Update app.py

app.py

@@ -335,7 +335,10 @@ def get_predictions(audio_input, generate_audio=True, chunk_duration=10):
     
     return results
 
-# Function to create a fade transition between images
+
+
+
+# Replace the create_fade_transition function with this updated version
 def create_fade_transition(images, fade_duration=1.0, fps=24):
     """
     Create a smooth fade transition between a sequence of images.
@@ -347,7 +350,7 @@ def create_fade_transition(images, fade_duration=1.0, fps=24):
     
     Returns:
         PIL Image containing the first frame of the animation (for display)
-        BytesIO object containing the animation as a GIF (for download)
+        Path to the saved GIF file (for download)
     """
     if not images or len(images) < 2:
         return None, None
@@ -382,20 +385,25 @@ def create_fade_transition(images, fade_duration=1.0, fps=24):
         for _ in range(fade_frames):
             frames.append(img2)
     
-    # Save as GIF to BytesIO
-    buffer = BytesIO()
-    frames[0].save(
-        buffer,
-        format='GIF',
-        save_all=True,
-        append_images=frames[1:],
-        duration=1000//fps,  # Duration per frame in milliseconds
-        loop=0  # Loop indefinitely
-    )
-    buffer.seek(0)
+    # Create a temporary file to save the GIF
+    with tempfile.NamedTemporaryFile(suffix=".gif", delete=False) as tmp_file:
+        # Save as GIF
+        frames[0].save(
+            tmp_file.name,
+            format='GIF',
+            save_all=True,
+            append_images=frames[1:],
+            duration=1000//fps,  # Duration per frame in milliseconds
+            loop=0  # Loop indefinitely
+        )
     
-    # Return the first frame for display and the buffer for download
-    return images[0], buffer
+    # Return the first frame for display and the file path for download
+    return images[0], tmp_file.name
+
+
+
+
+
 
 # Update the process_and_display function to handle the fade animation correctly
 def process_and_display(audio_input, generate_audio, chunk_duration):
@@ -445,13 +453,13 @@ def process_and_display(audio_input, generate_audio, chunk_duration):
     
     # Create fade animation if we have multiple images
     fade_preview = None
-    fade_animation = None
+    fade_animation_path = None
     if len(all_images) > 1:
         # Create a fade animation (GIF)
-        fade_preview, fade_animation = create_fade_transition(all_images, fade_duration=1.5, fps=15)
+        fade_preview, fade_animation_path = create_fade_transition(all_images, fade_duration=1.5, fps=15)
     
     # Hide loading indicator and show results
-    yield [gr.HTML("")] + group_visibility + outputs + [fade_preview, fade_animation]
+    yield [gr.HTML("")] + group_visibility + outputs + [fade_preview, fade_animation_path]
 
 # Update the clear_all function to handle the new outputs
 def clear_all():
@@ -567,60 +575,7 @@ with gr.Blocks(title="Affective Virtual Environments - Chunked Processing") as i
             interactive=False
         )
     
-    # Function to process and display results
-    def process_and_display(audio_input, generate_audio, chunk_duration):
-        # Validate chunk duration
-        if chunk_duration is None or chunk_duration <= 0:
-            chunk_duration = 10
-        
-        # Show loading indicator
-        yield [gr.HTML(f"""
-            <div style="text-align: center; margin: 20px;">
-                <p style="font-size: 18px; color: #4a4a4a;">Processing audio in {chunk_duration}-second chunks...</p>
-                <div style="border: 4px solid #f3f3f3; border-top: 4px solid #3498db; border-radius: 50%; width: 30px; height: 30px; animation: spin 2s linear infinite; margin: 0 auto;"></div>
-                <style>@keyframes spin {{ 0% {{ transform: rotate(0deg); }} 100% {{ transform: rotate(360deg); }} }}</style>
-            </div>
-        """)] + [gr.Group(visible=False)] * len(group_components) + [None] * (len(output_containers) * 5) + [None, None]
-        
-        results = get_predictions(audio_input, generate_audio, chunk_duration)
-        
-        # Initialize outputs list
-        outputs = []
-        group_visibility = []
-        all_images = []  # Collect all generated images for the fade animation
-        
-        # Process each result
-        for i, result in enumerate(results):
-            if i < len(output_containers):
-                group_visibility.append(gr.Group(visible=True))
-                outputs.extend([
-                    result['emotion'],
-                    result['transcription'],
-                    result['sentiment'],
-                    result['image'],
-                    result['music']
-                ])
-                # Collect the image for the fade animation
-                all_images.append(result['image'])
-            else:
-                # If we have more results than containers, just extend with None
-                group_visibility.append(gr.Group(visible=False))
-                outputs.extend([None] * 5)
-        
-        # Hide remaining containers
-        for i in range(len(results), len(output_containers)):
-            group_visibility.append(gr.Group(visible=False))
-            outputs.extend([None] * 5)
-        
-        # Create fade animation if we have multiple images
-        fade_preview = None
-        fade_animation = None
-        if len(all_images) > 1:
-            # Create a fade animation (GIF)
-            fade_preview, fade_animation = create_fade_transition(all_images, fade_duration=1.5, fps=15)
-        
-        # Hide loading indicator and show results
-        yield [gr.HTML("")] + group_visibility + outputs + [fade_preview, fade_animation]
+    
     
     # Function to handle example selection
     def load_example(example_name):