Update app.py

app.py

@@ -1,53 +1,18 @@
 import gradio as gr
-import spaces
-import torch
-import os
-from compel import Compel, ReturnedEmbeddingsType
-from diffusers import DiffusionPipeline
+from transformers import pipeline
 
+pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
 
-model_name = os.environ.get('MODEL_NAME', 'UnfilteredAI/NSFW-gen-v2')
-pipe = DiffusionPipeline.from_pretrained(
-    model_name,
-    torch_dtype=torch.float16
-)
-pipe.to('cuda')
+def predict(input_img):
+    predictions = pipeline(input_img)
+    return input_img, {p["label"]: p["score"] for p in predictions} 
 
-compel = Compel(
-  tokenizer=[pipe.tokenizer, pipe.tokenizer_2] ,
-  text_encoder=[pipe.text_encoder, pipe.text_encoder_2],
-  returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED,
-  requires_pooled=[False, True]
+gradio_app = gr.Interface(
+    predict,
+    inputs=gr.Image(label="Select hot dog candidate", sources=['upload', 'webcam'], type="pil"),
+    outputs=[gr.Image(label="Processed Image"), gr.Label(label="Result", num_top_classes=2)],
+    title="Hot Dog? Or Not?",
 )
 
-
-@spaces.GPU(duration=120)
-def generate(prompt, negative_prompt, num_inference_steps, guidance_scale, width, height, num_samples):
-    embeds, pooled = compel(prompt)
-    neg_embeds, neg_pooled = compel(negative_prompt)
-    return pipe(
-        prompt_embeds=embeds,
-        pooled_prompt_embeds=pooled,
-        negative_prompt_embeds=neg_embeds,
-        negative_pooled_prompt_embeds=neg_pooled,
-        num_inference_steps=num_inference_steps,
-        guidance_scale=guidance_scale,
-        width=width,
-        height=height,
-        num_images_per_prompt=num_samples
-    ).images
-
-
-gr.Interface(
-    fn=generate,
-    inputs=[
-        gr.Text(label="Prompt"),
-        gr.Text("", label="Negative Prompt"),
-        gr.Number(7, label="Number inference steps"),
-        gr.Number(3, label="Guidance scale"),
-        gr.Number(512, label="Width"),
-        gr.Number(512, label="Height"),
-        gr.Number(1, label="# images"),
-    ],
-    outputs=gr.Gallery(),
-).launch()
+if __name__ == "__main__":
+    gradio_app.launch()