app.py

import os
import sys
import subprocess
import streamlit as st
import io 
import pypdfium2 
from PIL import Image
import logging

# 设置日志记录器
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)


def clone_repo():
    # 从环境变量中获取 GitHub Token
    github_token = os.getenv('GH_TOKEN')

    if github_token is None:
        logger.error("GitHub token is not set. Please set the GH_TOKEN secret in your Space settings.")
        return False
    
    # 使用 GitHub Token 进行身份验证并克隆仓库
    clone_command = f'git clone https://{github_token}@github.com/mamba-ai/invoice_agent.git'
    repo_dir = 'invoice_agent'
    if os.path.exists(repo_dir):
        logger.warning("Repository already exists.")
        # 将仓库路径添加到 Python 模块搜索路径中
        # logger.warning(f"Adding {os.path.abspath(repo_dir)} to sys.path")
        # sys.path.append(os.path.abspath(repo_dir))
        return True
    else:
        logger.info("Cloning repository...")
        result = subprocess.run(clone_command, shell=True, capture_output=True, text=True)
    
    if result.returncode == 0:
        logger.warning("Repository cloned successfully.")
        repo_dir = 'invoice_agent'
        
        # 将仓库路径添加到 Python 模块搜索路径中
        sys.path.append(os.path.abspath(repo_dir))
        logger.warning(f"Adding {os.path.abspath(repo_dir)} to sys.path")
        return True
    else:
        logger.error(f"Failed to clone repository: {result.stderr}")
        return False



if clone_repo():
    # 克隆成功后导入模块    
    import invoice_agent.agent as ia
    # from invoice_agent.agent import load_models, get_ocr_predictions, get_json_result
    
    def open_pdf(pdf_file):
        stream = io.BytesIO(pdf_file.getvalue())
        return pypdfium2.PdfDocument(stream)


    @st.cache_data()
    def get_page_image(pdf_file, page_num, dpi=96):
        doc = open_pdf(pdf_file)
        renderer = doc.render(
            pypdfium2.PdfBitmap.to_pil,
            page_indices=[page_num - 1],
            scale=dpi / 72,
        )
        png = list(renderer)[0]
        png_image = png.convert("RGB")
        return png_image


    @st.cache_data()
    def page_count(pdf_file):
        doc = open_pdf(pdf_file)
        return len(doc)
    
    st.set_page_config(layout="wide")

    models = ia.load_models()

    st.title("""
    受領した請求書を自動で電子化 (Demo)
    """)

    col1, _, col2 = st.columns([.45, 0.1, .45])

    in_file = st.sidebar.file_uploader(
        "PDFファイルまたは画像:", 
        type=["pdf", "png", "jpg", "jpeg", "gif", "webp"],
    )

    if in_file is None:
        st.stop()

    filetype = in_file.type
    whole_image = False
    if "pdf" in filetype:
        page_count = page_count(in_file)
        page_number = st.sidebar.number_input(f"ページ番号 {page_count}:", min_value=1, value=1, max_value=page_count)

        pil_image = get_page_image(in_file, page_number)
    else:
        pil_image = Image.open(in_file).convert("RGB")

    text_rec = st.sidebar.button("認識開始")

    if pil_image is None:
        st.stop()
    
    with col1:
        st.write("## アップロードされたファイル")
        st.image(pil_image, caption="アップロードされたファイル", use_column_width=True)
    
    # if 'json_predictions' in st.session_state:
    #     prev_json_predictions = st.session_state.json_predictions
    #     prev_excel_file_path = st.session_state.excel_file_path
    #     with col2:
    #         st.write("## 結果")
    #         # 提供下载链接
    #         with open(prev_excel_file_path, "rb") as file:
    #             st.download_button(
    #                 label="Download Excel",
    #                 data=file,
    #                 file_name="output.xlsx",
    #                 mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
    #             )
    #         st.write("解析後の内容:")
    #         st.json(prev_json_predictions)
        
    if text_rec:
        with col2:
            st.write("## 結果")
        
            # Placeholder for status indicator
            status_placeholder = st.empty()
        
            with st.spinner('現在ファイルを解析中です'):
                # Simulate model running time
                # time.sleep(5)  # Replace this with actual model running code
                predictions = ia.get_ocr_predictions(pil_image, models)
            
                # Simulate OCR result as a JSON object
                json_predictions = ia.get_json_result(predictions)
                st.session_state.json_predictions = json_predictions
                
                # Convert JSON to Excel
                excel_file_path = "output.xlsx"
                st.session_state.excel_file_path = excel_file_path
                ia.json_to_excel_with_links(json_predictions, excel_file_path)
            
                # After model finishes
                status_placeholder.success('ファイルの解析が完了しました!')

                # 提供下载链接
                with open(excel_file_path, "rb") as file:
                    st.download_button(
                        label="Download Excel",
                        data=file,
                        file_name="output.xlsx",
                        mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
                    )
            
                # Display the result
                st.write("解析後の内容:")
                st.json(json_predictions)
                # st.write(predictions)