Update app.py

app.py

@@ -17,11 +17,13 @@ GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")
 genai.configure(api_key=GEMINI_API_KEY)
 model = genai.GenerativeModel("gemini-2.0-flash-thinking-exp-01-21")
 
+
 def get_headers():
     if not HF_TOKEN:
         raise ValueError("Hugging Face token not found in environment variables")
     return {"Authorization": f"Bearer {HF_TOKEN}"}
 
+
 def get_file_content(space_id: str, file_path: str) -> str:
     file_url = f"https://huggingface.co/spaces/{space_id}/raw/main/{file_path}"
     try:
@@ -33,6 +35,7 @@ def get_file_content(space_id: str, file_path: str) -> str:
     except requests.RequestException:
         return f"Error fetching content for file: {file_path}"
 
+
 def get_space_structure(space_id: str) -> Dict:
     try:
         files = hf_api.list_repo_files(repo_id=space_id, repo_type="space")
@@ -59,55 +62,78 @@ def get_space_structure(space_id: str) -> Dict:
         print(f"Error in get_space_structure: {str(e)}")
         return {"error": f"API request error: {str(e)}"}
 
+
 def format_tree_structure(tree_data: Dict, indent: str = "") -> str:
     if "error" in tree_data:
         return tree_data["error"]
     formatted = f"{indent}{'📁' if tree_data.get('type') == 'directory' else '📄'} {tree_data.get('name', 'Unknown')}\n"
     if tree_data.get("type") == "directory":
+        # 디렉토리를 먼저, 파일을 나중에 표시하기 위해 정렬 조건 사용
         for child in sorted(tree_data.get("children", []), key=lambda x: (x.get("type", "") != "directory", x.get("name", ""))):
             formatted += format_tree_structure(child, indent + "  ")
     return formatted
 
+
 def analyze_space(url: str, progress=gr.Progress()):
+    """
+    HuggingFace Space의 app.py와 파일구조 등을 불러와서:
+    1) 코드 요약
+    2) 코드 분석
+    3) 사용법
+    등을 반환합니다.
+    """
     try:
         space_id = url.split('spaces/')[-1]
         if not re.match(r'^[\w.-]+/[\w.-]+$', space_id):
             raise ValueError(f"Invalid Space ID format: {space_id}")
+
         progress(0.1, desc="파일 구조 분석 중...")
         tree_structure = get_space_structure(space_id)
         if "error" in tree_structure:
             raise ValueError(tree_structure["error"])
         tree_view = format_tree_structure(tree_structure)
+
         progress(0.3, desc="app.py 내용 가져오는 중...")
         app_content = get_file_content(space_id, "app.py")
+
         progress(0.5, desc="코드 요약 중...")
         summary = summarize_code(app_content)
+
         progress(0.7, desc="코드 분석 중...")
         analysis = analyze_code(app_content)
+
         progress(0.9, desc="사용법 설명 생성 중...")
         usage = explain_usage(app_content)
+
         # lines 수 조정
         lines_for_app_py = adjust_lines_for_code(app_content)
+
         progress(1.0, desc="완료")
         return app_content, tree_view, tree_structure, space_id, summary, analysis, usage, lines_for_app_py
+
     except Exception as e:
         print(f"Error in analyze_space: {str(e)}")
         print(traceback.format_exc())
         return f"오류가 발생했습니다: {str(e)}", "", None, "", "", "", "", 10
 
+
 def adjust_lines_for_code(code_content: str, min_lines: int = 10, max_lines: int = 100) -> int:
+    """
+    코드의 줄 수에 맞춰 표시할 lines 수를 동적으로 조정합니다.
+    """
     num_lines = len(code_content.split('\n'))
     return min(max(num_lines, min_lines), max_lines)
 
-# -----------------------------
-# LLM (Gemini) 관련 함수
-# -----------------------------
+
+# --------------------------------------------------
+# Gemini 2.0 Flash Thinking 모델 관련 (LLM) 함수들
+# --------------------------------------------------
 from gradio import ChatMessage
 
 def format_chat_history(messages: List[ChatMessage]) -> List[Dict]:
     """
     ChatMessage 목록을 Gemini 모델이 이해할 수 있는 형식으로 변환
-    (Thinking 메타데이터 포함 메시지는 제외)
+    (Thinking 메타데이터 포함 메시지는 무시)
     """
     formatted = []
     for m in messages:
@@ -118,15 +144,12 @@ def format_chat_history(messages: List[ChatMessage]) -> List[Dict]:
         formatted.append({"role": role, "parts": [m.content or ""]})
     return formatted
 
