Upload app.py

app.py

@@ -0,0 +1,77 @@
+import itertools
+from transformers import AutoTokenizer
+import ctranslate2
+import gradio as gr
+
+generator = ctranslate2.Generator("FixedStar-DebugChat/models/ct2-model" )
+tokenizer = AutoTokenizer.from_pretrained( "models", use_fast=False)
+
+static_prompt="""ユーザー: We will now start chatting. If spoken to in English, answer in English; if spoken to in Japanese, answer in Japanese. Please take a deep breath and calm down and have a conversation.
+システム: I'll try to keep calm and have a conversation.
+ユーザー: その調子で頑張ってください。
+システム: 分かりました。
+"""
+system_prompt_tokens=tokenizer.convert_ids_to_tokens(tokenizer.encode(static_prompt, add_special_tokens=False))
+
+def inference_func(prompt, max_length=128, sampling_topk=40, sampling_topp=0.75, sampling_temperature=0.7, repetition_penalty=1.4):
+    tokens = tokenizer.convert_ids_to_tokens( tokenizer.encode(prompt, add_special_tokens=False))
+    results = generator.generate_batch(
+        [tokens],
+        static_prompt=system_prompt_tokens,
+        max_length=max_length,
+        sampling_topk=sampling_topk,
+        sampling_topp=sampling_topp,  
+        sampling_temperature=sampling_temperature,
+        repetition_penalty=repetition_penalty,
+        include_prompt_in_result=False,
+    )
+    output = tokenizer.decode(results[0].sequences_ids[0])
+    return output
+
+def make_prompt(message, chat_history, max_context_size: int = 10):
+    contexts = chat_history + [[message, ""]]
+    contexts = list(itertools.chain.from_iterable(contexts))
+    if max_context_size > 0:
+        context_size = max_context_size - 1
+    else:
+        context_size = 100000
+    contexts = contexts[-context_size:]
+    prompt = []
+    for idx, context in enumerate(reversed(contexts)):
+        if idx % 2 == 0:
+            prompt = [f"システム: {context}"] + prompt
+        else:
+            prompt = [f"ユーザー: {context}"] + prompt
+    prompt = "\n".join(prompt)
+    return prompt
+
+
+def interact_func(message, chat_history, max_context_size, max_length, sampling_topk, sampling_topp, sampling_temperature, repetition_penalty ):
+    prompt = make_prompt(message, chat_history, max_context_size)
+    print(f"prompt: {prompt}")
+    generated = inference_func(prompt, max_length, sampling_topk, sampling_topp, sampling_temperature, repetition_penalty )
+    print(f"generated: {generated}")
+    chat_history.append((message, generated))
+    return "", chat_history
+
+with gr.Blocks( theme="monochrome" ) as demo:
+    with gr.Accordion("Parameters", open=False):
+        # max_context_size = the number of turns * 2
+        max_context_size = gr.Number(value=10, label="max_context_size", precision=0)
+        max_length = gr.Number(value=128, label="max_length", precision=0)
+        sampling_topk = gr.Slider(0, 1000, value=40, step=0.1, label="top_k")
+        sampling_topp = gr.Slider(0.1, 1.0, value=0.75, step=0.1, label="top_p")
+        sampling_temperature = gr.Slider(0.0, 10.0, value=0.7, step=0.1, label="temperature")
+        repetition_penalty = gr.Slider(0.0, 10.0, value=1.4, step=0.1, label="repetition_penalty")
+    chatbot = gr.Chatbot( show_copy_button=True, show_share_button="RETRY",  avatar_images=["icon.png", "user.png"] )
+    msg = gr.Textbox()
+    clear = gr.Button("RESET")
+    msg.submit(
+        interact_func,
+        [msg, chatbot, max_context_size, max_length, sampling_topk, sampling_topp, sampling_temperature, repetition_penalty],
+        [msg, chatbot],
+    )
+    clear.click(lambda: None, None, chatbot, queue=False)
+
+if __name__ == "__main__":
+    demo.launch()