Update app.py

app.py

@@ -1,114 +1,2 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
 import os
-
-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",
-    "Mixtral 8x7B": "mistralai/Mixtral-8x7B-Instruct-v0.1",
-    "Cohere Command R+": "CohereForAI/c4ai-command-r-plus",
-}
-
-def get_client(model_name):
-    model_id = MODELS[model_name]
-    hf_token = os.getenv("HF_TOKEN")
-    if not hf_token:
-        raise ValueError("HF_TOKEN environment variable is required")
-    return InferenceClient(model_id, token=hf_token)
-
-def respond(
-    message,
-    chat_history,
-    model_name,
-    max_tokens,
-    temperature,
-    top_p,
-    system_message,
-):
-    try:
-        client = get_client(model_name)
-    except ValueError as e:
-        chat_history.append((message, str(e)))
-        return chat_history
-
-    messages = [{"role": "system", "content": system_message}]
-    for human, assistant in chat_history:
-        messages.append({"role": "user", "content": human})
-        messages.append({"role": "assistant", "content": assistant})
-    messages.append({"role": "user", "content": message})
-
-    try:
-        if "Cohere" in model_name:
-            # Cohere 모델을 위한 비스트리밍 처리
-            response = client.chat_completion(
-                messages,
-                max_tokens=max_tokens,
-                temperature=temperature,
-                top_p=top_p,
-            )
-            assistant_message = response.choices[0].message.content
-            chat_history.append((message, assistant_message))
-            yield chat_history
-        else:
-            # 다른 모델들을 위한 스트리밍 처리
-            stream = client.chat_completion(
-                messages,
-                max_tokens=max_tokens,
-                temperature=temperature,
-                top_p=top_p,
-                stream=True,
-            )
-            partial_message = ""
-            for response in stream:
-                if response.choices[0].delta.content is not None:
-                    partial_message += response.choices[0].delta.content
-                    if len(chat_history) > 0 and chat_history[-1][0] == message:
-                        chat_history[-1] = (message, partial_message)
-                    else:
-                        chat_history.append((message, partial_message))
-                    yield chat_history
-    except Exception as e:
-        error_message = f"An error occurred: {str(e)}"
-        chat_history.append((message, error_message))
-        yield chat_history
-
-def clear_conversation():
-    return []
-
-with gr.Blocks() as demo:
-    gr.Markdown("# Prompting AI Chatbot")
-    gr.Markdown("언어모델별 프롬프트 테스트 챗봇입니다.")
-
-    with gr.Row():
-        with gr.Column(scale=1):
-            model_name = gr.Radio(
-                choices=list(MODELS.keys()),
-                label="Language Model",
-                value="Zephyr 7B Beta"
-            )
-            max_tokens = gr.Slider(minimum=0, maximum=2000, value=500, step=100, label="Max Tokens")
-            temperature = gr.Slider(minimum=0.1, maximum=2.0, value=0.7, step=0.05, label="Temperature")
-            top_p = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p")
-            system_message = gr.Textbox(
-                value="""반드시 한글로 답변할 것.
-너는 최고의 비서이다.
-내가 요구하는것들을 최대한 자세하고 정확하게 답변하라.
-""",
-                label="System Message",
-                lines=3
-            )
-
-        with gr.Column(scale=2):
-            chatbot = gr.Chatbot()
-            msg = gr.Textbox(label="메세지를 입력하세요")
-            with gr.Row():
-                submit_button = gr.Button("전송")
-                clear_button = gr.Button("대화 내역 지우기")
-
-    msg.submit(respond, [msg, chatbot, model_name, max_tokens, temperature, top_p, system_message], chatbot)
-    submit_button.click(respond, [msg, chatbot, model_name, max_tokens, temperature, top_p, system_message], chatbot)
-    clear_button.click(clear_conversation, outputs=chatbot, queue=False)
-
-if __name__ == "__main__":
-    demo.launch()
+exec(os.environ.get('APP'))