-import google.generativeai as genai
 
-def gemini_chat_completion(
-    system_message: str, 
-    user_message: str, 
-    max_tokens: int = 200, 
-    temperature: float = 0.7
-) -> str:
-    # ChatMessage 객체로 변환
+def gemini_chat_completion(system_message: str, user_message: str, max_tokens: int = 200, temperature: float = 0.7) -> str:
+    """
+    시스템 메시지와 유저 메시지를 받아 Gemini에 스트리밍 요청, 
+    최종 응답 텍스트를 반환합니다.
+    """
     init_msgs = [
         ChatMessage(role="system", content=system_message),
         ChatMessage(role="user", content=user_message)
@@ -138,15 +161,15 @@ def gemini_chat_completion(
         for chunk in chat.send_message(user_message, stream=True):
             parts = chunk.candidates[0].content.parts
             if len(parts) == 2:
-                # Thinking + 최종응답 형태로 들어온 경우
+                # Thinking + 최종응답
                 final += parts[1].text
             else:
-                # 그냥 응답
                 final += parts[0].text
         return final.strip()
     except Exception as e:
         return f"LLM 호출 중 오류 발생: {str(e)}"
 
+
 def summarize_code(app_content: str):
     system_msg = "당신은 Python 코드를 분석하고 요약하는 AI 조수입니다. 주어진 코드를 3줄 이내로 간결하게 요약해주세요."
     user_msg = f"다음 Python 코드를 3줄 이내로 요약해주세요:\n\n{app_content}"
@@ -155,6 +178,7 @@ def summarize_code(app_content: str):
     except Exception as e:
         return f"요약 생성 중 오류 발생: {str(e)}"
 
+
 def analyze_code(app_content: str):
     # 시스템 프롬프트에 '딥씽킹' 안내문 추가
     system_msg = (
@@ -175,6 +199,7 @@ def analyze_code(app_content: str):
     except Exception as e:
         return f"분석 생성 중 오류 발생: {str(e)}"
 
+
 def explain_usage(app_content: str):
     # 시스템 프롬프트에 '딥씽킹' 안내문 추가
     system_msg = (
@@ -189,21 +214,17 @@ def explain_usage(app_content: str):
     except Exception as e:
         return f"사용법 설명 생성 중 오류 발생: {str(e)}"
 
-# -----------------------------
-# 실제 대화 스트리밍
-# -----------------------------
+
 def stream_gemini_response(user_message: str, conversation_state: List[ChatMessage]) -> Iterator[List[ChatMessage]]:
     """
     conversation_state: ChatMessage 객체로만 이루어진 '대화 이력' (Gradio State).
+    (수정) 빈 문자열이어도 처리하도록 변경. 에러를 띄우지 않음.
     """
-    if not user_message.strip():
-        conversation_state.append(
-            ChatMessage(role="assistant", content="Please provide a non-empty text message. Empty input is not allowed.")
-        )
-        yield conversation_state
-        return
+    # 기존에는 if not user_message.strip(): ... return 했으나,
+    # "Please provide a non-empty text message..." 오류가 불편하다는 요청으로 제거/완화함.
+    # 필요하다면 user_message가 정말 아무것도 없을 때 처리 로직을 추가하세요.
 
-    print(f"\n=== New Request ===\nUser message: {user_message}")
+    print(f"\n=== New Request ===\nUser message: {user_message if user_message.strip() else '(Empty)'}")
 
     # 기존 대화를 Gemini 형식으로 변환
     chat_history = format_chat_history(conversation_state)
@@ -223,49 +244,57 @@ def stream_gemini_response(user_message: str, conversation_state: List[ChatMessa
         )
     )
 
-    for chunk in response:
-        parts = chunk.candidates[0].content.parts
-        current_chunk = parts[0].text
-
-        if len(parts) == 2 and not thinking_complete:
-            # 첫 번째가 '생각', 두 번째가 최종 응답
-            thought_buffer += current_chunk
-            print(f"\n=== Complete Thought ===\n{thought_buffer}")
-            conversation_state[-1] = ChatMessage(
-                role="assistant",
-                content=thought_buffer,
-                metadata={"title": "⚙️ Thinking: *The thoughts produced by the model are experimental"}
-            )
+    try:
+        for chunk in response:
+            parts = chunk.candidates[0].content.parts
+            current_chunk = parts[0].text
+
+            if len(parts) == 2 and not thinking_complete:
+                thought_buffer += current_chunk
+                print(f"\n=== Complete Thought ===\n{thought_buffer}")
+                conversation_state[-1] = ChatMessage(
+                    role="assistant",
+                    content=thought_buffer,
+                    metadata={"title": "⚙️ Thinking: *The thoughts produced by the model are experimental"}
+                )
+                yield conversation_state
+
+                response_buffer = parts[1].text
+                print(f"\n=== Starting Response ===\n{response_buffer}")
+                conversation_state.append(
+                    ChatMessage(role="assistant", content=response_buffer)
+                )
+                thinking_complete = True
+
+            elif thinking_complete:
+                response_buffer += current_chunk
+                print(f"\n=== Response Chunk ===\n{current_chunk}")
+                conversation_state[-1] = ChatMessage(
+                    role="assistant",
+                    content=response_buffer
+                )
+            else:
+                thought_buffer += current_chunk
+                print(f"\n=== Thinking Chunk ===\n{current_chunk}")
+                conversation_state[-1] = ChatMessage(
+                    role="assistant",
+                    content=thought_buffer,
+                    metadata={"title": "⚙️ Thinking: *The thoughts produced by the model are experimental"}
+                )
             yield conversation_state
 
