Update app.py

app.py

@@ -1,142 +1,50 @@
-import gradio as gr
-import numpy as np
-import random
-#import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
-import torch
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo" #Replace to the model you would like to use
-
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
-
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
-#@spaces.GPU #[uncomment to use ZeroGPU]
-def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, progress=gr.Progress(track_tqdm=True)):
+## import gradio as gr
+#
+## def greet(name):
+##     return "Hello " + name + "!!"
+#
+## iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+## iface.launch()
 
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-        
-    generator = torch.Generator().manual_seed(seed)
-    
-    image = pipe(
-        prompt = prompt, 
-        negative_prompt = negative_prompt,
-        guidance_scale = guidance_scale, 
-        num_inference_steps = num_inference_steps, 
-        width = width, 
-        height = height,
-        generator = generator
-    ).images[0] 
-    
-    return image, seed
-
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
-]
-
-css="""
-#col-container {
-    margin: 0 auto;
-    max-width: 640px;
-}
-"""
-
-with gr.Blocks(css=css) as demo:
-    
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(f"""
-        # Text-to-Image Gradio Template
-        """)
-        
-        with gr.Row():
-            
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
-            
-            run_button = gr.Button("Run", scale=0)
-        
-        result = gr.Image(label="Result", show_label=False)
-
-        with gr.Accordion("Advanced Settings", open=False):
-            
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
-            )
-            
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-            )
-            
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-            
-            with gr.Row():
-                
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024, #Replace with defaults that work for your model
-                )
-                
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024, #Replace with defaults that work for your model
-                )
-            
-            with gr.Row():
-                
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0, #Replace with defaults that work for your model
-                )
-                
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=2, #Replace with defaults that work for your model
-                )
-        
-        gr.Examples(
-            examples = examples,
-            inputs = [prompt]
-        )
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn = infer,
-        inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
-        outputs = [result, seed]
-    )
-
-demo.queue().launch()
+import gradio as gr
+from fastai.vision.all import *
+import skimage
+import pathlib
+temp = pathlib.PosixPath
+pathlib.PosixPath = pathlib.WindowsPath
+pathlib.PosixPath = temp
+
+learn = load_learner('model.pkl')
+labels = learn.dls.vocab
+def predict(img):
+   img = PILImage.create(img)
+   pred,pred_idx,probs = learn.predict(img)
+   return {labels[i]: float(probs[i]) for i in range(len(labels))}
+
+title = "Female/Male Classifier"
+description = "A Female/Male classifier trained on the duckduckgo search result with fastai. Created as a demo for Gradio and HuggingFace Spaces."
+## article="<p style='text-align: center'><a href='https://tmabraham.github.io/blog/gradio_hf_spaces_tutorial' target='_blank'>Blog post</a></p>"
+examples = ['femaleDefault.jpg', 'maleDefault.jpg',
+       'dragQueen1.jpg', 'dragQueen2.jpg',
+       'femaleAngry1.jpg', 'femaleAngry2.jpg',
+       'femaleMuscle1.jpg', 'femaleMuscle2.jpg',
+       'maleAsian.jpg', 'maleEurope.jpg',
+       'femaleAsian.jpg', 'femaleDefault.jpg',
+       'maleCrying2.jpg', 'maleCrying2No.jpg']
+#interpretation='default'
+enable_queue=True
+#
+## gr.Interface(fn=predict,inputs=gr.inputs.Image(shape=(512, 512)),outputs=gr.outputs.Label(),title=title,description=description,article=article,examples=examples,interpretation=interpretation,enable_queue=enable_queue).launch()
+#
+inter = gr.Interface(
+   fn=predict,
+   inputs=gr.inputs.Image(shape=(512, 512)),
+   outputs=gr.outputs.Label(),
+   title=title,
+   description=description,
+   examples=examples,
+   cache_examples=True,
+   examples_per_page=2)
+
+inter.queue()
+inter.launch()