Update app.py

app.py

@@ -1,25 +1,21 @@
 import gradio as gr
-import torch
-from diffusers import AutoPipelineForText2Image
+import replicate
 
-# Load pre-trained model and move it to GPU
-pipe = AutoPipelineForText2Image.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float32, variant="fp16")
-pipe.to("cpu")
+# Set the replicate API token
+import os
+os.environ["REPLICATE_API_TOKEN"] = "r8_JSR8xlRoCk6cmq3qEOOThVTn3dAgdPq1bWXdj"
 
-# Define function to generate image based on input prompt
-def generate_image(prompt):
-    image = pipe(prompt=prompt, num_inference_steps=1, guidance_scale=0.0).images[0]
-    return image
+def generate_output(prompt):
+    output = replicate.run(
+        "fofr/sdxl-turbo:6244ebc4d96ffcc48fa1270d22a1f014addf79c41732fe205fb1ff638c409267",
+        input={"prompt": prompt}
+    )
+    return output
 
-# Create Gradio interface
-input_text = gr.Textbox(lines=5, label="Enter the prompt")
-output_image = gr.Image(label="Generated Image")
+iface = gr.Interface(
+    fn=generate_output,
+    inputs=gr.inputs.Textbox(label="Input Prompt"),
+    outputs=gr.outputs.Textbox(label="Model Output")
+)
 
-# Define Gradio app
-gr.Interface(
-    generate_image, 
-    inputs=input_text, 
-    outputs=output_image,
-    title="Stable Diffusion SDXL Turbo",
-    description="Enter a prompt and generate an image based on the input prompt"
-).launch()
+iface.launch(share=True)