Upload app.py

app.py

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+# V5 engg upload
+
+import streamlit as st
+import pandas as pd
+import tabula
+import os
+from io import BytesIO
+
+# Engineering Result Type 1 Functions
+def extract_engineering_result(pdf_path):
+    try:
+        df = tabula.read_pdf(pdf_path, pages='all', multiple_tables=True)
+        return df
+    except Exception as e:
+        st.error(f"Error extracting data from Engineering PDF: {e}")
+        return None
+
+# HSC Result Function
+def extract_hsc_result(pdf_path):
+    try:
+        df = tabula.read_pdf(pdf_path, pages='all')
+        return df
+    except Exception as e:
+        st.error(f"Error extracting data from HSC PDF: {e}")
+        return None
+
+# Diploma Result Function
+def extract_diploma_result(pdf_path):
+    try:
+        df = tabula.read_pdf(pdf_path, pages='all')
+        return df
+    except Exception as e:
+        st.error(f"Error extracting data from Diploma PDF: {e}")
+        return None
+
+# Streamlit App
+def main():
+    st.title("PDF Result Converter")
+
+    # File Upload
+    uploaded_file = st.file_uploader("Upload a PDF file", type=["pdf"])
+
+    if uploaded_file is not None:
+        file_details = {"FileName": uploaded_file.name, "FileType": uploaded_file.type}
+        st.write(file_details)
+
+        # Determine which type of PDF and call the appropriate extraction function
+        if "engineering" in uploaded_file.name.lower() or "engg" in uploaded_file.name.lower():
+            extracted_data = extract_engineering_result(uploaded_file)
+        elif "hsc" in uploaded_file.name.lower():
+            extracted_data = extract_hsc_result(uploaded_file)
+        elif "diploma" in uploaded_file.name.lower():
+            extracted_data = extract_diploma_result(uploaded_file)
+        else:
+            st.error("Unsupported PDF type. Please upload a valid PDF.")
+            return
+
+        # Concatenate all extracted DataFrames into a single DataFrame
+        if extracted_data is not None and isinstance(extracted_data, list):
+            combined_df = pd.concat(extracted_data, ignore_index=True)
+        elif extracted_data is not None and isinstance(extracted_data, pd.DataFrame):
+            combined_df = extracted_data
+        else:
+            st.error("No data extracted or extraction failed. Please check the PDF file and extraction logic.")
+            return
+
+        # Display the extracted data (for debugging purposes)
+        st.subheader("Combined Extracted Data:")
+        st.write(combined_df)
+
+        # Convert to Excel and create download link
+        if st.button("Convert to Excel"):
+            output = BytesIO()
+            excel_writer = pd.ExcelWriter(output, engine='xlsxwriter')
+            combined_df.to_excel(excel_writer, index=False, sheet_name='Sheet1')
+            excel_writer.close()
+            excel_data = output.getvalue()
+            output.seek(0)
+
+            # Provide a download button for the generated Excel file
+            st.download_button(
+                label="Download Excel File",
+                data=excel_data,
+                file_name=f"{uploaded_file.name.split('.')[0]}.xlsx",
+                mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
+                key="download_excel"
+            )
+
+if __name__ == "__main__":
+    main()