Create app.py

app.py

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+import torch
+from transformers import AutoTokenizer, TextIteratorStreamer
+from modeling_nort5 import NorT5ForConditionalGeneration
+from threading import Thread
+
+
+# print(f"Starting to load the model to memory")
+
+# tokenizer = AutoTokenizer.from_pretrained("nort5_en-no_base")
+# cls_index = tokenizer.convert_tokens_to_ids("[CLS]")
+# sep_index = tokenizer.convert_tokens_to_ids("[SEP]")
+# user_index = tokenizer.convert_tokens_to_ids("[USER]")
+# assistent_index = tokenizer.convert_tokens_to_ids("[ASSISTENT]")
+
+# model = NorT5ForConditionalGeneration.from_pretrained("nort5_en-no_base", ignore_mismatched_sizes=True)
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+print(f"SYSTEM: Running on {device}", flush=True)
+
+# model = model.to(device)
+# model.eval()
+
+# print(f"Sucessfully loaded the model to the memory")
+
+
+INITIAL_PROMPT = "Du er NorT5, en språkmodell laget ved Universitetet i Oslo. Du er en hjelpsom og ufarlig assistent som er glade for å hjelpe brukeren med enhver forespørsel."
+TEMPERATURE = 0.7
+SAMPLE = True
+BEAMS = 1
+PENALTY = 1.2
+TOP_K = 64
+TOP_P = 0.95
+
+
+def translate(source, source_language, target_language):
+    return "This is a fake translation"
+
+
+import gradio as gr
+
+with gr.Blocks(theme='sudeepshouche/minimalist') as demo:
+    gr.Markdown("# Norwegian-English translation")
+    # gr.HTML('<img src="https://huggingface.co/ltg/norbert3-base/resolve/main/norbert.png" width=6.75%>')
+    # gr.Checkbox(label="I want to publish all my conversations", value=True)
+
+    # chatbot = gr.Chatbot(value=[[None, "Hei, hva kan jeg gjøre for deg? 😊"]])
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            source_language = gr.Dropdown(
+                ["English", "Norwegian (Bokmål)", "Norwegian (Nynorsk)"], label="English"
+            )
+            source = gr.Textbox(
+                label="Source text", placeholder="What do you want to translate?", show_label=True
+            )  # .style(container=False)
+            submit = gr.Button("Submit", variant="primary")  # .style(full_width=True)
+
+        with gr.Column(scale=1):
+            target_language = gr.Dropdown(
+                ["English", "Norwegian (Bokmål)", "Norwegian (Nynorsk)"], label="Norwegian (Bokmål)"
+            )
+            target = gr.Textbox(
+                label="Translation", show_label=True, interactive=False
+            )
+
+
+    def update_state_after_user():
+        return {
+            source: gr.update(interactive=False),
+            submit: gr.update(interactive=False),
+            source_language: gr.update(interactive=False),
+            target_language: gr.update(interactive=False)
+        }
+
+    def update_state_after_return():
+        return {
+            source: gr.update(interactive=True),
+            submit: gr.update(interactive=True),
+            source_language: gr.update(interactive=False),
+            target_language: gr.update(interactive=False)
+        }
+
+
+    submit_event = source.submit(
+        fn=update_state_after_user, inputs=None, outputs=[source, target, source_language, target_language], queue=False
+    ).then(
+        fn=translate, inputs=[source, source_language, target_language], outputs=[target], queue=True
+    ).then(
+        fn=update_state_after_return, inputs=None, outputs=[source, target, source_language, target_language], queue=False
+    )
+    
+    submit_click_event = submit.click(
+        fn=update_state_after_user, inputs=None, outputs=[source, target, source_language, target_language], queue=False
+    ).then(
+        fn=translate, inputs=[source, source_language, target_language], outputs=[target], queue=True
+    ).then(
+        fn=update_state_after_return, inputs=None, outputs=[source, target, source_language, target_language], queue=False
+    )
+
+demo.queue(max_size=32, concurrency_count=2)
+demo.launch()