Upload app.py

app.py

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+import streamlit as st
+import os
+import pandas as pd
+from azure.ai.formrecognizer import DocumentAnalysisClient
+from azure.core.credentials import AzureKeyCredential
+from PyPDF2 import PdfReader, PdfWriter
+from io import BytesIO
+
+YOUR_ENDPOINT = os.environ["YOUR_ENDPOINT"]
+YOUR_KEY = os.environ["YOUR_KEY"]
+
+st.set_page_config(
+    page_title="PDF Table Extractor",
+    layout="centered",
+    initial_sidebar_state="auto"
+)
+
+document_analysis_client = DocumentAnalysisClient(
+    endpoint=YOUR_ENDPOINT,
+    credential=AzureKeyCredential(YOUR_KEY)
+)
+
+# Function to convert table cells to pandas DataFrame
+
+
+def table2pandas(table):
+    data = []
+    for cell in table.cells:
+        while len(data) <= cell.row_index:
+            data.append([])
+        while len(data[cell.row_index]) <= cell.column_index:
+            data[cell.row_index].append("")
+        data[cell.row_index][cell.column_index] = cell.content
+    return pd.DataFrame(data)
+
+# Function to split PDF into pages
+
+
+def split_pdf_to_pages(filepath):
+    input_pdf = PdfReader(filepath)
+    pages = []
+    for page_num in range(len(input_pdf.pages)):
+        output_pdf = PdfWriter()
+        output_pdf.add_page(input_pdf.pages[page_num])
+        page_stream = BytesIO()
+        output_pdf.write(page_stream)
+        page_stream.seek(0)
+        pages.append(page_stream.read())
+    return pages
+
+# Streamlit app
+
+
+def main():
+    st.title("PDF Table Extractor")
+
+    # Upload PDF file
+    uploaded_file = st.file_uploader("Upload a PDF", type=["pdf"])
+
+    if uploaded_file is not None:
+        # Temporarily save uploaded PDF
+        os.makedirs("temp_files", exist_ok=True)
+        temp_filepath = os.path.join("temp_files", uploaded_file.name)
+        with open(temp_filepath, "wb") as f:
+            f.write(uploaded_file.getbuffer())
+
+        st.text("Uploaded successfully. Extracting tables...")
+
+        # Process the uploaded PDF
+        pages = split_pdf_to_pages(temp_filepath)
+        for page_num, page_bytes in enumerate(pages):
+            poller = document_analysis_client.begin_analyze_document(
+                "prebuilt-layout", document=page_bytes)
+            result = poller.result()
+
+            if hasattr(result, 'tables') and result.tables:
+                for table_num, table in enumerate(result.tables):
+                    table_df = table2pandas(table)
+                    st.write(table_df)  # Display table in Streamlit (optional)
+
+                    # Provide a download link for the CSV file
+                    csv_file = table_df.to_csv(index=False).encode('utf-8')
+                    st.download_button(
+                        label="Download CSV",
+                        data=csv_file,
+                        file_name=f"{os.path.basename(uploaded_file.name).replace('.pdf', '')}_page{page_num + 1}_table{table_num}.csv",
+                        mime="text/csv"
+                    )
+
+        st.success("Tables extracted and saved successfully!")
+
+
+if __name__ == "__main__":
+    main()