Create app.py

app.py

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+from peft import PeftModel, PeftConfig
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+from transformers import GenerationConfig
+
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+
+#Base Model 및 Lora Model 선택
+base_model = "beomi/KoAlpaca-Polyglot-5.8B"
+lora_weights = 'KimSHine/Scenario_Koalpaca_5.8B-lora'
+load_8bit = True
+
+# Base Model Tokenizer
+tokenizer1 = AutoTokenizer.from_pretrained(base_model)
+## token id 추가
+tokenizer1.pad_token_id = 0
+tokenizer1.eos_token_id = 2
+
+"""### Base Model 불러오기"""
+
+# KoAlpaca-polyglot-5.8B
+
+model1 = AutoModelForCausalLM.from_pretrained(
+            base_model,
+            load_in_8bit=load_8bit,
+            torch_dtype=torch.float16,
+            device_map="auto",
+        )
+
+model1.config.pad_token_id = 0
+model1.config.eos_token_id = 2
+
+"""### LoRA Model 불러오기
+Fine Tuning한 Model
+"""
+
+model1 = PeftModel.from_pretrained(
+    model1,
+    lora_weights,
+    torch_dtype=torch.float16,
+)
+
+model1.config.pad_token_id = 0  # unk
+model1.config.bos_token_id = 0
+model1.config.eos_token_id = 2
+
+def yeollm_text(instruction, summary):
+
+    prompt = f"""아래는 작업을 설명하는 지시문과 대본을 생성하는데 참고할 줄거리와 짝을 이루는 예제입니다. 요청을 적절히 만족하는 대본을 작성하세요.
+    ### 지시문:
+    {instruction}
+    ### 줄거리:
+    {summary}
+    ### 대본:
+    """
+
+    temperature = 0.3
+    top_p = 0.95
+    top_k = 40
+    max_new_tokens = 512 #2048
+    no_repeat_ngram_size = 5    # 3개 이상의 토큰이 반복될 경우 확률을 0으로 만듦
+    ## greed search, beam search의 결과는 바뀌지 않음 (물론 최종 결과는 바뀜, 중간 sample 만드는 것은 동일하다는 것)
+    do_sample = True    ## True : random, False(default) : Greedy Search
+    num_beams = 5   ## do_sample 이 false일 때 여기에 값이 있으면, beam search
+
+    inputs = tokenizer1(prompt, return_tensors="pt")
+    input_ids = inputs["input_ids"].to(DEVICE)
+
+    generation_config = GenerationConfig(
+        do_sample = do_sample,
+        temperature=temperature,
+        top_p=top_p,
+        top_k=top_k,
+        pad_token_id = 0,   # pad token 추가
+        no_repeat_ngram_size = no_repeat_ngram_size,
+        # num_beams=num_beams,
+        # **kwargs,
+    )
+
+    # Generate text
+    with torch.no_grad():
+        generation_output = model1.generate(
+            input_ids=input_ids,
+            generation_config=generation_config,
+            return_dict_in_generate=True,
+            output_scores=True,
+            max_new_tokens=max_new_tokens,
+        )
+    s = generation_output.sequences[0]
+    output = tokenizer1.decode(s)
+    output = output.split('### 대본:')[1]
+    if output[-13:] == '<|endoftext|>':
+      output = output[:-13]
+    return output.lstrip()
+
+"""## text davinci 003 불러오기"""
+
+import openai
+OPENAI_API_KEY = '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'
+openai.api_key = OPENAI_API_KEY
+
+model2 = 'text-davinci-003' #'gpt-3.5-turbo'
+max_tokens = 2048
+temperature = 0.3
+Top_p = 1
+
+def davinci_text(instruction, summary):
+    prompt = f"""
+    아래의 줄거리를 보고 {instruction}
+    ### 줄거리:
+    {summary}
+    ### 대본:
+    """
+
+    response = openai.Completion.create(
+        engine = model2,
+        prompt = prompt,
+        temperature = temperature,
+        max_tokens = max_tokens,
+        n=1,
+    )
+    return response.choices[0].text.strip()
+
+
+"""## gpt 3.5 turbo 불러오기"""
+
+import openai
+OPENAI_API_KEY = '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'
+openai.api_key = OPENAI_API_KEY
+
+model4 = 'gpt-3.5-turbo' #'gpt-3.5-turbo'
+max_tokens = 2048
+temperature = 0.3
+Top_p = 1
+
+
+def gpt_text(instruction, summary):
+  prompt =  f"""
+    ### 지시문:
+    {instruction}
+    ### 줄거리:
+    {summary}
+    ### 대본:
+    """
+  response = openai.ChatCompletion.create(
+      model = model4,
+      messages=[
+        {"role": "system", "content": "아래는 작업을 설명하는 지시문과 대본을 생성하는데 참고할 줄거리와 짝을 이루는 예제입니다. 요청을 적절히 만족하는 대본을 작성하세요."},
+        {"role": "user", "content": prompt},
+        ],
+      temperature = temperature,
+      max_tokens = max_tokens,
+      n=1,
+  )
+  for choice in response["choices"]:
+      content = choice["message"]["content"]
+
+  return content
+
+"""# gradio"""
+
+import gradio as gr
+
+generator1 = gr.Interface(
+    fn=yeollm_text,
+    inputs=[
+        gr.inputs.Textbox(label="Instruction"),
+        gr.inputs.Textbox(label="Summary")
+    ],
+    outputs=gr.outputs.Textbox(label="Yeollm Scenario"),
+    title="Yeollm Scenario Generation",
+    description="Generate scenarios using the Yeollm model.",
+    theme="huggingface"
+)
+
+generator2 = gr.Interface(
+    fn=davinci_text,
+    inputs=[
+        gr.inputs.Textbox(label="Instruction"),
+        gr.inputs.Textbox(label="Summary")
+    ],
+    outputs=gr.outputs.Textbox(label="Davinci Scenario"),
+    title="Davinci Generation",
+    description="Generate scenarios using the Davinci model.",
+    theme="huggingface"
+)
+
+generator3 = gr.Interface(
+    fn=gpt_text,
+    inputs=[
+        gr.inputs.Textbox(label="Instruction"),
+        gr.inputs.Textbox(label="Summary")
+    ],
+    outputs=gr.outputs.Textbox(label="GPT Scenario"),
+    title="GPT Generation",
+    description="Generate scenarios using the GPT model.",
+    theme="huggingface"
+)
+
+gr.Parallel(generator1, generator2, generator3).launch(share=True, debug=True)