mock-up

README.md

@@ -0,0 +1 @@
+챗봇을 만들어 봅시다

app/main.py

@@ -5,28 +5,51 @@ from transformers import pipeline
 from PIL import Image
 import re, os
 import gradio as gr
+import torch
 
 app = FastAPI()
 
 # 1. LLM 파이프라인 초기화 (SmolLM3 모델)
-llm = pipeline("text-generation", model="HuggingFaceTB/SmolLM3-3B")
+print("[torch] is available:", torch.cuda.is_available())
+print("[device] default:", torch.device("cuda" if torch.cuda.is_available() else "cpu"))
+llm = pipeline("text-generation", model="HuggingFaceTB/SmolLM3-3B", device=0 if torch.cuda.is_available() else -1)
 
 # 2. 감정 및 상황별 이미지 매핑
-emotion_to_face = {
-    "happy": "aria_happy.png",
-    "sad": "aria_sad.png",
-    "angry": "aria_angry.png",
-    "excited": "aria_excited.png",
-    "nervous": "aria_nervous.png",
-    "neutral": "aria_neutral.png"
-}
-situation_to_bg = {
-    "greeting": "bg_town.jpg",
-    "mission_start": "bg_forest_day.jpg",
-    "enemy_detected": "bg_dungeon_dark.jpg",
-    "spooky_location": "bg_cave.png",
-    "farewell": "bg_sunset.jpg"
+'''
+이미지 매핑 예시
+-----------------------------
+이미지를 다음과 같이 집어넣으면 (./asset/face/)
+happy.png
+sad.png
+angry.png
+=>
+이런 딕셔너리 형식으로 반환됨
+{
+    "happy": "happy.png",
+    "sad": "sad.png",
+    "angry": "angry.png"
 }
+=>
+모델 출력의 감정 부분에 대응되는 이미지 출력
+'''
+def load_faces(face_dir="assets/face"):
+    emotion_to_face = {}
+    for filename in os.listdir(face_dir):
+        if filename.endswith(".png"):
+            emotion = os.path.splitext(filename)[0]  # 'happy.png' → 'happy'
+            emotion_to_face[emotion] = filename      # "happy": "happy.png"
+    return emotion_to_face
+
+def load_bgs(bg_dir="assets/bg"):
+    situation_to_bg = {}
+    for filename in os.listdir(bg_dir):
+        if filename.endswith(".png"):
+            emotion = os.path.splitext(filename)[0]  # 'happy.png' → 'happy'
+            situation_to_bg[emotion] = filename      # "happy": "happy.png"
+    return situation_to_bg
+
+emotion_to_face = load_faces()
+situation_to_bg = load_bgs()
 
 # 3. 출력 라인 파싱 함수
 def parse_output(text: str):
@@ -43,33 +66,58 @@ def parse_output(text: str):
     return results
 
 # 4. 이미지 합성 함수
-def combine_images(bg_path, char_path):
-    bg = Image.open(bg_path).convert("RGBA")
-    char = Image.open(char_path).convert("RGBA")
-    char = char.resize((300, 300))
-    pos = ((bg.width - char.width) // 2, bg.height - char.height - 20)
-    bg.paste(char, pos, char)
+def combine_images(bg_path, face_path):
+    try:
+        bg = Image.open(bg_path).convert("RGBA")
+    except FileNotFoundError:
+        print(f"[warning] 배경 이미지 없음: {bg_path}")
+        return None
+    try:
+        face = Image.open(face_path).convert("RGBA")
+    except FileNotFoundError:
+        print(f"[warning] 캐릭터 이미지 없음: {face_path}")
+        return None
+    # 이미지 합성
+    bg.paste(face, (0, 0), face)
     return bg
 
 # 5. 챗봇 처리 함수 (Gradio용)
-def character_chat(prompt):
+'''
+지금까지 대화 내용을 모두 프롬프트로 넣어서 대화내용을 기억하도록 함
+'''
+def build_prompt(chat_history, user_msg):
     system_prompt = (
         "You are Aria, a cheerful and expressive fantasy mage."
         " Respond in multiple steps if needed."
         " Format: \"text\" (emotion: tag, situation: tag)"
     )
-    full_prompt = system_prompt + "\nUser: " + prompt + "\nAria:"
 
