Update app.py

app.py

@@ -1,47 +1,61 @@
+
 import gradio as gr
-from diffusers import StableDiffusionPipeline
-import torch
+import requests
 from PIL import Image
+from io import BytesIO
 
-# Initialize the Stable Diffusion pipeline (optimized for CPU)
-pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float32)
-pipe = pipe.to("cpu")  # Move model to CPU
+# Hugging Face Inference API token (you need to get your own token from Hugging Face)
+API_TOKEN = "your_huggingface_api_token_here"
+API_URL = "https://api-inference.huggingface.co/models/stabilityai/stable-diffusion-2"
 
-# Function to split the story into key scenes
-def split_story_into_scenes(story):
-    # Split the story into sentences (scenes) for generating images
-    scenes = story.split(". ")  # Splitting based on period and space
-    return [scene for scene in scenes if scene]  # Remove empty scenes
+headers = {"Authorization": f"Bearer {API_TOKEN}"}
 
-# Function to generate images for each scene using Stable Diffusion
+# Function to split story into key scenes
+def split_story_into_scenes(story):
+    scenes = story.strip().split(". ")
+    return [scene.strip() for scene in scenes if scene.strip()]  # Return non-empty, stripped scenes
+
+# Function to generate images using the Hugging Face Inference API
+def generate_image_from_prompt(prompt):
+    payload = {"inputs": prompt}
+    response = requests.post(API_URL, headers=headers, json=payload)
+    if response.status_code == 200:
+        image = Image.open(BytesIO(response.content))
+        return image
+    else:
+        print(f"Error {response.status_code}: {response.content}")
+        return None
+
+# Function to generate images for all scenes
 def generate_images(scenes):
     images = []
     for scene in scenes:
-        # Generate image for each scene (text prompt)
         print(f"Generating image for scene: {scene}")
-        image = pipe(scene).images[0]  # Get the first generated image
-        images.append(image)
+        image = generate_image_from_prompt(scene)
+        if image:
+            images.append(image)
     return images
 
 # Function to create the comic-strip layout
 def generate_comic_strip(story):
-    scenes = split_story_into_scenes(story)  # Split the story into scenes
-    images = generate_images(scenes)  # Generate images for each scene
-    return images  # Return the images for display in the gallery
+    scenes = split_story_into_scenes(story)
+    images = generate_images(scenes)
+    return images
 
 # Gradio Interface
 description = "GenArt Narrative: Turn your story descriptions into comic-strip visuals!"
 
 interface = gr.Interface(
-    fn=generate_comic_strip,  # Function to run
-    inputs=gr.Textbox(lines=5, placeholder="Enter your short story here..."),  # Input: user story
-    outputs=gr.Gallery(label="Generated Comic Strip"),  # Output: gallery of images
+    fn=generate_comic_strip,
+    inputs=gr.Textbox(lines=5, placeholder="Enter your short story here..."),
+    outputs=gr.Gallery(label="Comic Strip"),
     title="GenArt Narrative",
     description=description,
-    examples=[["A brave knight sets out on a quest to rescue a princess from a dragon."]]  # Example story
+    examples=[
+        ["Once upon a time, a dragon guarded a secret treasure in a mystical land."]
+    ],
 )
 
-# Launch the Gradio app
+# Launch the app
 if __name__ == "__main__":
     interface.launch()
-