Update app.py

app.py

@@ -1,21 +1,23 @@
-# CHATGPT API로 변경
-
 # ──────────────────────────────── Imports ────────────────────────────────
 import os, json, re, logging, requests, markdown, time, io
 from datetime import datetime
 
 import streamlit as st
-import anthropic
+# >>> Anthropic 부분 삭제
+# import anthropic
+from openai import OpenAI  # 커스텀 래퍼(또는 별도 라이브러리)라고 가정
+
 from gradio_client import Client
 import pandas as pd
 import PyPDF2  # For handling PDF files
 
 # ──────────────────────────────── Environment Variables / Constants ─────────────────────────
-ANTHROPIC_KEY  = os.getenv("API_KEY", "")
+# 기존 ANTHROPIC_KEY -> OPENAI_API_KEY 로 변경
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY", "")  
 BRAVE_KEY      = os.getenv("SERPHOUSE_API_KEY", "")  # Keep this name
 BRAVE_ENDPOINT = "https://api.search.brave.com/res/v1/web/search"
 IMAGE_API_URL  = "http://211.233.58.201:7896"
-MAX_TOKENS     = 7_999
+MAX_TOKENS     = 7999
 
 # Blog template and style definitions (in English)
 BLOG_TEMPLATES = {
@@ -45,10 +47,18 @@ EXAMPLE_TOPICS = {
 logging.basicConfig(level=logging.INFO,
                     format="%(asctime)s - %(levelname)s - %(message)s")
 
-# ──────────────────────────────── Anthropic Client ────────────────────────
+# ──────────────────────────────── OpenAI Client ──────────────────────────
 @st.cache_resource
-def get_anthropic_client():
-    return anthropic.Anthropic(api_key=ANTHROPIC_KEY)
+def get_openai_client():
+    """
+    커스텀 OpenAI 객체를 생성한다고 가정합니다. 
+    실제로는 openai.api_key = OPENAI_API_KEY 로만 설정하는 경우가 많습니다.
+    """
+    if not OPENAI_API_KEY:
+        raise RuntimeError("⚠️ OPENAI_API_KEY 환경 변수가 설정되지 않았습니다.")
+    # 여기서는 예시로서 다음과 같이 초기화:
+    client = OpenAI(api_key=OPENAI_API_KEY)
+    return client
 
 # ──────────────────────────────── Blog Creation System Prompt ─────────────
 def get_system_prompt(template="ginigen", tone="professional", word_count=1750, include_search_results=False, include_uploaded_files=False) -> str:
@@ -514,19 +524,55 @@ def extract_image_prompt(blog_text: str, topic: str):
     Analyze the blog content (blog_text) to generate a one-line English image prompt
     related to the topic.
     """
-    client = get_anthropic_client()
-    sys = f"Generate a single-line English image prompt from the following text:\nTopic: {topic}"
+    # 기존 anthropic 클라이언트 사용 -> OpenAI 호출로 대체
+    client = get_openai_client()
+
+    system_msg = (
+        f"Generate a single-line English image prompt from the following text:\n"
+        f"Topic: {topic}\n\n"
+        f"---\n"
+        f"{blog_text}\n\n"
+        f"---\n"
+        f"Return only the prompt text, nothing else."
+    )
+
+    # 실제로는 openai API를 어떻게 호출하느냐에 따라 달라집니다.
+    # 여기서는 예시로 client.responses.create()를 사용한다고 가정
     try:
-        # Simple one-time call
-        res = client.messages.create(
-            model="claude-3-7-sonnet-20250219",
-            max_tokens=80,
-            system=sys,
-            messages=[{"role": "user", "content": blog_text}]
+        response = client.responses.create(
+            model="gpt-4.1-mini",
+            input=[
+                {
+                    "role": "system",
+                    "content": [
+                        {
+                            "type": "input_text",
+                            "text": system_msg
+                        }
+                    ]
+                }
+            ],
+            text={"format": {"type": "text"}},
+            temperature=1,
+            max_output_tokens=80,
+            top_p=1
         )
-        return res.content[0].text.strip()
-    except Exception:
-        # Fallback prompt
+        # 예시: response 객체에서 첫 번째 출력만 가져온다고 가정
+        # response 구조는 사용 중인 래퍼에 따라 다릅니다.
+        content = ""
+        if "responses" in dir(response):
+            # 가상 예시: response.responses[0].content[0].text
+            # 또는 response["choices"][0]["text"] 형태일 수도 있음
+            first_resp = response.responses[0]  # 가정
+            # 본 예시는 "content" 필드가 list로 있고, 그 중 [0]["text"]에 값이 있다고 가정
+            content = first_resp.content[0]["text"].strip()
+        else:
+            content = "A professional photo related to the topic, high quality"
+
+        return content
+
+    except Exception as e:
+        logging.error(f"OpenAI image prompt generation error: {e}")
         return f"A professional photo related to {topic}, high quality"
 
 def md_to_html(md: str, title="Ginigen Blog"):
@@ -544,7 +590,8 @@ def ginigen_app():
 
