Add application

app.py

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+import gradio as gr
+import torch
+import os
+from transformers import AutoTokenizer, T5ForConditionalGeneration
+
+model_id = 'ksabeh/t5-base-qpave'
+max_input_length = 512
+max_target_length = 20
+auth_token = os.environ.get('TOKEN')
+
+model = T5ForConditionalGeneration.from_pretrained(model_id, use_auth_token=auth_token)
+tokenizer = AutoTokenizer.from_pretrained(model_id, use_auth_token=auth_token)
+
+def predict(cg_attribute, text, fg_attribute, category):
+    input = f"{fg_attribute}: {text}"
+    model_input = tokenizer(input, max_length=max_input_length, truncation=True,
+                           padding="max_length")
+    model_input = {k:torch.unsqueeze(torch.tensor(v),dim=0) for k,v in model_input.items()}
+    predictions = model.generate(**model_input, num_beams=4, do_sample=True, max_length=10)
+    return tokenizer.batch_decode(predictions, skip_special_tokens=True)[0]
+
+# iface = gr.Interface(
+#     predict,
+#     inputs=["text", "text", "text", "text"],
+#     outputs=['text'],
+#     title="QPAVE",
+#     examples=[["Arriba Salsa Garlic and Cilantro, 16 oz", "Food"], 
+#     ["MV Verholen Black GPS Ball Mount for BMW K1200S K1200R K1300S K1300R Black GPS Ball Mount VER-4901-10181", "Toys"],
+#     ["Mitsubishi 3000GT License Plate Frame (Zince Metal)", "Automotive"],
+#     ["Fun Fire Truck Pinata Personalized", "Toys"],
+#     ["White Chocolate Caramel Gourmet Popcorn Kelly", "Food"]
+#     ]
+# )
+
+# iface.launch()
+
+demo = gr.Interface(
+    predict,
+    [
+        gr.Textbox(
+            label = "Coarse-grained Attribute",
+            info = "The coarse-grained attribute name",
+            lines = 1,
+        ),
+        gr.Textbox(
+            label = "Context",
+            info = "The value of the coarse-grained attribute",
+            lines = 1,
+        ),
+        gr.Textbox(
+            label = "Fine-grained Attribute",
+            info = "The target fine-grained attribute name",
+            lines = 1,
+        ),
+        gr.Textbox(
+            label = "Category",
+            info = "The product category",
+            lines = 1,
+        )
+    ],
+    "dataframe",
+    title="QPAVE",
+    examples=[["Processor", "3ghz intel core i5", "Brand Name", "Computers & Tablets"]
+              ],
+    cache_examples = True
+)

requirements.txt

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+transformers
+torch