Add description

app.py

@@ -12,6 +12,8 @@ from time import sleep
 from threading import Thread
 from torch import float16
 import spaces
+import huggingface_hub
+
 
 config = PeftConfig.from_pretrained("lang-uk/dragoman")
 quant_config = BitsAndBytesConfig(
@@ -41,40 +43,64 @@ def translate(input_text):
     input_text = f"[INST] {input_text} [/INST]"
     inputs = tokenizer([input_text], return_tensors="pt").to(model.device)
 
-    generation_kwargs = dict(inputs, max_new_tokens=200, num_beams=10, temperature=1) # streamer=streamer,
-    
-    
+    generation_kwargs = dict(
+        inputs, max_new_tokens=200, num_beams=10, temperature=1
+    )  # streamer=streamer,
+
     # streaming support
-    #streamer = TextIteratorStreamer(
+    # streamer = TextIteratorStreamer(
     #    tokenizer, skip_prompt=True, skip_special_tokens=True
-    #)
-
-    #thread = Thread(target=model.generate, kwargs=generation_kwargs)
+    # )
 
-    #thread.start()
+    # thread = Thread(target=model.generate, kwargs=generation_kwargs)
 
+    # thread.start()
 
-    #for new_text in streamer:
+    # for new_text in streamer:
     #    generated_text += new_text
     #    yield generated_text
 
-    #generated_text += "\n"
-    #yield generated_text
+    # generated_text += "\n"
+    # yield generated_text
 
     output = model.generate(**generation_kwargs)
-    output = tokenizer.decode(output[0], skip_special_tokens=True).split("[/INST] ")[-1].strip()
+    output = (
+        tokenizer.decode(output[0], skip_special_tokens=True)
+        .split("[/INST] ")[-1]
+        .strip()
+    )
     return output
 
 
+# download description of the model
+desc_file = huggingface_hub.hf_hub_download("lang-uk/dragoman", "README.md")
+
+with open(desc_file, "r") as f:
+    model_description = f.read()
+    model_description = model_description[model_description.find("---", 1) + 5 :]
+
+
 iface = gr.Interface(
     fn=translate,
     inputs=gr.Textbox(
-        value="",
+        value='This demo contains a model from paper "Setting up the Data Printer with Improved English to Ukrainian Machine Translation", accepted to UNLP 2024 workshop at the LREC-COLING 2024 conference.',
         label="Source sentence",
     ),
     outputs=gr.Textbox(label="Translated sentence"),
-    examples=[[
-        "ChatGPT (Chat Generative Pre-trained Transformer) is a chatbot developed by OpenAI and launched on November 30, 2022. Based on a large language model, it enables users to refine and steer a conversation towards a desired length, format, style, level of detail, and language. Successive prompts and replies, known as prompt engineering, are considered at each conversation stage as a context.[2] ",
-        "who holds this neighborhood?"]],
+    examples=[
+        [
+            "ChatGPT (Chat Generative Pre-trained Transformer) is a chatbot developed by OpenAI and launched on November 30, 2022. Based on a large language model, it enables users to refine and steer a conversation towards a desired length, format, style, level of detail, and language. Successive prompts and replies, known as prompt engineering, are considered at each conversation stage as a context.[2] ",
+            "who holds this neighborhood?",
+        ]
+    ],
+    title="Dragoman: SOTA English-Ukrainian translation model",
+    description='This demo contains a model from paper "Setting up the Data Printer with Improved English to Ukrainian Machine Translation", accepted to UNLP 2024 workshop at the LREC-COLING 2024 conference.',
+    article=model_description,
+    # thumbnail: str | None = None,
+    # css: str | None = None,
+    # batch: bool = False,
+    # max_batch_size: int = 4,
+    # api_name: str | Literal[False] | None = "predict",
+    submit_btn="Translate",
 )
 iface.launch()