Update app.py

app.py

@@ -8,111 +8,6 @@ model = AutoModelForCausalLM.from_pretrained(
     device_map="auto"
 )
 tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, use_fast=False)
-
-device = model.device
-device
-
-user_prompt_template = "ユーザー: Hello, you are an assistant that helps me learn Japanese. I am going to ask you a question, so please answer *briefly*."
-system_prompt_template = "システム: Sure, I will answer briefly. What can I do for you?"
-
-# one-shot
-user_sample = "ユーザー: 日本で一番高い山は何ですか？"
-system_sample = "システム: 富士山です。高さは3776メートルです。"
-
-# 質問
-user_prerix = "ユーザー: "
-user_question = "人工知能とは何ですか？"
-system_prefix = "システム: "
-
-# プロンプトの整形
-prompt = user_prompt_template + "\n" + system_prompt_template + "\n"
-prompt += user_sample + "\n" + system_sample + "\n"
-prompt += user_prerix + user_question + "\n" + system_prefix
-
-inputs = tokenizer(
-    prompt,
-    add_special_tokens=False, # プロンプトに余計なトークンが付属するのを防ぐ
-    return_tensors="pt"
-)
-inputs = inputs.to(model.device)
-with torch.no_grad():
-    tokens = model.generate(
-        **inputs,
-        temperature=0.3,
-        top_p=0.85,
-        max_new_tokens=2048,
-        repetition_penalty=1.05,
-        do_sample=True,
-        pad_token_id=tokenizer.pad_token_id,
-        bos_token_id=tokenizer.bos_token_id,
-        eos_token_id=tokenizer.eos_token_id
-    )
-
-tokens
-
-output = tokenizer.decode(
-    tokens[0],
-    skip_special_tokens=True # 出力に余計なトークンが付属するのを防ぐ
-)
-print(output)
-
-output[len(prompt):]
-
-def generate(user_question,
-             temperature=0.3,
-             top_p=0.85,
-             max_new_tokens=2048,
-             repetition_penalty=1.05
-            ):
-
-    user_prompt_template = "ユーザー: Hello, you are an assistant that helps me learn Japanese. I am going to ask you a question, so please answer *briefly*."
-    system_prompt_template = "システム: Sure, I will answer briefly. What can I do for you?"
-
-    user_sample = "ユーザー: 日本で一番高い山は何ですか？"
-    system_sample = "システム: 富士山です。高さは3776メートルです。"
-
-    user_prerix = "ユーザー: "
-    system_prefix = "システム: "
-
-    prompt = user_prompt_template + "\n" + system_prompt_template + "\n"
-    prompt += user_sample + "\n" + system_sample + "\n"
-    prompt += user_prerix + user_question + "\n" + system_prefix
-
-    inputs = tokenizer(prompt, add_special_tokens=False, return_tensors="pt")
-    inputs = inputs.to(model.device)
-    with torch.no_grad():
-        tokens = model.generate(
-            **inputs,
-            temperature=temperature,
-            top_p=top_p,
-            max_new_tokens=max_new_tokens,
-            repetition_penalty=repetition_penalty,
-            do_sample=True,
-            pad_token_id=tokenizer.pad_token_id,
-            bos_token_id=tokenizer.bos_token_id,
-            eos_token_id=tokenizer.eos_token_id
-        )
-    output = tokenizer.decode(tokens[0], skip_special_tokens=True)
-    return output[len(prompt):]
-
-output = generate('人工知能とは何ですか？')
-output
-
-
-import gradio as gr # 慣習としてgrと略記
-
-with gr.Blocks() as demo:
-    inputs = gr.Textbox(label="Question:", placeholder="人工知能とは何ですか？")
-    outputs = gr.Textbox(label="Answer:")
-    btn = gr.Button("Send")
-
-    # ボタンが押された時の動作を以下のように定義する:
-    # 「inputs内の値を入力としてモデルに渡し、その戻り値をoutputsの値として設定する」
-    btn.click(fn=generate, inputs=inputs, outputs=outputs)
-
-if __name__ == "__main__":
-    demo.launch()
-    
 def generate_response(user_question,
              chat_history,
              temperature=0.3,
@@ -158,7 +53,9 @@ def generate_response(user_question,
     output = tokenizer.decode(tokens[0], skip_special_tokens=True)
     return output[len(prompt):]
 
+    import gradio as gr # 慣習としてgrと略記
 
+    
 with gr.Blocks() as demo:
     chat_history = gr.Chatbot()
     user_message = gr.Textbox(label="Question:", placeholder="人工知能とは何ですか？")
@@ -174,3 +71,4 @@ with gr.Blocks() as demo:
 if __name__ == "__main__":
     demo.launch()
 
+