     # Set default session state
     if "ai_model" not in st.session_state:
-        st.session_state.ai_model = "claude-3-7-sonnet-20250219"
+        # 기존 anthropic 모델명 대신, gpt-4.1-mini
+        st.session_state.ai_model = "gpt-4.1-mini"
     if "messages" not in st.session_state:
         st.session_state.messages = []
     if "auto_save" not in st.session_state:
@@ -710,7 +757,7 @@ def process_example(topic):
     process_input(topic, [])
 
 def process_input(prompt: str, uploaded_files):
-    # Add user's message if it doesn't already exist
+    # Add user's message
     if not any(m["role"] == "user" and m["content"] == prompt for m in st.session_state.messages):
         st.session_state.messages.append({"role": "user", "content": prompt})
 
@@ -725,7 +772,7 @@ def process_input(prompt: str, uploaded_files):
         has_uploaded_files = bool(uploaded_files) and len(uploaded_files) > 0
         
         try:
-            client = get_anthropic_client()
+            client = get_openai_client()
             
             # Prepare conversation messages
             messages = [{"role": m["role"], "content": m["content"]} for m in st.session_state.messages]
@@ -752,7 +799,7 @@ def process_input(prompt: str, uploaded_files):
                 include_uploaded_files=has_uploaded_files
             )
 
-            # If we have file content, append it to the system prompt
+            # 파일 내용이 있다면 system prompt에 추가
             if file_content:
                 sys_prompt += (
                     "\n\n"
@@ -761,7 +808,7 @@ def process_input(prompt: str, uploaded_files):
                     "Ensure the file content is accurately reflected in the blog.\n"
                 )
             
-            # Append additional user message about file usage
+            # 사용자가 입력한 prompt와 파일 참고 메시지
             if has_uploaded_files:
                 extra_user_msg = (
                     f"{prompt}\n\n"
@@ -770,19 +817,53 @@ def process_input(prompt: str, uploaded_files):
                 )
                 messages.append({"role": "user", "content": extra_user_msg})
             
-            # Claude streaming
-            with client.messages.stream(
-                model=st.session_state.ai_model,
-                max_tokens=MAX_TOKENS,
-                system=sys_prompt,
-                messages=messages
-            ) as stream:
-                for t in stream.text_stream:
-                    answer += t or ""
-                    placeholder.markdown(answer + "▌")
-            placeholder.markdown(answer)
+            # 이제 OpenAI client로 요청을 보냅니다.
+            with st.spinner("Generating blog content..."):
+                response = client.responses.create(
+                    model=st.session_state.ai_model,
+                    # OpenAI 래퍼에 맞춰서 대략적으로 재구성한 예입니다.
+                    input=[
+                        {
+                            "role": "system",
+                            "content": [
+                                {
+                                    "type": "input_text",
+                                    "text": sys_prompt
+                                }
+                            ]
+                        },
+                        {
+                            "role": "user",
+                            "content": [
+                                {
+                                    "type": "input_text",
+                                    "text": prompt
+                                }
+                            ]
+                        }
+                        # 필요하면 messages 전체를 넣을 수도 있지만,
+                        # 여기서는 prompt와 system prompt만 넣는 예시
+                    ],
+                    text={"format": {"type": "text"}},
+                    temperature=1,
+                    max_output_tokens=MAX_TOKENS,
+                    top_p=1,
+                    store=True
+                )
+
+                # 예시상 response 객체에서 최종 텍스트를 꺼내는 로직
+                # 실제 구조는 라이브러리 구현에 따라 다릅니다
+                if "responses" in dir(response):
+                    # 가정: response.responses[0].content[0].text 형태
+                    content_blocks = response.responses[0].content
+                    answer = "\n".join(block["text"] for block in content_blocks if block["type"] == "output_text")
+                else:
+                    answer = "Error: Unable to parse the OpenAI response."
             
-            # Image generation option
+            # 스트리밍이 아니므로 ���번에 answer 획득 후 출력
+            placeholder.markdown(answer)
+
+            # 이미지 생성
             answer_entry_saved = False
             if st.session_state.generate_image:
                 with st.spinner("Generating image..."):
@@ -797,8 +878,8 @@ def process_input(prompt: str, uploaded_files):
                             "image_caption": cap
                         })
                         answer_entry_saved = True
-            
-            # Save the answer
+
+            # Save the answer if not saved above
             if not answer_entry_saved:
                 st.session_state.messages.append({"role": "assistant", "content": answer})
 
@@ -827,16 +908,6 @@ def process_input(prompt: str, uploaded_files):
                 except Exception as e:
                     logging.error(f"Auto-save failed: {e}")
 
-        except anthropic.BadRequestError as e:
-            error_message = str(e)
-            if "credit balance is too low" in error_message:
-                placeholder.error("⚠️ Insufficient API credits: Please top up your Anthropic API account.")
-                ans = "Unable to generate blog due to low API credits. Please recharge and try again."
-            else:
-                placeholder.error(f"API request error: {error_message}")
-                ans = f"An error occurred while calling the API: {error_message}"
-            st.session_state.messages.append({"role": "assistant", "content": ans})
-            
         except Exception as e:
             error_message = str(e)
             placeholder.error(f"An error occurred: {error_message}")
@@ -848,4 +919,4 @@ def main():
     ginigen_app()
 
 if __name__ == "__main__":
-    main()
+    main()