Update app.py

app.py

@@ -12,67 +12,84 @@ emotion_classifier = pipeline("text-classification", model="j-hartmann/emotion-e
 
 # Function to analyze colors in an image
 def analyze_colors(image):
-    # Convert image to RGB
-    image = image.convert("RGB")
-    # Resize image for faster processing
-    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))
-    
-    # Use KMeans to find the dominant colors
-    kmeans = KMeans(n_clusters=5)
-    kmeans.fit(pixels)
-    dominant_colors = kmeans.cluster_centers_
-    
-    # Plot the colors for visualization
-    plt.figure(figsize=(8, 6))
-    plt.imshow([dominant_colors.astype(int)])
-    plt.axis('off')
-    plt.show()
-    
-    return dominant_colors
+    try:
+        # Convert image to RGB if not already in that mode
+        if image.mode != "RGB":
+            image = image.convert("RGB")
+        # Resize image for faster processing
+        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))
+
+        # Use KMeans to find the dominant colors
+        kmeans = KMeans(n_clusters=5)
+        kmeans.fit(pixels)
+        dominant_colors = kmeans.cluster_centers_
+
+        # Plot the colors for visualization
+        plt.figure(figsize=(8, 6))
+        plt.imshow([dominant_colors.astype(int)])
+        plt.axis('off')
+        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):
-    # Map certain color ranges to emotions
-    emotions = []
-    for color in dominant_colors:
-        # Simple logic: darker tones could indicate sadness
-        if np.mean(color) < 85:
-            emotions.append("Sadness")
-        elif np.mean(color) > 170:
-            emotions.append("Happiness")
-        else:
-            emotions.append("Neutral")
-    
-    return emotions
+    try:
+        emotions = []
+        for color in dominant_colors:
+            # Simple logic: darker tones could indicate sadness
+            if np.mean(color) < 85:
+                emotions.append("Sadness")
+            elif np.mean(color) > 170:
+                emotions.append("Happiness")
+            else:
+                emotions.append("Neutral")
+        return emotions
+    except Exception as e:
+        print(f"Error in color_emotion_analysis: {e}")
+        return ["Error analyzing emotions"]
 
 # Function to analyze patterns and shapes using OpenCV
 def analyze_patterns(image):
-    # Convert to grayscale for edge detection
-    gray_image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2GRAY)
-    edges = cv2.Canny(gray_image, 100, 200)
-    
-    # Calculate the number of edges (chaos metric)
-    num_edges = np.sum(edges > 0)
-    
-    if num_edges > 10000:  # Arbitrary threshold for "chaos"
-        return "Chaotic patterns - possibly distress"
-    else:
-        return "Orderly patterns - possibly calm"
+    try:
+        # Convert to grayscale for edge detection
+        gray_image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2GRAY)
+        edges = cv2.Canny(gray_image, 100, 200)
+
+        # Calculate the number of edges (chaos metric)
+        num_edges = np.sum(edges > 0)
+
+        if num_edges > 10000:  # Arbitrary threshold for "chaos"
+            return "Chaotic patterns - possibly distress"
+        else:
+            return "Orderly patterns - possibly calm"
+    except Exception as e:
+        print(f"Error in analyze_patterns: {e}")
+        return "Error analyzing patterns"
 
 # Main function to process image and analyze emotional expression
 def analyze_emotion_from_image(image):
-    # Analyze colors
-    dominant_colors = analyze_colors(image)
-    color_emotions = color_emotion_analysis(dominant_colors)
-    
-    # Analyze patterns
-    pattern_analysis = analyze_patterns(image)
-    
-    return f"Color-based emotions: {color_emotions}\nPattern analysis: {pattern_analysis}"
+    try:
+        # Analyze colors
+        dominant_colors = analyze_colors(image)
+        if dominant_colors is None:
+            return "Error analyzing colors"
+        
+        color_emotions = color_emotion_analysis(dominant_colors)
+
+        # Analyze patterns
+        pattern_analysis = analyze_patterns(image)
+
+        return f"Color-based emotions: {color_emotions}\nPattern analysis: {pattern_analysis}"
+    except Exception as e:
+        return f"Error processing image: {str(e)}"
 
 # Gradio interface to upload image files and perform analysis
 iface = gr.Interface(fn=analyze_emotion_from_image, inputs="image", outputs="text")