-    raw_output = llm(full_prompt, max_new_tokens=300)[0]['generated_text']
+    dialogue = ""
+    for item in chat_history:
+        if item["role"] == "user":
+            dialogue += f"User: {item['text']}\n"
+        elif item["role"] == "bot":
+            dialogue += f"Aria: {item['text']}\n"
+
+    dialogue += f"User: {user_msg}\nAria:"
+    return system_prompt + "\n" + dialogue
+
+def character_chat(prompt):
+    full_prompt = build_prompt(chat_history, prompt)
+
+    #raw_output = llm(full_prompt, max_new_tokens=300)[0]['generated_text']
+    raw_output = '"우오아" (emotion: tag, situation: tag)'
     parsed = parse_output(raw_output)
 
     result_outputs = []
     for i, item in enumerate(parsed):
-        face = emotion_to_face.get(item['emotion'], "aria_neutral.png")
-        bg = situation_to_bg.get(item['situation'], "bg_default.jpg")
+        face = emotion_to_face.get(item['emotion'], "neutral.png")
+        bg = situation_to_bg.get(item['situation'], "default.jpg")
         composite = combine_images(os.path.join("assets/bg", bg), os.path.join("assets/face", face))
-        img_path = f"static/output_{i}.png"
-        composite.save(img_path)
+        img_path = None #이미지가 없으면 출력 안함
+        if composite:
+            img_path = f"static/output_{i}.png"
+            composite.save(img_path)
         result_outputs.append((item['text'], img_path))
 
     return result_outputs
@@ -83,27 +131,45 @@ with gr.Blocks(css="""
 .bubble-right { background-color: #d1e7ff; border-radius: 10px; padding: 10px; margin: 5px; max-width: 70%; float: right; clear: both; text-align: right; }
 .image-preview { margin: 5px 0; max-width: 100%; border-radius: 10px; }
 """) as demo:
-    gr.Markdown("# Aria 캐릭터 챗봇")
+    gr.Markdown("챗봇")
     with gr.Column():
         chat_output = gr.HTML(value="<div class='chat-box' id='chat-box'></div>")
         user_input = gr.Textbox(label="Say something to Aria", placeholder="Type here and press Enter")
 
     def render_chat():
-        html = "<div class='chat-box'>"
+        html = ""
         for item in chat_history:
             if item['role'] == 'user':
                 html += f"<div class='bubble-right'>{item['text']}</div>"
             elif item['role'] == 'bot':
-                html += f"<div class='bubble-left'>{item['text']}<br><img class='image-preview' src='{item['image']}'></div>"
-        html += "</div>"
+                bubble = f"<div class='bubble-left'>{item['text']}"
+                if 'image' in item and item['image']:
+                    bubble += f"<br><img class='image-preview' src='{item['image']}'>"
+                bubble += "</div>"
+                html += bubble
         return html
 
     def on_submit(user_msg):
         chat_history.append({"role": "user", "text": user_msg})
+
         bot_results = character_chat(user_msg)
-        for text, image_path in bot_results:
-            chat_history.append({"role": "bot", "text": text, "image": image_path})
-        return render_chat(), ""
+
+        for item in bot_results:
+            try:
+                text, image_path = item  # unpack 시도
+            except (ValueError, TypeError):
+                # unpack 안되면 기본값 처리 (이미지 없이)
+                text = str(item)
+                image_path = None
+
+            chat_entry = {"role": "bot", "text": text}
+            if image_path:
+                chat_entry["image"] = image_path
+
+            chat_history.append(chat_entry)
+
+        new_chat_html = render_chat()
+        return f"<div class='chat-box' id='chat-box'>{new_chat_html}</div>", ""
 
     user_input.submit(on_submit, inputs=user_input, outputs=[chat_output, user_input])
 

todo.txt

@@ -0,0 +1,4 @@
+💡 추가 팁
+텍스트를 전부 저장해서 prompt에 누적할 수도 있지만,
+너무 길어지면 이전 내용을 요약하거나, 중요한 발언만 남기는 요약 기억 방식(memory compression) 도 고려해볼 수 있어요.
+필요하면 요약 기억 방식도 도와드릴게요!