Create app.py

app.py

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+import torch, PIL
+import gradio as gr
+
+title = "OctoBERT"
+description = """Interactive Demo for OctoBERT. This base model is trained only on Flickr-30k."""
+examples =[
+['https://i.imgur.com/ASXtqVc.jpg','The woman stands outdoors, next to a child in a <mask>.'],
+['https://i.imgur.com/UspUc7t.jpg', 'A woman in blue shorts and white shirt holds a tennis racket on a blue <mask> court.'],
+['https://i.imgur.com/IRUoMUg.jpg', 'The smiling <mask> is celebrating her <mask> party with friends, surrounded by balloons and a <mask> with candles.'],
+['https://i.imgur.com/23BZbaH.jpg', 'A person in a rainbow colored snowsuit is snowboarding down a <mask> slope.'],
+['https://i.imgur.com/hoQCg1h.jpg', 'A man with <mask> plays with a little girl while walking down the street, while an Asian woman walks ahead of them.'],
+['https://i.imgur.com/dFeN6bs.jpg', 'A black dog stands on a <mask>, green fields behind him.'],
+]
+device = "cuda" if torch.cuda.is_available() else "cpu"
+# model, img_transform, tokenizer, post_processor, plot_results = torch.hub.load('Jiayi-Pan/RefCloze_Pub', 'flickr_base_model')
+model, img_transform, tokenizer, post_processor, plot_results = torch.hub.load('.', 'flickr_base_model', source='local')
+model = model.to(device)
+
+def plot_inference(img, caption):
+    imgs_tensor = img_transform(img).to(device).unsqueeze(0)
+    tokens_tensor = tokenizer(caption, return_tensors="pt").to(device)
+    with torch.no_grad():
+        outputs = model(imgs_tensor, tokens_tensor, one_pass=True)
+    processed_outputs = post_processor(outputs, img, tokenizer)
+    vis = plot_results(img, processed_outputs, save_path="numpy_array")
+    return vis, processed_outputs['cap']
+
+
+gr.Interface(
+    plot_inference, 
+    [gr.inputs.Image(type="pil", label="Input"), gr.inputs.Textbox(label="input text")], 
+    [gr.outputs.Image(type="numpy", label="Output"), gr.outputs.Textbox(label="Predicted Words")],
+    title=title,
+    description=description,
+    examples=examples,
+    cache_examples=True,
+    ).launch()