Update app.py

app.py

@@ -1,13 +1,12 @@
-# 필요한 라이브러리를 가져옵니다.
-from langchain_community.llms import HuggingFacePipeline  # 수정된 임포트
+from langchain_community.llms import HuggingFacePipeline 
 import torch
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
-
-# 나머지 컴포넌트 임포트 (코드에 따라 다름)
 from components import caption_chain, tag_chain
 from components import pexels, utils
 import os, gc
 import gradio as gr
+import os
+from moviepy.editor import VideoFileClip, concatenate_videoclips
 
 # 모델과 토크나이저 로드
 model = AutoModelForSeq2SeqLM.from_pretrained("declare-lab/flan-alpaca-large")
@@ -31,6 +30,33 @@ sum_llm_chain = tag_chain.chain(llm=local_llm)
 # Pexels API 키
 pexels_api_key = os.getenv('pexels_api_key')
 
+
+def pred(product_name, orientation):
+    # 기존 코드 생략...
+    folder_name, sentences = pexels.generate_videos(product_name, pexels_api_key, orientation, height, width, llm_chain, sum_llm_chain)
+    video_files = [os.path.join(folder_name, f) for f in os.listdir(folder_name) if f.endswith('.mp4')]
+    
+    if not video_files:  # 비디오 파일이 없으면 메시지 반환
+        return ["No videos were generated. Please check the input and try again.", ""]
+    
+    # 비디오 파일 결합
+    video_path = combine_videos(video_files, folder_name)
+    if not video_path:  # 비디오 결합에 실패하면 메시지 반환
+        return ["Failed to combine videos.", ""]
+    
+    return ["\n".join(sentences), video_path]
+
+def combine_videos(video_files, output_folder):
+    if not video_files:
+        print("No video files to combine.")
+        return None
+    
+    clips = [VideoFileClip(vf) for vf in video_files]
+    final_clip = concatenate_videoclips(clips)
+    output_path = os.path.join(output_folder, "final_video.mp4")
+    final_clip.write_videofile(output_path)
+    return output_path
+    
 # 예측 함수
 def pred(product_name, orientation):
     # 비디오 방향과 해상도 설정