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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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+*.ckpt filter=lfs diff=lfs merge=lfs -text
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requirements.txt

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+gradio
+huggingface_hub