Create app.py

app.py

@@ -0,0 +1,148 @@
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import gradio as gr
+import mdtex2html
+import torch
+
+"""Override Chatbot.postprocess"""
+
+
+# model_path = '/cjl/llm_finetuning/output/prompt_engineer_en_final/bpo_model'
+model_path = 'lmsys/vicuna-7b-v1.5'
+
+device = 'cpu'
+
+tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True, add_prefix_space=True)
+model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True).to(device)
+model = model.eval()
+
+prompt_template = "[INST] You are an expert prompt engineer. Please help me improve this prompt to get a more helpful and harmless response:\n{} [/INST]"
+
+
+def postprocess(self, y):
+    if y is None:
+        return []
+    for i, (message, response) in enumerate(y):
+        y[i] = (
+            None if message is None else mdtex2html.convert((message)),
+            None if response is None else mdtex2html.convert(response),
+        )
+    return y
+
+
+gr.Chatbot.postprocess = postprocess
+
+
+def parse_text(text):
+    """copy from https://github.com/GaiZhenbiao/ChuanhuChatGPT/"""
+    lines = text.split("\n")
+    lines = [line for line in lines if line != ""]
+    count = 0
+    for i, line in enumerate(lines):
+        if "```" in line:
+            count += 1
+            items = line.split('`')
+            if count % 2 == 1:
+                lines[i] = f'<pre><code class="language-{items[-1]}">'
+            else:
+                lines[i] = f'<br></code></pre>'
+        else:
+            if i > 0:
+                if count % 2 == 1:
+                    line = line.replace("`", "\`")
+                    line = line.replace("<", "&lt;")
+                    line = line.replace(">", "&gt;")
+                    line = line.replace(" ", "&nbsp;")
+                    line = line.replace("*", "&ast;")
+                    line = line.replace("_", "&lowbar;")
+                    line = line.replace("-", "&#45;")
+                    line = line.replace(".", "&#46;")
+                    line = line.replace("!", "&#33;")
+                    line = line.replace("(", "&#40;")
+                    line = line.replace(")", "&#41;")
+                    line = line.replace("$", "&#36;")
+                lines[i] = "<br>"+line
+    text = "".join(lines)
+    return text
+
+
+def predict(input, chatbot, max_length, top_p, temperature, history):
+
+    if input.strip() == "":
+        chatbot = [(parse_text(input), parse_text("Please input a valid user prompt. Empty string is not supported."))]
+        return chatbot, history
+
+    prompt = prompt_template.format(input)
+    model_inputs = tokenizer(prompt, return_tensors="pt").to(device)
+    output = model.generate(**model_inputs, max_length=max_length, do_sample=True, top_p=top_p, 
+                            temperature=temperature, num_beams=1)
+    resp = tokenizer.decode(output[0], skip_special_tokens=True).split('[/INST]')[1].strip()
+
+    optimized_prompt = """Here are several optimized prompts:
+
+====================Stable Optimization====================
+"""
+    optimized_prompt += resp
+    chatbot = [(parse_text(input), parse_text(optimized_prompt))]
+    yield chatbot, history
+
+    optimized_prompt += "\n\n====================Aggressive Optimization===================="
+
+    texts = [input] * 5  
+    responses = []
+    num = 0
+    for text in texts:
+        num += 1
+        seed = torch.seed()
+        torch.manual_seed(seed)
+        prompt = prompt_template.format(text)
+        min_length = len(tokenizer(prompt)['input_ids']) + len(tokenizer(text)['input_ids']) + 5
+        model_inputs = tokenizer(prompt, return_tensors="pt").to(device)
+        bad_words_ids = [tokenizer(bad_word, add_special_tokens=False).input_ids for bad_word in ["[PROTECT]", "\n\n[PROTECT]", "[KEEP", "[INSTRUCTION]"]]
+        # eos and \n
+        eos_token_ids = [tokenizer.eos_token_id, 13]
+        output = model.generate(**model_inputs, max_new_tokens=1024, do_sample=True, top_p=0.9, temperature=0.9, bad_words_ids=bad_words_ids, num_beams=1, eos_token_id=eos_token_ids, min_length=min_length)
+        resp = tokenizer.decode(output[0], skip_special_tokens=True).split('[/INST]')[1].split('[KE')[0].split('[INS')[0].split('[PRO')[0].strip()
+        
+        optimized_prompt += f"\n{num}. {resp}"
+
+        chatbot = [(parse_text(input), parse_text(optimized_prompt))]
+        yield chatbot, history
+ 
+    # for i in responses:
+        # print("[Aggressive Optimization] ", i)
+
+
+def reset_user_input():
+    return gr.update(value='')
+
+
+def reset_state():
+    return [], []
+
+
+with gr.Blocks() as demo:
+    gr.HTML("""<h1 align="center">Prompt Preference Optimizer</h1>""")
+
+    chatbot = gr.Chatbot(label="Prompt Optimization Chatbot")
+    with gr.Row():
+        with gr.Column(scale=4):
+            with gr.Column(scale=12):
+                user_input = gr.Textbox(show_label=False, placeholder="User Prompt...", lines=10).style(
+                    container=False)
+            with gr.Column(min_width=32, scale=1):
+                submitBtn = gr.Button("Submit", variant="primary")
+        with gr.Column(scale=1):
+            emptyBtn = gr.Button("Clear History")
+            max_length = gr.Slider(0, 4096, value=2048, step=1.0, label="Maximum length", interactive=True)
+            top_p = gr.Slider(0, 1, value=0.9, step=0.01, label="Top P", interactive=True)
+            temperature = gr.Slider(0, 1, value=0.6, step=0.01, label="Temperature", interactive=True)
+
+    history = gr.State([])
+
+    submitBtn.click(predict, [user_input, chatbot, max_length, top_p, temperature, history], [chatbot, history],
+                    show_progress=True)
+    submitBtn.click(reset_user_input, [], [user_input])
+
+    emptyBtn.click(reset_state, outputs=[chatbot, history], show_progress=True)
+
+demo.queue().launch(share=False, inbrowser=True)