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app.py

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+import gradio as gr
+import json
+from llmlingua import PromptCompressor
+import tiktoken
+
+compressors = {
+    "xlm-roberta": PromptCompressor(
+        #model_name="microsoft/llmlingua-2-xlm-roberta-large-meetingbank",
+        model_name='qminh369/token-classification-llmlingua2-xlm-roberta-42k_merge_1_epoch',
+        use_llmlingua2=True,
+        device_map="cpu"
+    )
+}
+
+tokenizer = tiktoken.encoding_for_model("gpt-4")
+
+with open('data/benchmark_33_bctn_so_lieu_5context.json', 'r') as f:
+    examples = json.load(f)
+
+def compress(original_prompt, compression_rate, base_model="xlm-roberta-large", force_tokens=['\n'], chunk_end_tokens=['.', '\n']):
+    if '\\n' in force_tokens:
+        idx = force_tokens.index('\\n')
+        force_tokens[idx] = '\n'
+
+    compressor = compressors.get(base_model, compressors["mbert-base"])
+    results = compressor.compress_prompt_llmlingua2(
+            original_prompt,
+            rate=compression_rate,
+            force_tokens=force_tokens,
+            chunk_end_tokens=chunk_end_tokens,
+            return_word_label=True,
+            drop_consecutive=True
+            )
+
+    compressed_prompt = results["compressed_prompt"]
+    n_word_compressed = len(tokenizer.encode(compressed_prompt))
+    
+    word_sep = "\t\t|\t\t"
+    label_sep = " "
+    lines = results["fn_labeled_original_prompt"].split(word_sep)
+    preserved_tokens = []
+    for line in lines:
+        word, label = line.split(label_sep)
+        preserved_tokens.append((word, '+') if label == '1' else (word, None))
+
+    return compressed_prompt, preserved_tokens, n_word_compressed
+
+title = "LLMLingua-2"
+
+header = """# LLMLingua-2
+        """
+
+theme = "soft"
+css = """#anno-img .mask {opacity: 0.5; transition: all 0.2s ease-in-out;}
+            #anno-img .mask.active {opacity: 0.7}"""
+
+original_prompt_text = """
+"""
+
+with gr.Blocks(title=title, css=css) as app:
+    gr.Markdown(header)
+    with gr.Row():
+        with gr.Column(scale=3):
+            original_prompt = gr.Textbox(value=original_prompt_text, label="Original Prompt", lines=10, max_lines=10, interactive=True)
+            compressed_prompt = gr.Textbox(value='', label="Compressed Prompt", lines=10, max_lines=10, interactive=False)
+            
+        with gr.Column(scale=1):
+            base_model = gr.Radio(["xlm-roberta"], label="Base Model", value="xlm-roberta", interactive=True)
+            force_tokens = gr.Dropdown(['\\n', '.', '!', '?', ','],
+                                       label="Tokens to Preserve",
+                                       value=['\\n', '.', '!', '?', ','],
+                                       multiselect=True,
+                                       interactive=True)
+            compression_rate = gr.Slider(minimum=0.1, maximum=1.0, step=0.1, value=0.7, label="Compression rate", info="after compr. / befor compr.", interactive=True)
+            n_word_original = gr.Textbox(lines=1, label="Original (GPT-4 Tokens)", interactive=False, value=len(tokenizer.encode(original_prompt_text)))
+            n_word_compressed = gr.Textbox(lines=1, label="Compressed (GPT-4 Tokens)", interactive=False)
+    button = gr.Button("⚡Click to Compress")
+    with gr.Accordion(label="Compression Details", open=False):
+        diff_text = gr.HighlightedText(label="Diff", combine_adjacent=False, show_legend=True, color_map={"+": "green"})
+
+    original_prompt.change(lambda x: len(tokenizer.encode(x)), inputs=[original_prompt], outputs=[n_word_original])
+    original_prompt.change(lambda x: ("", "", []), inputs=[original_prompt], outputs=[compressed_prompt, n_word_compressed, diff_text])
+
+    button.click(fn=compress,
+                 inputs=[original_prompt, compression_rate, base_model, force_tokens],
+                 outputs=[compressed_prompt, diff_text, n_word_compressed])
+        
+app.queue(max_size=10, api_open=False).launch(show_api=False)