image

app.py

@@ -1,13 +1,59 @@
 import gradio as gr
+import numpy as np
+import random
+from diffusers import DiffusionPipeline
+import torch
 from huggingface_hub import InferenceClient
 import requests
 from PIL import Image
 from io import BytesIO
 
-# Initialize the client
+# Device configuration
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# Load the diffusion pipeline
+if torch.cuda.is_available():
+    torch.cuda.max_memory_allocated(device=device)
+    pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
+    pipe.enable_xformers_memory_efficient_attention()
+    pipe = pipe.to(device)
+else: 
+    pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", use_safetensors=True)
+    pipe = pipe.to(device)
+
+MAX_SEED = np.iinfo(np.int32).max
+MAX_IMAGE_SIZE = 1024
+
+# Inference function for generating images
+def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+        
+    generator = torch.Generator().manual_seed(seed)
+    
+    image = pipe(
+        prompt=prompt, 
+        negative_prompt=negative_prompt,
+        guidance_scale=guidance_scale, 
+        num_inference_steps=num_inference_steps, 
+        width=width, 
+        height=height,
+        generator=generator
+    ).images[0] 
+    
+    return image
+
+# Examples for the text-to-image generation
+examples = [
+    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
+    "An astronaut riding a green horse",
+    "A delicious ceviche cheesecake slice",
+]
+
+# Initialize the InferenceClient
 client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
-# Define the function to respond to user inputs
+# Respond function for the chatbot
 def respond(message, history, system_message, max_tokens, temperature, top_p):
     messages = [{"role": "system", "content": system_message}]
     
@@ -28,7 +74,7 @@ def respond(message, history, system_message, max_tokens, temperature, top_p):
     
     return response.choices[0].message['content']
 
-# Define the function to generate posts
+# Function to generate posts
 def generate_post(prompt, max_tokens, temperature, top_p):
     response = client.chat_completion(
         [{"role": "user", "content": prompt}],
@@ -38,21 +84,20 @@ def generate_post(prompt, max_tokens, temperature, top_p):
     )
     return response.choices[0].message['content']
 
-# Define the function to moderate posts
+# Function to moderate posts
 def moderate_post(post):
     # Implement your post moderation logic here
     if "inappropriate" in post:
         return "Post does not adhere to community guidelines."
     return "Post adheres to community guidelines."
 
-# Define the function to generate images
+# Function to generate images using the diffusion pipeline
 def generate_image(prompt):
-    # Replace with actual model or API endpoint for image generation
-    response = client.text_to_image(prompt)
-    image = Image.open(BytesIO(response))
+    generator = torch.manual_seed(random.randint(0, MAX_SEED))
+    image = pipe(prompt=prompt, generator=generator).images[0]
     return image
 
-# Define the function to moderate images
+# Function to moderate images
 def moderate_image(image):
     # Convert the PIL image to a format that can be sent for moderation
     buffered = BytesIO()
@@ -73,10 +118,21 @@ def moderate_image(image):
         return "Image does not adhere to community guidelines."
 
 # Create the Gradio interface
-demo = gr.Blocks()
+css = """
+#col-container {
+    margin: 0 auto;
+    max-width: 520px;
+}
+"""
+
+if torch.cuda.is_available():
+    power_device = "GPU"
+else:
+    power_device = "CPU"
 
-with demo:
+with gr.Blocks(css=css) as demo:
     gr.Markdown("# AI-driven Content Generation and Moderation Bot")
+    gr.Markdown(f"Currently running on {power_device}.")
 
     with gr.Tabs():
         with gr.TabItem("Chat"):
@@ -131,4 +187,4 @@ with demo:
             moderate_image_button.click(moderate_image, uploaded_image, image_moderation_result)
 
 if __name__ == "__main__": 
-    demo.launch()
+    demo.launch()