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| import streamlit as st | |
| import pandas as pd | |
| from pymongo import MongoClient | |
| from dotenv import load_dotenv | |
| import os | |
| import json | |
| import re | |
| # 1. Load environment variables | |
| load_dotenv() | |
| MONGODB_URI = os.getenv( | |
| "MONGODB_UR", | |
| "mongodb+srv://milind:[email protected]/?retryWrites=true&w=majority&appName=Cluster0", | |
| ) | |
| # 2. Create MongoDB connection | |
| client = MongoClient(MONGODB_URI) | |
| db = client["novascholar_db"] | |
| collection = db["research_papers"] | |
| def convert_mixed_columns(df: pd.DataFrame) -> pd.DataFrame: | |
| """ | |
| Convert any columns that contain lists into comma-separated strings | |
| in order to ensure consistent data types for CSV export. | |
| """ | |
| for col in df.columns: | |
| if any(isinstance(val, list) for val in df[col].dropna()): | |
| df[col] = df[col].apply( | |
| lambda x: ( | |
| ", ".join(map(str, x)) | |
| if isinstance(x, list) | |
| else (str(x) if pd.notna(x) else "") | |
| ) | |
| ) | |
| return df | |
| def filter_and_export_collection_to_csv(keyword: str, doc_collection=None): | |
| """ | |
| Find documents in the given collection with a matching keyword | |
| in the 'Keywords' field, export them to CSV, and return the DataFrame | |
| and CSV filename. | |
| """ | |
| # Use the default 'research_papers' collection if none provided | |
| if doc_collection is None: | |
| doc_collection = collection | |
| docs = list(doc_collection.find({"Keywords": {"$regex": keyword, "$options": "i"}})) | |
| if docs: | |
| df = pd.DataFrame(docs) | |
| df = convert_mixed_columns(df) | |
| csv_filename = "papers_filtered_export.csv" | |
| df.to_csv(csv_filename, index=False) | |
| return df, csv_filename | |
| else: | |
| # Return an empty DataFrame if no documents found | |
| return pd.DataFrame(), None | |
| def main(): | |
| # st.set_page_config(page_title="Filter and Export Papers", layout="wide") | |
| st.title("Filter and Export Papers by Keyword") | |
| # Let user select the paper type | |
| paper_type = st.selectbox( | |
| "Select type of research paper:", | |
| [ | |
| "Review Based Paper", | |
| "Opinion/Perspective Based Paper", | |
| "Empirical Research Paper", | |
| "Research Paper (Other)", | |
| ], | |
| ) | |
| # 5. Let user enter the keyword to filter | |
| keyword_input = st.text_input( | |
| "Enter the exact keyword to filter papers by 'Keywords' field:" | |
| ) | |
| # When user clicks button, use the collection for the selected paper type | |
| if st.button("Export Filtered Papers to CSV"): | |
| with st.spinner("Exporting filtered documents..."): | |
| try: | |
| # Determine dynamic collection based on paper type | |
| collection_name = paper_type.replace(" ", "_").lower() | |
| doc_collection = db[collection_name] | |
| df, csv_filename = filter_and_export_collection_to_csv( | |
| keyword_input, doc_collection | |
| ) | |
| if not df.empty and csv_filename: | |
| st.success( | |
| f"Successfully exported filtered papers to {csv_filename}!" | |
| ) | |
| st.write("Preview of the filtered DataFrame:") | |
| st.dataframe(df) | |
| else: | |
| st.warning("No matching documents found for that keyword.") | |
| except Exception as e: | |
| st.error(f"Error exporting filtered papers: {str(e)}") | |
| if __name__ == "__main__": | |
| main() | |