Create app.py

app.py

@@ -0,0 +1,56 @@
+import gradio as gr
+import torch
+from diffusers import StableDiffusionPipeline
+
+# Load model
+print("Loading model...")
+pipe = StableDiffusionPipeline.from_pretrained(
+    "runwayml/stable-diffusion-v1-5",
+    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
+    safety_checker=None
+)
+
+# Load YOUR LoRA weights
+pipe.load_lora_weights("ozzyzoz123/indian-clothing-lora")
+
+# Move to GPU if available
+device = "cuda" if torch.cuda.is_available() else "cpu"
+pipe = pipe.to(device)
+print(f"Model loaded on {device}")
+
+# Generation function
+def generate(prompt, negative_prompt, steps, guidance_scale):
+    image = pipe(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        num_inference_steps=int(steps),
+        guidance_scale=guidance_scale
+    ).images[0]
+    return image
+
+# Gradio Interface
+demo = gr.Interface(
+    fn=generate,
+    inputs=[
+        gr.Textbox(
+            label="Prompt",
+            value="product photo of Indian Saree, black background, no person, studio shot"
+        ),
+        gr.Textbox(
+            label="Negative Prompt",
+            value="human face, person, human body, skin texture, portrait"
+        ),
+        gr.Slider(10, 50, value=30, step=1, label="Steps"),
+        gr.Slider(1, 15, value=7.5, step=0.5, label="Guidance Scale"),
+    ],
+    outputs=gr.Image(label="Generated Image"),
+    title="🇮🇳 Indian Clothing Generator",
+    description="Generate product photos of Indian clothing (Saree, Kurta, Shirt, Jacket, T-shirt)",
+    examples=[
+        ["product photo of Indian Saree, black background, no person", "human face, person", 30, 7.5],
+        ["product photo of Indian Kurta, black background, no person", "human face, person", 30, 7.5],
+        ["product photo of Indian Jacket, black background, no person", "human face, person", 30, 7.5],
+    ]
+)
+
+demo.launch()