Delete app.py

app.py

@@ -1,64 +0,0 @@
-from tensorflow import keras
-
-keras.mixed_precision.set_global_policy("mixed_float16")
-
-import time
-
-import gradio as gr
-import keras_cv
-
-from constants import css, examples, img_height, img_width, num_images_to_gen
-from share_btn import community_icon_html, loading_icon_html, share_js
-
-# Load model.
-weights_path = keras.utils.get_file(
-    origin="https://huggingface.co/mayve/GP/blob/main/%D9%86%D8%B3%D8%AE%D8%A9%20%D9%85%D9%86%20ckpt_epoch_96.h5",
-    file_hash="4b4348297aa9853ff9dc4da7f52dcb240210564400f164e5155e5f4dc1866626"
-)
-pokemon_model = keras_cv.models.StableDiffusion(
-    img_width=img_width, img_height=img_height
-)
-pokemon_model.diffusion_model.load_weights(weights_path)
-
-pokemon_model.diffusion_model.compile(jit_compile=True)
-pokemon_model.decoder.compile(jit_compile=True)
-pokemon_model.text_encoder.compile(jit_compile=True)
-
-# Warm-up the model.
-#_ = pokemon_model.text_to_image("Teddy bear", batch_size=num_images_to_gen)
-
-
-def generate_image_fn(prompt: str, unconditional_guidance_scale: int) -> list:
-    start_time = time.time()
-    # `images is an `np.ndarray`. So we convert it to a list of ndarrays.
-    # Each ndarray represents a generated image.
-    # Reference: https://gradio.app/docs/#gallery
-    images = pokemon_model.text_to_image(
-        prompt,
-        batch_size=num_images_to_gen,
-        unconditional_guidance_scale=unconditional_guidance_scale,
-    )
-    end_time = time.time()
-    print(f"Time taken: {end_time - start_time} seconds.")
-    return [image for image in images]
-
-
-description = "This Space demonstrates a fine-tuned Stable Diffusion model. You can use it for generating custom pokemons. To get started, either enter a prompt and pick one from the examples below. For details on the fine-tuning procedure, refer to [this repository](https://github.com/sayakpaul/stable-diffusion-keras-ft/)."
-article = "This Space leverages a T4 GPU to run the predictions. We use mixed-precision to speed up the inference latency. We further use XLA to carve out maximum performance from TensorFlow."
-gr.Interface(
-    generate_image_fn,
-    inputs=[
-        gr.Textbox(
-            label="Enter your prompt",
-            max_lines=1,
-            placeholder="cute Sundar Pichai creature",
-        ),
-        gr.Slider(value=40, minimum=8, maximum=50, step=1),
-    ],
-    outputs=[gr.outputs.Image(type="pil"),gr.outputs.Image(type="pil")],
-    title="Generate custom pokemons",
-    description=description,
-    article=article,
-    examples=[["cute Sundar Pichai creature", 40], ["Hello kitty", 40]],
-    allow_flagging=False,
-).launch()