Create app.py

app.py

@@ -0,0 +1,40 @@
+import torch
+from rudalle import get_tokenizer, get_vae
+from rudalle.utils import seed_everything
+
+import sys
+from rudolph.model.utils import get_i2t_attention_mask, get_t2t_attention_mask
+from rudolph.model import get_rudolph_model, ruDolphModel, FP16Module
+from rudolph.pipelines import generate_codebooks, self_reranking_by_image, self_reranking_by_text, show, generate_captions, generate_texts from rudolph.pipelines import  zs_clf
+import gradio as gr
+from rudolph import utils
+device = 'cuda'
+
+model = get_rudolph_model('350M',  fp16=True, device='cuda')
+tokenizer = get_tokenizer()
+vae = get_vae(dwt=False).to(device)
+
+
+
+
+
+
+# Download human-readable labels for ImageNet.
+
+
+
+def classify_image(inp):
+  print(type(inp))
+  inp =  Image.fromarray(inp)
+  texts = generate_captions(inp, tokenizer, model, vae, template=template, top_k=16, captions_num=1, bs=16, top_p=0.6, seed=43, temperature=0.8)
+
+  
+  return texts
+
+image = gr.inputs.Image(shape=(128, 128))
+label = gr.outputs.Label(num_top_classes=3)
+
+
+iface = gr.Interface(fn=classify_image, inputs=image, outputs="text",examples=[
+  ['b9c277a3.jpeg']])
+iface.launch(share=True)