Update app.py

app.py

@@ -3,7 +3,6 @@ import os
 from lumaai import AsyncLumaAI
 import asyncio
 import aiohttp
-import tempfile
 
 async def generate_video(api_key, prompt, loop=False, aspect_ratio="16:9", progress=gr.Progress()):
     client = AsyncLumaAI(auth_token=api_key)
@@ -61,27 +60,14 @@ async def text_to_video(api_key, prompt, loop, aspect_ratio, progress=gr.Progres
     except Exception as e:
         return None, f"An error occurred: {str(e)}"
 
-async def image_to_video(api_key, prompt, image, loop, aspect_ratio, progress=gr.Progress()):
+async def image_to_video(api_key, prompt, image_url, loop, aspect_ratio, progress=gr.Progress()):
     if not api_key:
         raise gr.Error("Please enter your Luma AI API key.")
     
-    if image is None:
-        raise gr.Error("Please upload an image.")
-    
     try:
         client = AsyncLumaAI(auth_token=api_key)
         
-        progress(0, desc="Uploading image...")
-        
-        # Create a temporary file to store the uploaded image
-        with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as temp_file:
-            temp_file.write(image)
-            temp_file_path = temp_file.name
-        
-        # Upload the image to Luma AI (you might need to implement this function)
-        image_url = await upload_image_to_luma(client, temp_file_path)
-        
-        progress(0.1, desc="Initiating video generation from image...")
+        progress(0, desc="Initiating video generation from image...")
         generation = await client.generations.create(
             prompt=prompt,
             loop=loop,
@@ -94,7 +80,7 @@ async def image_to_video(api_key, prompt, image, loop, aspect_ratio, progress=gr
             }
         )
         
-        progress(0.2, desc="Video generation started. Waiting for completion...")
+        progress(0.1, desc="Video generation started. Waiting for completion...")
         
         # Poll for completion
         start_time = asyncio.get_event_loop().time()
@@ -107,7 +93,7 @@ async def image_to_video(api_key, prompt, image, loop, aspect_ratio, progress=gr
             
             # Update progress based on time elapsed (assuming 60 seconds total)
             elapsed_time = asyncio.get_event_loop().time() - start_time
-            progress_value = min(0.2 + (elapsed_time / 60) * 0.7, 0.9)
+            progress_value = min(0.1 + (elapsed_time / 60) * 0.8, 0.9)
             progress(progress_value, desc="Generating video...")
             
             await asyncio.sleep(5)
@@ -127,20 +113,11 @@ async def image_to_video(api_key, prompt, image, loop, aspect_ratio, progress=gr
                                 break
                             fd.write(chunk)
         
-        # Clean up the temporary file
-        os.unlink(temp_file_path)
-        
         progress(1.0, desc="Video generation complete!")
         return file_name, ""
     except Exception as e:
         return None, f"An error occurred: {str(e)}"
 
-# You need to implement this function based on Luma AI's API for image uploading
-async def upload_image_to_luma(client, image_path):
-    # This is a placeholder. You need to implement the actual image upload logic
-    # using the Luma AI API. The function should return the URL of the uploaded image.
-    raise NotImplementedError("Image upload to Luma AI is not implemented yet.")
-
 with gr.Blocks() as demo:
     gr.Markdown("# Luma AI Text-to-Video Demo")
     
@@ -164,7 +141,7 @@ with gr.Blocks() as demo:
     
     with gr.Tab("Image to Video"):
         img_prompt = gr.Textbox(label="Prompt")
-        img_input = gr.Image(label="Upload Image", type="numpy")
+        img_url = gr.Textbox(label="Image URL")
         img_generate_btn = gr.Button("Generate Video from Image")
         img_video_output = gr.Video(label="Generated Video")
         img_error_output = gr.Textbox(label="Error Messages", visible=True)
@@ -175,7 +152,7 @@ with gr.Blocks() as demo:
         
         img_generate_btn.click(
             image_to_video,
-            inputs=[api_key, img_prompt, img_input, img_loop, img_aspect_ratio],
+            inputs=[api_key, img_prompt, img_url, img_loop, img_aspect_ratio],
             outputs=[img_video_output, img_error_output]
         )