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README.md

@@ -1,13 +1,15 @@
 ---
-title: LLMpromt
-emoji: 🏃
-colorFrom: gray
-colorTo: red
+title: Prompt Playground
+emoji: 🏢
+colorFrom: yellow
+colorTo: pink
 sdk: gradio
 sdk_version: 4.42.0
 app_file: app.py
-pinned: false
+pinned: true
 license: apache-2.0
+short_description: Prompt Playground_Pv
+
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+from huggingface_hub import InferenceClient
+import os
+import random
+import logging
+
+# 로깅 설정
+logging.basicConfig(filename='language_model_playground.log', level=logging.DEBUG, 
+ format='%(asctime)s - %(levelname)s - %(message)s')
+
+# 모델 목록
+MODELS = {
+ "Zephyr 7B Beta": "HuggingFaceH4/zephyr-7b-beta",
+ "DeepSeek Coder V2": "deepseek-ai/DeepSeek-Coder-V2-Instruct",
+ "Meta Llama 3.1 8B": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+ "Meta-Llama 3.1 70B-Instruct": "meta-llama/Meta-Llama-3.1-70B-Instruct",
+ "Microsoft": "microsoft/Phi-3-mini-4k-instruct",
+ "Mixtral 8x7B": "mistralai/Mistral-7B-Instruct-v0.3",
+ "Mixtral Nous-Hermes": "NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO",
+ "Cohere Command R+": "CohereForAI/c4ai-command-r-plus",
+ "Aya-23-35B": "CohereForAI/aya-23-35B"
+}
+
+# HuggingFace 토큰 설정
+hf_token = os.getenv("HF_TOKEN")
+if not hf_token:
+ raise ValueError("HF_TOKEN 환경 변수가 설정되지 않았습니다.")
+
+def call_hf_api(prompt, reference_text, max_tokens, temperature, top_p, model):
+ client = InferenceClient(model=model, token=hf_token)
+ combined_prompt = f"{prompt}\n\n참고 텍스트:\n{reference_text}"
+ random_seed = random.randint(0, 1000000)
+ 
+ try:
+ response = client.text_generation(
+ combined_prompt,
+ max_new_tokens=max_tokens,
+ temperature=temperature,
+ top_p=top_p,
+ seed=random_seed
+ )
+ return response
+ except Exception as e:
+ logging.error(f"HuggingFace API 호출 중 오류 발생: {str(e)}")
+ return f"응답 생성 중 오류 발생: {str(e)}. 나중에 다시 시도해 주세요."
+
+def generate_response(prompt, reference_text, max_tokens, temperature, top_p, model):
+ response = call_hf_api(prompt, reference_text, max_tokens, temperature, top_p, MODELS[model])
+ response_html = f"""
+ <h3>생성된 응답:</h3>
+ <div style='max-height: 500px; overflow-y: auto; white-space: pre-wrap; word-wrap: break-word;'>
+ {response}
+ </div>
+ """
+ return response_html
+
+# Gradio 인터페이스 설정
+with gr.Blocks() as demo:
+ gr.Markdown("## 언어 모델 프롬프트 플레이그라운드")
+
+ with gr.Column():
+ model_radio = gr.Radio(choices=list(MODELS.keys()), value="Zephyr 7B Beta", label="언어 모델 선택")
+ prompt_input = gr.Textbox(label="프롬프트 입력", lines=5)
+ reference_text_input = gr.Textbox(label="참고 텍스트 입력", lines=5)
+ 
+ with gr.Row():
+ max_tokens_slider = gr.Slider(minimum=0, maximum=5000, value=2000, step=100, label="최대 토큰 수")
+ temperature_slider = gr.Slider(minimum=0, maximum=1, value=0.75, step=0.05, label="온도")
+ top_p_slider = gr.Slider(minimum=0, maximum=1, value=0.95, step=0.05, label="Top P")
+ 
+ generate_button = gr.Button("응답 생성")
+ response_output = gr.HTML(label="생성된 응답")
+
+ # 버튼 클릭 시 응답 생성
+ generate_button.click(
+ generate_response,
+ inputs=[prompt_input, reference_text_input, max_tokens_slider, temperature_slider, top_p_slider, model_radio],
+ outputs=response_output
+ )
+
+# 인터페이스 실행
+demo.launch(share=True)

gitattributes

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requirements.txt

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+gradio
+huggingface_hub