Create app.py

app.py

@@ -0,0 +1,57 @@
+import gradio as gr
+import torch
+from diffusers import StableDiffusionPipeline
+import os
+
+# Set the model ID
+MODEL_ID = "runwayml/stable-diffusion-v1-5"
+
+# Detect hardware
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+# Use float16 for faster, lighter inference if on GPU
+TORCH_DTYPE = torch.float16 if DEVICE == "cuda" else torch.float32
+
+# Load the pipeline
+print(f"Loading model on {DEVICE}...")
+pipe = StableDiffusionPipeline.from_pretrained(
+    MODEL_ID, 
+    torch_dtype=TORCH_DTYPE,
+    use_safetensors=True
+)
+pipe = pipe.to(DEVICE)
+
+def generate_image(prompt, negative_prompt):
+    if not prompt:
+        return None
+    
+    try:
+        # Generate the image
+        image = pipe(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            num_inference_steps=30,
+            guidance_scale=7.5
+        ).images[0]
+        return image
+    except Exception as e:
+        print(f"Error: {e}")
+        return None
+
+# Build the UI
+with gr.Blocks(theme=gr.themes.Base()) as demo:
+    gr.Markdown("# ✨ AI Image Generator")
+    gr.Markdown("Enter a prompt and click generate to create an image.")
+    
+    with gr.Row():
+        with gr.Column():
+            prompt = gr.Textbox(label="Prompt", placeholder="A majestic lion in the jungle...")
+            neg_prompt = gr.Textbox(label="Negative Prompt", placeholder="blurry, low quality, distorted")
+            btn = gr.Button("Generate", variant="primary")
+        
+        with gr.Column():
+            output = gr.Image(label="Result")
+
+    btn.click(fn=generate_image, inputs=[prompt, neg_prompt], outputs=output)
+
+if __name__ == "__main__":
+    demo.launch()