Create app.py

app.py

@@ -0,0 +1,34 @@
+import os
+import subprocess
+
+GIT_TOKEN = os.environ.get("GIT_TOKEN")
+
+lib_url = f"git+https://vasudevgupta7:{GIT_TOKEN}@github.com/vasudevgupta7/huggingface-task@main"
+subprocess.run(f"pip3 install -q {lib_url}".split())
+
+import torch
+import gradio as gr
+from pytorch_pretrained_biggan import BigGAN
+from transformers import AutoModel, AutoTokenizer
+from huggingface_task.autoencoder import AutoEncoder
+from huggingface_task.run_model import generate_image_from_text
+
+biggan_id = 'biggan-deep-128'
+text_encoder_id = "distilbert-base-uncased"
+autoencoder_id = "vasudevgupta/biggan-mapping-model"
+
+text_tokenizer = AutoTokenizer.from_pretrained(text_encoder_id)
+text_model = AutoModel.from_pretrained(text_encoder_id)
+
+autoencoder = AutoEncoder.from_pretrained(autoencoder_id)
+biggan = BigGAN.from_pretrained(biggan_id)
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+biggan.to(device).eval()
+text_model.to(device).eval()
+autoencoder.to(device).eval()
+
+def get_image(text_query):
+  return generate_image_from_text(text_query, text_tokenizer, text_model, autoencoder, biggan, device=device)
+
+gr.Interface(fn=get_image, inputs="text", outputs="image").launch()