Update app.py

app.py

@@ -1,64 +1,156 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+import gc
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+class ModelManager:
+    def __init__(self):
+        self.model = None
+        self.tokenizer = None
+        self.model_name = "CohereForAI/c4ai-command-r-plus-4bit"
+    
+    def load_model(self):
+        if self.model is None:
+            try:
+                print("모델 로딩 중... 시간이 걸릴 수 있습니다.")
+                self.tokenizer = AutoTokenizer.from_pretrained(self.model_name)
+                self.model = AutoModelForCausalLM.from_pretrained(
+                    self.model_name,
+                    torch_dtype=torch.float16,
+                    device_map="auto",
+                    trust_remote_code=True,
+                    load_in_4bit=True,
+                    low_cpu_mem_usage=True
+                )
+                print("모델 로딩 완료!")
+                return True
+            except Exception as e:
+                print(f"모델 로딩 실패: {e}")
+                return False
+        return True
+    
+    def generate(self, message, history, max_tokens=1000, temperature=0.7):
+        if not self.load_model():
+            return "모델 로딩에 실패했습니다."
+        
+        try:
+            # 채팅 히스토리 구성
+            conversation = []
+            for human, assistant in history:
+                conversation.append({"role": "user", "content": human})
+                if assistant:
+                    conversation.append({"role": "assistant", "content": assistant})
+            conversation.append({"role": "user", "content": message})
+            
+            # 토큰화
+            input_ids = self.tokenizer.apply_chat_template(
+                conversation,
+                return_tensors="pt",
+                add_generation_prompt=True
+            )
+            
+            if torch.cuda.is_available():
+                input_ids = input_ids.to("cuda")
+            
+            # 생성
+            with torch.no_grad():
+                outputs = self.model.generate(
+                    input_ids,
+                    max_new_tokens=max_tokens,
+                    temperature=temperature,
+                    do_sample=True,
+                    pad_token_id=self.tokenizer.eos_token_id,
+                    eos_token_id=self.tokenizer.eos_token_id
+                )
+            
+            response = self.tokenizer.decode(
+                outputs[0][input_ids.shape[-1]:], 
+                skip_special_tokens=True
+            )
+            
+            return response
+            
+        except Exception as e:
+            return f"생성 중 오류 발생: {str(e)}"
+        finally:
+            # 메모리 정리
+            if torch.cuda.is_available():
+                torch.cuda.empty_cache()
+            gc.collect()
 
+# 모델 매니저 인스턴스
+model_manager = ModelManager()
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
+def chat_fn(message, history, max_tokens, temperature):
+    if not message.strip():
+        return history, ""
+    
+    # 사용자 메시지 추가
+    history.append([message, "생성 중..."])
+    
+    # 봇 응답 생성
+    response = model_manager.generate(message, history[:-1], max_tokens, temperature)
+    history[-1][1] = response
+    
+    return history, ""
 
+# Gradio 인터페이스
+with gr.Blocks(title="Command R+ Chat") as demo:
+    gr.Markdown("""
+    # 🤖 Command R+ 4bit 채팅봇
+    
+    Cohere의 Command R+ 4bit 양자화 모델과 대화할 수 있습니다.
+    ⚠️ 첫 실행 시 모델 로딩에 시간이 걸릴 수 있습니다.
+    """)
+    
+    chatbot = gr.Chatbot(
+        height=500,
+        show_label=False,
+        show_copy_button=True
+    )
+    
+    with gr.Row():
+        msg = gr.Textbox(
+            label="메시지 입력",
+            placeholder="Command R+에게 질문하세요...",
+            lines=2,
+            scale=4
+        )
+        submit = gr.Button("전송 📤", variant="primary", scale=1)
+        
+    with gr.Row():
+        clear = gr.Button("대화 초기화 🗑️")
+    
+    with gr.Accordion("고급 설정", open=False):
+        max_tokens = gr.Slider(
+            minimum=100, 
+            maximum=2000, 
+            value=1000, 
+            step=100,
+            label="최대 토큰 수"
+        )
+        temperature = gr.Slider(
+            minimum=0.1, 
+            maximum=1.0, 
+            value=0.7, 
+            step=0.1,
+            label="Temperature (창의성)"
+        )
+    
+    # 이벤트 핸들러
+    msg.submit(
+        chat_fn,
+        [msg, chatbot, max_tokens, temperature],
+        [chatbot, msg]
+    )
+    
+    submit.click(
+        chat_fn,
+        [msg, chatbot, max_tokens, temperature],
+        [chatbot, msg]
+    )
+    
+    clear.click(lambda: ([], ""), outputs=[chatbot, msg])
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()