Update app.py

app.py

@@ -1,70 +1,92 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
+import torch
+import time
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+from nltk.tokenize import sent_tokenize
 
+class DipperParaphraser(object):
+    def __init__(self, model="kalpeshk2011/dipper-paraphraser-xxl", verbose=True):
+        time1 = time.time()
+        self.tokenizer = T5Tokenizer.from_pretrained('google/t5-v1_1-xxl')
+        self.model = T5ForConditionalGeneration.from_pretrained(model)
+        if verbose:
+            print(f"{model} model loaded in {time.time() - time1}")
+        self.model.cuda()
+        self.model.eval()
 
-def respond(
-    message,
-    history: list[dict[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-    hf_token: gr.OAuthToken,
-):
-    """
-    For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-    """
-    client = InferenceClient(token=hf_token.token, model="openai/gpt-oss-20b")
+    def paraphrase(self, input_text, lex_diversity, order_diversity, prefix="", sent_interval=3, **kwargs):
+        assert lex_diversity in [0, 20, 40, 60, 80, 100], "Lexical diversity must be one of 0, 20, 40, 60, 80, 100."
+        assert order_diversity in [0, 20, 40, 60, 80, 100], "Order diversity must be one of 0, 20, 40, 60, 80, 100."
 
-    messages = [{"role": "system", "content": system_message}]
+        lex_code = int(100 - lex_diversity)
+        order_code = int(100 - order_diversity)
 
-    messages.extend(history)
+        input_text = " ".join(input_text.split())
+        sentences = sent_tokenize(input_text)
+        prefix = " ".join(prefix.replace("\n", " ").split())
+        output_text = ""
 
-    messages.append({"role": "user", "content": message})
+        for sent_idx in range(0, len(sentences), sent_interval):
+            curr_sent_window = " ".join(sentences[sent_idx:sent_idx + sent_interval])
+            final_input_text = f"lexical = {lex_code}, order = {order_code}"
+            if prefix:
+                final_input_text += f" {prefix}"
+            final_input_text += f" <sent> {curr_sent_window} </sent>"
 
-    response = ""
+            final_input = self.tokenizer([final_input_text], return_tensors="pt")
+            final_input = {k: v.cuda() for k, v in final_input.items()}
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        choices = message.choices
-        token = ""
-        if len(choices) and choices[0].delta.content:
-            token = choices[0].delta.content
+            with torch.inference_mode():
+                outputs = self.model.generate(**final_input, **kwargs)
+            outputs = self.tokenizer.batch_decode(outputs, skip_special_tokens=True)
+            prefix += " " + outputs[0]
+            output_text += " " + outputs[0]
 
-        response += token
-        yield response
+        return output_text.strip()
 
+dp = None
 
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-chatbot = gr.ChatInterface(
-    respond,
-    type="messages",
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
+def paraphrase_interface(prompt, input_text, lex_diversity, order_diversity, sent_interval, top_p, top_k, max_length, do_sample):
+    global dp
+    if dp is None:
+        dp = DipperParaphraser(verbose=False)
+    kwargs = {
+        "do_sample": do_sample,
+        "top_p": top_p,
+        "top_k": top_k if top_k else None,
+        "max_length": max_length,
+    }
+    return dp.paraphrase(
+        input_text,
+        lex_diversity=lex_diversity,
+        order_diversity=order_diversity,
+        prefix=prompt,
+        sent_interval=sent_interval,
+        **kwargs
+    )
 
 with gr.Blocks() as demo:
-    with gr.Sidebar():
-        gr.LoginButton()
-    chatbot.render()
+    gr.Markdown("# DIPPER Paraphraser XXL")
+    with gr.Row():
+        with gr.Column():
+            prompt = gr.Textbox(label="Prompt (Optional)", value="In a shocking finding, scientist discovered a herd of unicorns living in a remote valley.")
+            input_text = gr.Textbox(label="Text to Paraphrase", lines=8, value="They have never been known to mingle with humans. Today, it is believed these unicorns live in an unspoilt environment which is surrounded by mountains. Its edge is protected by a thick wattle of wattle trees, giving it a majestic appearance. Along with their so-called miracle of multicolored coat, their golden coloured feather makes them look like mirages. Some of them are rumored to be capable of speaking a large amount of different languages. They feed on elk and goats as they were selected from those animals that possess a fierceness to them, and can \"eat\" them with their long horns.")
+            lex_diversity = gr.Dropdown(label="Lexical Diversity", choices=[0, 20, 40, 60, 80, 100], value=60)
+            order_diversity = gr.Dropdown(label="Order Diversity", choices=[0, 20, 40, 60, 80, 100], value=0)
+            sent_interval = gr.Number(label="Sentence Interval", value=3, precision=0)
+            top_p = gr.Number(label="Top P (sampling)", value=0.75)
+            top_k = gr.Number(label="Top K (sampling, None for default)", value=None, precision=0)
+            max_length = gr.Number(label="Max Length", value=512, precision=0)
+            do_sample = gr.Checkbox(label="Enable Sampling", value=True)
+            btn = gr.Button("Paraphrase")
+        with gr.Column():
+            output = gr.Textbox(label="Paraphrased Output", lines=8)
 
+    btn.click(
+        paraphrase_interface,
+        inputs=[prompt, input_text, lex_diversity, order_diversity, sent_interval, top_p, top_k, max_length, do_sample],
+        outputs=output
+    )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()