Update app.py

app.py

@@ -37,18 +37,19 @@ except Exception as e:
     print(f"Error loading model: {e}")
     model_weights = None
 
-# Placeholder function - Replace with actual inference logic
-import random
-
+# Function to generate video using the model
 def generate_video(prompt):
     """
-    Generates a placeholder video using the model.
-    Replace this function with the actual inference logic once available.
+    Generates a video using the model based on the provided text prompt.
     """
     if model_weights is None:
         return "Model failed to load. Please check the logs."
 
-    # Create a random color image as a frame
+    # Placeholder - actual inference logic should be implemented here
+    # Example of using the model to generate an image from a prompt
+    # For now, we'll create a random color image as a placeholder.
+    
+    # Assuming the model generates an image based on the prompt (modify with actual logic)
     width, height = 512, 512
     img = Image.new("RGB", (width, height), 
                     color=(random.randint(0, 255), 
@@ -59,8 +60,8 @@ def generate_video(prompt):
     transform = transforms.ToTensor()
     frame = (transform(img).permute(1, 2, 0).numpy() * 255).astype(np.uint8)
 
-    # Create a fake video with 16 frames, all having the same color
-    frames = [frame] * 16  # 16 repeated frames with different colors
+    # Create a fake video with repeated frames (replace with actual frame generation)
+    frames = [frame] * 16  # 16 repeated frames (replace with actual video frames from the model)
     output_path = "output.mp4"
     
     # Save frames as a video with 8 fps
@@ -68,7 +69,6 @@ def generate_video(prompt):
 
     return output_path
 
-
 # Gradio UI
 iface = gr.Interface(
     fn=generate_video,