Update app.py

app.py

@@ -1,9 +1,6 @@
 import os
-import gradio as gr
-import numpy as np
-import random
 from diffusers import DiffusionPipeline
-import torch
+import gradio as gr
 
 # Retrieve the Hugging Face token from the environment variable
 hf_token = os.getenv("HUGGINGFACE_TOKEN")
@@ -12,147 +9,19 @@ hf_token = os.getenv("HUGGINGFACE_TOKEN")
 if hf_token is None:
     raise EnvironmentError("Hugging Face token not found in environment variables. Make sure it's correctly set.")
 
-# Set device to CPU or GPU based on availability
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
 # Load the model
-if torch.cuda.is_available():
-    torch.cuda.max_memory_allocated(device=device)
+try:
     pipe = DiffusionPipeline.from_pretrained(
-        "Maryamm/Lora_finetune_mnist", 
-        torch_dtype=torch.float16, 
-        variant="fp16", 
-        use_safetensors=True,
-        use_auth_token=hf_token  # Use token to authenticate
+        "Maryamm/Lora_finetune_mnist",  # Your private model's path
+        use_auth_token=hf_token,
+        force_download=True  # Ensure it downloads the model
     )
-    pipe.enable_xformers_memory_efficient_attention()
-    pipe = pipe.to(device)
-else: 
-    pipe = DiffusionPipeline.from_pretrained(
-        "Maryamm/Lora_finetune_mnist", 
-        use_safetensors=True,
-        use_auth_token=hf_token  # Use token to authenticate
-    )
-    pipe = pipe.to(device)
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 512
+    pipe = pipe.to("cpu")  # Ensure it's running on CPU
+except Exception as e:
+    raise RuntimeError(f"Failed to load model: {e}")
 
-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(device=device).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] 
-    
+def infer(prompt):
+    image = pipe(prompt).images[0]
     return image
 
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
-]
-
-css = """
-#col-container {
-    margin: 0 auto;
-    max-width: 520px;
-}
-"""
-
-power_device = "GPU" if torch.cuda.is_available() else "CPU"
-
-with gr.Blocks(css=css) as demo:
-    
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(f"""
-        # Text-to-Image Gradio Template
-        Currently running on {power_device}.
-        """)
-        
-        with gr.Row():
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
-            
-            run_button = gr.Button("Run", scale=0)
-        
-        result = gr.Image(label="Result", show_label=False)
-
-        with gr.Accordion("Advanced Settings", open=False):
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
-            )
-            
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-            )
-            
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-            
-            with gr.Row():
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=512,
-                )
-                
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=512,
-                )
-            
-            with gr.Row():
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,
-                )
-                
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=12,
-                    step=1,
-                    value=2,
-                )
-        
-        gr.Examples(
-            examples=examples,
-            inputs=[prompt]
-        )
-
-    run_button.click(
-        fn=infer,
-        inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
-        outputs=[result]
-    )
-
-demo.queue().launch()
+gr.Interface(fn=infer, inputs="text", outputs="image").launch()