Update app.py

app.py

@@ -1,63 +1,12 @@
 import gradio as gr
-from transformers import AutoProcessor, AutoModelForImageGeneration
-import torch
-import random
-
-# Check if GPU is available
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-# Load the model and processor (replace with your actual model)
-def load_model():
-    model_name = "robiai/picasoe"  # Replace with the actual model name
-    processor = AutoProcessor.from_pretrained(model_name)
-    model = AutoModelForImageGeneration.from_pretrained(model_name).to(device)
-    return processor, model
-
-processor, model = load_model()
 
 def generate_image(prompt, image_size="Default", num_inference_steps=28, seed="random", guidance_scale=3.5, sync_mode=True, num_images=1):
-    try:
-        # Set seed for reproducibility
-        if seed == "random":
-            seed_value = random.randint(0, 2**32 - 1)
-        else:
-            seed_value = int(seed)
-        torch.manual_seed(seed_value)
-        
-        # Map image size to dimensions (customize as needed)
-        size_mapping = {
-            "Default": (512, 512),
-            "Square": (512, 512),
-            "Square HD": (1024, 1024),
-            "Portrait 3:4": (768, 1024),
-            "Portrait 9:16": (576, 1024),
-            "Landscape 4:3": (1024, 768),
-            "Landscape 16:9": (1024, 576),
-            "Custom": (512, 512)  # Default for custom
-        }
-        width, height = size_mapping[image_size]
-        
-        # Prepare inputs for the model
-        inputs = processor(text=prompt, return_tensors="pt").to(device)
-        
-        # Generate images
-        with torch.no_grad():
-            outputs = model.generate(
-                **inputs,
-                num_inference_steps=num_inference_steps,
-                guidance_scale=guidance_scale,
-                num_images_per_prompt=num_images,
-                output_type="pil"
-            )
-        
-        # Return the first generated image (or handle multiple images as needed)
-        return outputs[0] if num_images == 1 else outputs
-    
-    except Exception as e:
-        print(f"Error during image generation: {e}")
-        return None
+    # Load the model (assuming it supports these parameters)
+    model = gr.load("models/robiai/picasoe", provider="hf-inference")
+    # Implement image generation logic here
+    # Return the generated image
+    pass
 
-# Gradio Interface
 with gr.Blocks(title="Picasoe") as demo:
     gr.Markdown("# Picasoe")
     gr.Markdown("Convert your ideas into jaw-dropping visuals.")