Update app.py

app.py

@@ -1,19 +1,21 @@
-from transformers import pipeline
+from diffusers import StableDiffusionXLPipeline
 import gradio as gr
 import torch
 
-def textSummarizer(ARTICLE,MaxTokens,MinTokens):
-  summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
-  result = summarizer(ARTICLE, max_length=MaxTokens, min_length=MinTokens, do_sample=False)
-  return result[0]["summary_text"]
+def segMindImage(prompt, negative_prompt):
+  pipe = StableDiffusionXLPipeline.from_pretrained("segmind/SSD-1B", torch_dtype=torch.float16, use_safetensors=True, variant="fp16")
+  pipe.to("cuda")
+  prompt = prompt # Your prompt here
+  neg_prompt = negative_prompt # Negative prompt here
+  image = pipe(prompt=prompt, negative_prompt=neg_prompt).images[0]
 
+  return image
 
-app = gr.Interface(textSummarizer,
-                   inputs = [gr.Textbox(),
-                             gr.Slider(minimum=0,maximum=500,step=2),
-                             gr.Slider(minimum=0,maximum=100,step=1)],
-                   outputs = gr.Textbox(),
-                   title = "Article Summarizer",
-                   theme=gr.themes.Soft())
+  app = gr.Interface(segMindImage,
+                   inputs = [gr.Text(label="Prompt",placeholder="Write Prompt"),gr.Text(label="Negative Prompt",placeholder="Write Negative Prompt")],
+                   outputs = gr.Image(label="Image"),
+                   css =".gradio-container {background-image: linear-gradient(#7F7FD5, #91EAE4, #A3C9E2)}",
+                   theme = gr.themes.Soft(),
+                   title = "SD Image Diffusion")
 
-app.launch()
+  app.launch()