Spaces:
Runtime error
Runtime error
| import pandas as pd | |
| import gradio as gr | |
| from sklearn.feature_extraction.text import TfidfVectorizer | |
| from sklearn.metrics.pairwise import cosine_similarity | |
| from openpyxl import load_workbook | |
| from IPython.display import Markdown | |
| import os | |
| # Initialize empty DataFrame and TF-IDF matrix | |
| data = pd.DataFrame() | |
| tfidf_vectorizer = None | |
| tfidf_matrix = None | |
| # Helper: Load Data from File (Excel or CSV) | |
| def load_file(file): | |
| global data, tfidf_vectorizer, tfidf_matrix | |
| try: | |
| # Read file based on its extension | |
| if file.endswith(".xlsx"): | |
| # Read Excel file | |
| workbook = load_workbook(filename=file, data_only=True) | |
| sheet = workbook.active | |
| headers = [cell.value if cell.value is not None else f"Unnamed Column {i}" for i, cell in enumerate(sheet[1])] | |
| rows = sheet.iter_rows(min_row=2, values_only=True) | |
| data = pd.DataFrame(rows, columns=headers) | |
| # Extract hyperlinks from "Information" column if present | |
| if "Information" in data.columns: | |
| for i, row in enumerate(sheet.iter_rows(min_row=2)): | |
| cell = row[data.columns.get_loc("Information")] | |
| if cell.hyperlink: | |
| data.at[i, "Information"] = cell.hyperlink.target | |
| elif file.endswith(".csv"): | |
| # Read CSV file | |
| data = pd.read_csv(file) | |
| else: | |
| return "Unsupported file format. Please upload a .xlsx or .csv file." | |
| # Initialize TF-IDF for employee search | |
| tfidf_vectorizer = TfidfVectorizer(analyzer="char_wb", ngram_range=(2, 4)) | |
| tfidf_matrix = tfidf_vectorizer.fit_transform(data["Employee Name"].astype(str)) | |
| return "File loaded successfully!" | |
| except Exception as e: | |
| return f"Error loading file: {e}" | |
| # Helper: Generate Report Dynamically | |
| import os | |
| def generate_dynamic_report(query): | |
| if data.empty or tfidf_vectorizer is None: | |
| return None | |
| query_vec = tfidf_vectorizer.transform([query]) | |
| similarities = cosine_similarity(query_vec, tfidf_matrix).flatten() | |
| best_match_idx = similarities.argmax() | |
| if similarities[best_match_idx] > 0: | |
| employee = data.iloc[best_match_idx] | |
| employee_name = employee["Employee Name"] | |
| report_file = f"{employee_name.replace(' ', '_')}_report.html" | |
| # Generate HTML content dynamically | |
| with open(report_file, "w") as file: | |
| file.write(f"<html><head><title>{employee_name} Report</title></head><body>") | |
| file.write(f"<h1>Employee Report for {employee_name}</h1>") | |
| file.write("<hr>") | |
| # Employee Details | |
| file.write("<h2>Employee Details:</h2><ul>") | |
| for column, value in employee.items(): | |
| if column == "Information" and isinstance(value, str): | |
| if value.startswith("http") or value.startswith("file://"): | |
| # Detect file type from hyperlink | |
| file_type = os.path.splitext(value)[-1].lower() | |
| if file_type == ".pdf": | |
| label = "View PDF Document" | |
| elif file_type in [".ppt", ".pptx"]: | |
| label = "View PowerPoint Presentation" | |
| elif file_type in [".doc", ".docx"]: | |
| label = "View Word Document" | |
| else: | |
| label = "Download/View File" | |
| value = f'<a href="{value}" target="_blank" style="color:blue;text-decoration:underline;">{label}</a>' | |
| else: | |
| # Add a clickable link for local paths | |
| value = f'<a href="file:///{value}" target="_blank" style="color:blue;text-decoration:underline;">Download/View File</a>' | |
| file.write(f"<li><strong>{column}:</strong> {value}</li>") | |
| file.write("</ul>") | |
| # Customer Insights | |
| file.write("<h2>Customer Insights:</h2><ul>") | |
| if "Experience" in data.columns: | |
| experience = employee.get("Experience", "No experience details available.") | |
| file.write(f"<li>Experience in the field: {experience}</li>") | |
| if "Skills" in data.columns: | |
| skills = employee.get("Skills", "No skills information available.") | |
| file.write(f"<li>Key skills: {skills}</li>") | |
| if "Projects" in data.columns: | |
| projects = employee.get("Projects", "No projects listed.") | |
| file.write(f"<li>Notable projects: {projects}</li>") | |
| file.write("</ul>") | |
| # Summary | |
| file.write("<h2>Summary:</h2>") | |
| file.write(f"<p>{employee_name} has shown notable contributions in the domain. Refer to the linked documents for more details.</p>") | |
| file.write("<p>Thank you for using our Employee Dashboard!</p>") | |
| file.write("</body></html>") | |
| return report_file | |
| else: | |
| return None | |
| # Helper: Search Employee | |
| def search_employee(query): | |
| if data.empty or tfidf_vectorizer is None: | |
| return pd.DataFrame([{"Error": "No data available. Please upload a file first."}]) | |
| query_vec = tfidf_vectorizer.transform([query]) | |
| similarities = cosine_similarity(query_vec, tfidf_matrix).flatten() | |
| best_match_idx = similarities.argmax() | |
| if similarities[best_match_idx] > 0: | |
| # Ensure output is a valid DataFrame with one row | |
| employee = data.iloc[best_match_idx].to_frame().T # Convert Series to DataFrame | |
| return employee | |
| else: | |
| return pd.DataFrame([{"Error": "No matching employee found."}]) | |
| # Gradio Interface | |
| with gr.Blocks() as interface: | |
| gr.Markdown(""" | |
| <h1 style="text-align: center;">Employee Dashboard</h1> | |
| <p style="text-align: center;">Upload your Excel or CSV file to get started. Search employees, view metrics, and generate dynamic reports.</p> | |
| """) | |
| with gr.Row(): | |
| file_upload = gr.File(label="Upload Excel or CSV File", type="filepath") | |
| upload_status = gr.Textbox(label="Upload Status", interactive=False) | |
| upload_button = gr.Button("Upload") | |
| upload_button.click(load_file, inputs=[file_upload], outputs=[upload_status]) | |
| with gr.Row(): | |
| search_query = gr.Textbox(label="Search Employee", placeholder="Type partial name (e.g., 'Aar')") | |
| employee_details = gr.Dataframe(label="Employee Details", interactive=True) | |
| report_output = gr.File(label="Download Report") | |
| search_button = gr.Button("Search") | |
| search_button.click( | |
| search_employee, | |
| inputs=[search_query], | |
| outputs=[employee_details], | |
| ) | |
| search_button.click( | |
| generate_dynamic_report, | |
| inputs=[search_query], | |
| outputs=[report_output], | |
| ) | |
| if __name__ == "__main__": | |
| interface.launch(share=True) |