Add application file

README.md

@@ -1,13 +1,13 @@
 ---
 title: Mt5 Translate Summ
-emoji: 😻
+emoji: 🎓
 colorFrom: pink
 colorTo: green
 sdk: gradio
 sdk_version: 3.38.0
 app_file: app.py
 pinned: false
-license: mit
+license: apache-2.0
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+import random
+import torch
+from transformers import MT5Tokenizer, MT5ForConditionalGeneration
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+tokenizer = MT5Tokenizer.from_pretrained("potsawee/mt5-english-thai-large-translation")
+translator = MT5ForConditionalGeneration.from_pretrained("potsawee/mt5-english-thai-large-translation")
+summarizer = MT5ForConditionalGeneration.from_pretrained("potsawee/mt5-english-thai-large-summarization")
+translator.eval()
+summarizer.eval()
+translator.to(device)
+summarizer.to(device)
+
+
+# def generate_multiple_choice_question(
+#     context
+# ):
+#     num_questions = 1
+#     question_item = question_generation_sampling(
+#         g1_model, g1_tokenizer,
+#         g2_model, g2_tokenizer,
+#         context, num_questions, device
+#     )[0]
+#     question = question_item['question']
+#     options = question_item['options']
+#     options[0] = f"{options[0]} [ANSWER]"
+#     random.shuffle(options)
+#     output_string = f"Question: {question}\n[A] {options[0]}\n[B] {options[1]}\n[C] {options[2]}\n[D] {options[3]}"
+#     return output_string
+#
+# demo = gr.Interface(
+#     fn=generate_multiple_choice_question,
+#     inputs=gr.Textbox(lines=8, placeholder="Context Here..."),
+#     outputs=gr.Textbox(lines=5, placeholder="Question: \n[A] \n[B] \n[C] \n[D] "),
+#     title="Multiple-choice Question Generator",
+#     description="Provide some context (e.g. news article or any passage) in the context box and click **Submit**. The models currently support English only. This demo is a part of MQAG - https://github.com/potsawee/mqag0.",
+#     allow_flagging='never'
+# )
+
+def generate_output(
+    task,
+    text,
+):
+    inputs = tokenizer(
+        [text],
+        padding="longest",
+        max_length=1024,
+        truncation=True,
+        return_tensors="pt",
+    ).to(device)
+    if task == 'Translation':
+        outputs = translator.generate(
+            **inputs,
+            max_new_tokens=256,
+        )
+    elif task == 'Summarization':
+        outputs = summarizer.generate(
+            **inputs,
+            max_new_tokens=256,
+        )
+    else:
+        raise ValueError("task undefined!")
+    gen_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return gen_text
+
+TASKS = ["Translation", "Summarization"]
+
+demo = gr.Interface(
+    fn=generate_output,
+    inputs=[
+        gr.components.Radio(label="Task", choices=TASKS, value="Translation"),
+        gr.components.Textbox(label="Text (in English)", lines=10),
+    ],
+    outputs=gr.Textbox(label="Text (in Thai)", lines=4),
+    # examples=[["Building a translation demo with Gradio is so easy!", "eng_Latn", "spa_Latn"]],
+    cache_examples=False,
+    title="English🇬🇧 to Thai🇹🇭 | Translation or Summarization",
+    description="Provide some text (in English) & select one of the tasks (Translation or Summarization). Note that currently the model only supports text up to 1024 tokens. The base architecture is mt5-large with the embeddings filtered to only English and Thai tokens and fine-tuned to XSum (Eng2Thai) Dataset (https://huggingface.co/datasets/potsawee/xsum_eng2thai).",
+    allow_flagging='never'
+
+)
+
+demo.launch()

requirements.txt

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+torch>=1.10
+transformers>=4.11.3