Update app.py

app.py

@@ -13,21 +13,29 @@ 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
+        # Ensure the image is in RGB format
         if image.mode != "RGB":
             image = image.convert("RGB")
-        # Resize the image for faster processing (small but still sufficient for color analysis)
+
+        # Resize the image for faster processing
         image = image.resize((150, 150))
+
         # Convert to numpy array
         img_array = np.array(image)
+
+        # Check if the image has a valid shape
+        if img_array.ndim != 3 or img_array.shape[2] != 3:
+            raise ValueError("Invalid image array shape: Expected a 3D array with 3 channels.")
+
         # Reshape image to be a list of pixels
         pixels = img_array.reshape((-1, 3))
 
-        # Check if there are enough pixels to perform KMeans
+        print(f"Image shape: {img_array.shape}")  # Debugging line
+        print(f"Number of pixels: {len(pixels)}")  # Debugging line
+
         if len(pixels) < 5:
             return "Image has too few pixels for analysis"
 
-        # Use KMeans to find the dominant colors
         kmeans = KMeans(n_clusters=5, random_state=0)
         kmeans.fit(pixels)
         dominant_colors = kmeans.cluster_centers_
@@ -41,10 +49,11 @@ def analyze_colors(image):
         return dominant_colors
 
     except Exception as e:
-        print(f"Error in analyze_colors: {e}")
+        print(f"Error in analyze_colors: {e}")  # Capture the error
         return None
 
 
+
 # Function to detect emotions from colors (simplified emotion-color mapping)
 def color_emotion_analysis(dominant_colors):
     try: