Update app.py

app.py

@@ -1,7 +1,6 @@
 import subprocess
 from pathlib import Path
 import einops
-import gradio as gr
 import numpy as np
 import torch
 from huggingface_hub import hf_hub_download
@@ -9,9 +8,11 @@ from PIL import Image
 from torch import nn
 from torchvision.utils import save_image
 from huggingface_hub.hf_api import HfApi
+import streamlit as st
 
 hfapi = HfApi()
 
+
 class Generator(nn.Module):
     def __init__(self, num_channels=4, latent_dim=100, hidden_size=64):
         super(Generator, self).__init__()
@@ -45,7 +46,7 @@ class Generator(nn.Module):
 
 
 @torch.no_grad()
-def interpolate(save_dir='./lerp/', frames=100, rows=8, cols=8):
+def interpolate(model, save_dir='./lerp/', frames=100, rows=8, cols=8):
     save_dir = Path(save_dir)
     save_dir.mkdir(exist_ok=True, parents=True)
 
@@ -80,7 +81,7 @@ def predict(model_name, choice, seed):
     torch.manual_seed(seed)
 
     if choice == 'interpolation':
-        interpolate()
+        interpolate(model)
         return 'out.gif'
     else:
         z = torch.randn(64, 100, 1, 1)
@@ -92,17 +93,117 @@ def predict(model_name, choice, seed):
         return 'image.png'
 
 
-models = [model.modelId[model.modelId.index("/") + 1:] for model in hfapi.list_models(author="huggingnft")]
-gr.Interface(
-    predict,
-    inputs=[
-        gr.inputs.Dropdown(models, label='Model'),
-        gr.inputs.Dropdown(['image', 'interpolation'], label='Output Type'),
-        gr.inputs.Slider(label='Seed', minimum=0, maximum=1000, default=42),
-    ],
-    outputs="image",
-    title="Cryptopunks GAN",
-    description="These CryptoPunks do not exist. You have the choice of either generating random punks, or a gif showing the interpolation between two random punk grids.",
-    article="<p style='text-align: center'><a href='https://arxiv.org/pdf/1511.06434.pdf'>Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks</a> | <a href='https://github.com/teddykoker/cryptopunks-gan'>Github Repo</a></p>",
-    examples=[["interpolation", 100], ["interpolation", 500], ["image", 100], ["image", 500]],
-).launch(cache_examples=True)
+model_names = [model.modelId[model.modelId.index("/") + 1:] for model in hfapi.list_models(author="huggingnft")]
+
+st.set_page_config(page_title="Hugging NFT")
+
+st.title("Hugging NFT")
+st.sidebar.markdown(
+    """
+<style>
+.aligncenter {
+    text-align: center;
+}
+</style>
+<p class="aligncenter">
+    <img src="https://raw.githubusercontent.com/AlekseyKorshuk/optimum-transformers/master/data/social_preview.png" width="300" />
+</p>
+""",
+    unsafe_allow_html=True,
+)
+st.sidebar.markdown(
+    """
+<style>
+.aligncenter {
+    text-align: center;
+}
+</style>
+
+<p style='text-align: center'>
+<a href="https://github.com/AlekseyKorshuk/huggingnft" target="_blank">GitHub</a>
+</p>
+
+<p class="aligncenter">
+    <a href="https://github.com/AlekseyKorshuk/huggingnft" target="_blank"> 
+        <img src="https://img.shields.io/github/stars/AlekseyKorshuk/huggingnft?style=social"/>
+    </a>
+</p>
+<p class="aligncenter">
+    <a href="https://twitter.com/alekseykorshuk" target="_blank"> 
+        <img src="https://img.shields.io/twitter/follow/alekseykorshuk?style=social"/>
+    </a>
+</p>
+    """,
+    unsafe_allow_html=True,
+)
+
+st.markdown(
+    "🤗 [Hugging NFT](https://github.com/AlekseyKorshuk/huggingnft) - Generate NFT by OpenSea collection name.")
+
+st.markdown(
+    "🚀️ SN-GAN used to train all models.")
+
+st.markdown(
+    "⁉️ Want to train your model? Check [project repository](https://github.com/AlekseyKorshuk/huggingnft) and make in in few clicks!")
+#
+# st.markdown("🚀 Up to 1ms on Bert-based transformers")
+#
+# st.markdown(
+#     "‼️ NOTE: This Space **does not show** the real power of this project because: low recources, not possbile to optimize models. Check [project repository](https://github.com/AlekseyKorshuk/optimum-transformers) with real bechmarks!")
+
+# st.sidebar.header("Settings:")
+model_name = st.selectbox(
+    'Choose model:',
+    model_names)
+
+output_type = st.selectbox(
+    'Output type:',
+    ['image', 'interpolation'])
+
+seed_value = st.slider("Seed:",
+                       min_value=1,
+                       max_value=1000,
+                       step=1,
+                       value=100,
+                       )
+
+model_html = """
+
+<div class="inline-flex flex-col" style="line-height: 1.5;">
+    <div class="flex">
+        <div
+\t\t\tstyle="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;USER_PROFILE&#39;)">
+        </div>
+    </div>
+    <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
+    <div style="text-align: center; font-size: 16px; font-weight: 800">USER_NAME</div>
+    <a href="https://genius.com/artists/USER_HANDLE">
+    \t<div style="text-align: center; font-size: 14px;">@USER_HANDLE</div>
+    </a>
+</div>
+"""
+
+if st.button("Run"):
+    with st.spinner(text=f"Generating..."):
+        st.image(predict(model_name, output_type, seed_value))
+    st.subheader("Please star project repository, this space and follow my Twitter:")
+    st.markdown(
+        """
+    <style>
+    .aligncenter {
+        text-align: center;
+    }
+    </style>
+    <p class="aligncenter">
+        <a href="https://github.com/AlekseyKorshuk/huggingnft" target="_blank">
+            <img src="https://img.shields.io/github/stars/AlekseyKorshuk/huggingnft?style=social"/>
+        </a>
+    </p>
+    <p class="aligncenter">
+        <a href="https://twitter.com/alekseykorshuk" target="_blank">
+            <img src="https://img.shields.io/twitter/follow/alekseykorshuk?style=social"/>
+        </a>
+    </p>
+        """,
+        unsafe_allow_html=True,
+    )