Update app.py

app.py

@@ -17,17 +17,16 @@ warnings.filterwarnings('ignore')
 # Initialize Gradio clients with public demo spaces
 def initialize_clients():
     try:
-        # Use a public Stable Diffusion demo space instead of SDXL
-        image_client = Client("gradio/stable-diffusion-2")
-        arxiv_client = Client("awacke1/Arxiv-Paper-Search-And-QA-RAG-Pattern")
-        return image_client, arxiv_client
+        # Use a simpler public demo space
+        image_client = Client("awacke1/Arxiv-Paper-Search-And-QA-RAG-Pattern")
+        return image_client
     except Exception as e:
         print(f"Error initializing clients: {str(e)}")
-        return None, None
+        return None
 
 if gr.NO_RELOAD:
-    # Initialize clients in NO_RELOAD block to prevent multiple initializations
-    IMAGE_CLIENT, ARXIV_CLIENT = initialize_clients()
+    # Initialize client in NO_RELOAD block to prevent multiple initializations
+    CLIENT = initialize_clients()
 
 STORY_GENRES = [
     "Science Fiction",
@@ -73,10 +72,10 @@ def generate_story_prompt(base_prompt, genre, structure):
 def generate_story(prompt, model_choice):
     """Generate story using specified model"""
     try:
-        if ARXIV_CLIENT is None:
+        if CLIENT is None:
             return "Error: Story generation service is not available."
         
-        result = ARXIV_CLIENT.predict(
+        result = CLIENT.predict(
             prompt,
             model_choice,
             True,
@@ -86,39 +85,6 @@ def generate_story(prompt, model_choice):
     except Exception as e:
         return f"Error generating story: {str(e)}"
 
-def generate_image_from_text(text_prompt):
-    """Generate an image from text description"""
-    try:
-        if IMAGE_CLIENT is None:
-            return None
-            
-        result = IMAGE_CLIENT.predict(
-            text_prompt,
-            api_name="/predict"  # Updated API endpoint for the public demo
-        )
-        return result
-    except Exception as e:
-        print(f"Error generating image: {str(e)}")
-        return None
-
-def create_video_from_images(image_paths, durations):
-    """Create video from a series of images"""
-    try:
-        if not image_paths:
-            return None
-            
-        clips = [ImageClip(img_path).set_duration(dur) for img_path, dur in zip(image_paths, durations) if os.path.exists(img_path)]
-        if not clips:
-            return None
-            
-        final_clip = concatenate_videoclips(clips, method="compose")
-        output_path = tempfile.mktemp(suffix=".mp4")
-        final_clip.write_videofile(output_path, fps=24)
-        return output_path
-    except Exception as e:
-        print(f"Error creating video: {str(e)}")
-        return None
-
 def process_story(story_text, num_scenes=5):
     """Break story into scenes for visualization"""
     if not story_text:
@@ -146,40 +112,18 @@ def story_generator_interface(prompt, genre, structure, model_choice, num_scenes
         if story.startswith("Error"):
             return story, None, None, None
         
-        # Process story into scenes
-        scenes = process_story(story, num_scenes)
-        
-        # Generate images for each scene
-        image_paths = []
-        for scene in scenes:
-            image = generate_image_from_text(scene)
-            if image is not None:
-                if isinstance(image, (str, bytes)):
-                    image_paths.append(image)
-                else:
-                    temp_path = tempfile.mktemp(suffix=".png")
-                    Image.fromarray(image).save(temp_path)
-                    image_paths.append(temp_path)
-        
         # Generate speech
         audio_path = asyncio.run(generate_speech(story))
         
-        # Create video if we have images
-        if image_paths:
-            scene_durations = [5.0] * len(image_paths)  # 5 seconds per scene
-            video_path = create_video_from_images(image_paths, scene_durations)
-        else:
-            video_path = None
-        
-        return story, image_paths, audio_path, video_path
+        return story, None, audio_path, None
         
     except Exception as e:
         error_msg = f"An error occurred: {str(e)}"
         return error_msg, None, None, None
 
 # Create Gradio interface
-with gr.Blocks(title="AI Story Generator & Visualizer") as demo:
-    gr.Markdown("# 🎭 AI Story Generator & Visualizer")
+with gr.Blocks(title="AI Story Generator") as demo:
+    gr.Markdown("# 🎭 AI Story Generator")
     
     with gr.Row():
         with gr.Column():
@@ -217,26 +161,23 @@ with gr.Blocks(title="AI Story Generator & Visualizer") as demo:
                 value=50,
                 step=10
             )
-            generate_btn = gr.Button("Generate Story & Media")
+            generate_btn = gr.Button("Generate Story")
     
     with gr.Row():
         with gr.Column():
             story_output = gr.Textbox(
                 label="Generated Story",
                 lines=10,
-                readonly=True
+                interactive=False  # Changed from readonly to interactive=False
             )
-        with gr.Column():
-            gallery = gr.Gallery(label="Scene Visualizations")
     
     with gr.Row():
         audio_output = gr.Audio(label="Story Narration")
-        video_output = gr.Video(label="Story Video")
     
     generate_btn.click(
         fn=story_generator_interface,
         inputs=[prompt_input, genre_input, structure_input, model_choice, num_scenes, words_per_scene],
-        outputs=[story_output, gallery, audio_output, video_output]
+        outputs=[story_output, None, audio_output, None]  # Set image and video outputs to None for now
     )
 
 if __name__ == "__main__":