Create app.py

app.py

@@ -0,0 +1,54 @@
+import os
+import gradio as gr
+
+from transformers import T5ForConditionalGeneration, AutoTokenizer
+# from transformers import pipeline
+
+auth_token = os.environ.get("CLARIN_KNEXT")
+
+model_name = "clarin-knext/plt5-large-poquad-ext-qa-autotoken"
+tokenizer = AutoTokenizer.from_pretrained(model_name, use_auth_token=auth_token)
+model = T5ForConditionalGeneration.from_pretrained(model_name, use_auth_token=auth_token)
+
+
+default_generate_kwargs = {
+            "max_length": 192,
+            "num_beams": 2,
+            "length_penalty": 0,
+            "early_stopping": True,
+        }
+
+# keywords_pipe = pipeline(model=model, tokenizer=tokenizer, **default_generate_kwargs)
+
+examples = [
+    ["extract question: Kiedy Arek ma spotkanie dzisiaj? context: Arek ma spotkanie przez cały dzeiń. Bardzo to lubi."]
+
+
+
+def generate(context):
+    context = context + " </s>"
+    inputs = tokenizer(
+        context, 
+        max_length=512,
+        add_special_tokens=True,
+        truncation=True,
+        padding=False,
+        return_tensors="pt"
+        )
+    
+    outs = model.generate(
+        input_ids=inputs['input_ids'], 
+        attention_mask=inputs['attention_mask'],
+        **default_generate_kwargs
+        )
+    prediction = tokenizer.decode(outs[0], skip_special_tokens=True)
+    return prediction
+
+demo = gr.Interface(
+    fn=generate,
+    inputs=gr.inputs.Textbox(lines=5, label="Input Text"),
+    outputs=gr.outputs.Textbox(label="Keywords"),
+    examples=examples,
+)
+
+demo.launch()