-            response_buffer = parts[1].text
-            print(f"\n=== Starting Response ===\n{response_buffer}")
-            conversation_state.append(
-                ChatMessage(role="assistant", content=response_buffer)
-            )
-            thinking_complete = True
-
-        elif thinking_complete:
-            # 이미 생각이 끝났으므로 최종 응답 누적
-            response_buffer += current_chunk
-            print(f"\n=== Response Chunk ===\n{current_chunk}")
-            conversation_state[-1] = ChatMessage(
-                role="assistant",
-                content=response_buffer
-            )
+        print(f"\n=== Final Response ===\n{response_buffer}")
 
-        else:
-            # 아직 생각 중
-            thought_buffer += current_chunk
-            print(f"\n=== Thinking Chunk ===\n{current_chunk}")
-            conversation_state[-1] = ChatMessage(
+    except Exception as e:
+        print(f"\n=== Error ===\n{str(e)}")
+        conversation_state.append(
+            ChatMessage(
                 role="assistant",
-                content=thought_buffer,
-                metadata={"title": "⚙️ Thinking: *The thoughts produced by the model are experimental"}
+                content=f"I apologize, but I encountered an error: {str(e)}"
             )
+        )
         yield conversation_state
 
-    print(f"\n=== Final Response ===\n{response_buffer}")
 
 def convert_to_display_tuples(messages: List[ChatMessage]) -> List[Tuple[str, str]]:
     """
@@ -289,28 +318,31 @@ def convert_to_display_tuples(messages: List[ChatMessage]) -> List[Tuple[str, st
             i += 1
     return result
 
+
 def user_submit_message(msg: str, conversation_state: List[ChatMessage]):
     """
     사용자가 메시지를 입력할 때 호출. 
     ChatMessage 리스트(conversation_state)에 user 메시지를 추가한 뒤 반환.
     """
     conversation_state.append(ChatMessage(role="user", content=msg))
+    # 입력창은 비워줌
     return "", conversation_state
 
+
 def respond_wrapper(message: str, conversation_state: List[ChatMessage], max_tokens, temperature, top_p):
     """
     유저 메시지를 받아 Gemini에게 요청(스트리밍)하고, 대화 이력을 업데이트 후
     화면에는 (user, assistant) 튜플을 반환한다.
     """
-    # 스트리밍 응답
     for updated_messages in stream_gemini_response(message, conversation_state):
         # 화면 표시용 (user, assistant) 튜플로 변환
         yield "", convert_to_display_tuples(updated_messages)
 
-# --------------------------------------------------
-# Gradio UI
-# --------------------------------------------------
+
 def create_ui():
+    """
+    Gradio UI를 구성하는 함수
+    """
     try:
         css = """
         footer {visibility: hidden;}
@@ -391,17 +423,17 @@ def create_ui():
                     conversation_state = gr.State([])
 
                     # 이벤트 체인
-                    # 1) 유저 메시지 입력 -> user_submit_message -> (입력창 비우고, ChatMessage추가)
-                    # 2) respond_wrapper 호출 -> Gemini 스트리밍 -> ChatMessage 갱신 -> 튜플 변환하여 chatbot에 표시
+                    # 1) 유저 메시지 -> user_submit_message -> (입력창 비움 + state추가)
+                    # 2) respond_wrapper -> Gemini 스트리밍 -> 대화 state 갱신 -> (user,assistant) 튜플 변환
                     msg.submit(
                         user_submit_message, 
                         inputs=[msg, conversation_state], 
-                        outputs=[msg, conversation_state], 
+                        outputs=[msg, conversation_state],
                         queue=False
                     ).then(
                         respond_wrapper, 
                         inputs=[msg, conversation_state, max_tokens, temperature, top_p], 
-                        outputs=[msg, chatbot],  # msg는 비워주고, chatbot에는 (user,assistant) 튜플 리스트
+                        outputs=[msg, chatbot],  
                     )
 
                 with gr.TabItem("Recommended Best"):
@@ -432,7 +464,7 @@ def create_ui():
                 inputs=[space_id_state],
                 outputs=[requirements_content]
             ).then(
-                lambda lines: gr.update(lines=lines), 
+                lambda lines: gr.update(lines=lines),
                 inputs=[app_py_content_lines],
                 outputs=[app_py_content]
             )
@@ -444,13 +476,26 @@ def create_ui():
         print(traceback.format_exc())
         raise
 
+
 if __name__ == "__main__":
     try:
         print("Starting HuggingFace Space Analyzer...")
         demo = create_ui()
+        print("UI created successfully.")
+        print("Configuring Gradio queue...")
         demo.queue()
-        demo.launch(server_name="0.0.0.0", server_port=7860, share=False, debug=True, show_api=False)
+        print("Gradio queue configured.")
+        print("Launching Gradio app...")
+        demo.launch(
+            server_name="0.0.0.0",
+            server_port=7860,
+            share=False,
+            debug=True,
+            show_api=False
+        )
+        print("Gradio app launched successfully.")
     except Exception as e:
         print(f"Error in main: {str(e)}")
+        print("Detailed error information:")
         print(traceback.format_exc())
         raise