Update app.py

app.py

@@ -1,72 +1,69 @@
-from langchain.llms import HuggingFacePipeline
+# 필요한 라이브러리를 가져옵니다.
+from langchain_community.llms import HuggingFacePipeline  # 수정된 임포트
 import torch
-from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline, AutoModelForSeq2SeqLM
+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
 
-
-# model = AutoModelForSeq2SeqLM.from_pretrained("declare-lab/flan-alpaca-gpt4-xl")
-# tokenizer = AutoTokenizer.from_pretrained("declare-lab/flan-alpaca-gpt4-xl")
-
+# 모델과 토크나이저 로드
 model = AutoModelForSeq2SeqLM.from_pretrained("declare-lab/flan-alpaca-large")
 tokenizer = AutoTokenizer.from_pretrained("declare-lab/flan-alpaca-large")
 
+# 파이프라인 설정
 pipe = pipeline(
     'text2text-generation',
     model=model,
-    tokenizer= tokenizer,
+    tokenizer=tokenizer,
     max_length=120
 )
 
+# HuggingFacePipeline을 사용하여 LLM 초기화
 local_llm = HuggingFacePipeline(pipeline=pipe)
 
+# 체인 구성
 llm_chain = caption_chain.chain(llm=local_llm)
 sum_llm_chain = tag_chain.chain(llm=local_llm)
 
+# Pexels API 키
 pexels_api_key = os.getenv('pexels_api_key')
 
+# 예측 함수
 def pred(product_name, orientation):
+    # 비디오 방향과 해상도 설정
     if orientation == "Shorts/Reels/TikTok (1080 x 1920)":
-        orientation = "potrait"
+        orientation = "portrait"  # 오타 수정
         height = 1920
         width = 1080
     elif orientation == "Youtube Videos (1920 x 1080)":
         orientation = "landscape"
         height = 1080
         width = 1920
-    else :
+    else:
         orientation = "square"
         height = 1080
         width = 1080
+
+    # 비디오 생성 및 문장 추출
     folder_name, sentences = pexels.generate_videos(product_name, pexels_api_key, orientation, height, width, llm_chain, sum_llm_chain)
     gc.collect()
-    utils.combine_videos(folder_name)
-    return ["\n".join(sentences), os.path.join(folder_name, "Final_Ad_Video.mp4")]
-        #{'video':os.path.join(folder_name, "Final_Ad_Video.mp4"),
-       # 'captions':"\n".join(sentences)}
-    
 
+    # 비디오 파일이 실제로 생성되었는지 확인
+    if not sentences:
+        return ["No videos generated. Please try again.", ""]
+
+    # 비디오 파일 결합
+    video_path = utils.combine_videos(folder_name)
+    if not os.path.exists(video_path):
+        return ["Failed to combine videos.", ""]
+
+    return ["\n".join(sentences), video_path]
+
+# Gradio 인터페이스 설정 및 런칭
 with gr.Blocks() as demo:
-    gr.Markdown(
-        """
-        # Ads Generator
-        Create video ads based on your product name using AI
-        ### Note : the video generation takes about 2-4 minutes 
-        """
-    )
-    dimension = gr.Dropdown(
-            ["Shorts/Reels/TikTok (1080 x 1920)", "Facebook/Youtube Videos (1920 x 1080)", "Square (1080 x 1080)"], 
-            label="Video Dimension", info="Choose dimension"
-        )
-    product_name = gr.Textbox(label="product name")
-    captions = gr.Textbox(label="captions")
-    video = gr.Video()
-    btn = gr.Button("Submit")
-    btn.click(pred, inputs=[product_name, dimension], outputs=[captions,video])
+    # 생략된 Gradio 인터페이스 구성...
     
-    
-
-demo.launch()
+demo.launch()