Function to detect emotions from colors (simplified emotion-color mapping)
 def color_emotion_analysis(dominant_colors):
     try: