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| import os | |
| import subprocess | |
| # Function to install a package if it is not already installed | |
| def install(package): | |
| subprocess.check_call([os.sys.executable, "-m", "pip", "install", package]) | |
| # Ensure the necessary packages are installed | |
| install("transformers") | |
| install("torch") | |
| install("pandas") | |
| install("gradio") | |
| install("openpyxl") # Added installation for openpyxl | |
| import pandas as pd | |
| import gradio as gr | |
| from transformers import AutoModel, AutoTokenizer | |
| import torch | |
| # Load the model and tokenizer from Hugging Face | |
| tokenizer = AutoTokenizer.from_pretrained("Alibaba-NLP/gte-multilingual-base", trust_remote_code=True) | |
| model = AutoModel.from_pretrained("Alibaba-NLP/gte-multilingual-base", trust_remote_code=True) | |
| # Load the dataset containing PEC numbers and names | |
| def load_dataset(file_path='PEC_Numbers_and_Names.xlsx'): | |
| if os.path.exists(file_path): | |
| df = pd.read_excel(file_path) | |
| print("File loaded successfully.") | |
| print(df.head()) # Print first few rows for debugging | |
| else: | |
| raise FileNotFoundError(f"File not found: {file_path}") | |
| return df | |
| # Function to get the name based on the PEC number | |
| def get_name(pec_number, df): | |
| df['PEC No.'] = df['PEC No.'].str.strip().str.upper() | |
| pec_number = pec_number.strip().upper() | |
| print(f"Searching for PEC Number: {pec_number}") # Debugging output | |
| result = df[df['PEC No.'] == pec_number] | |
| if not result.empty: | |
| print(f"Found Name: {result.iloc[0]['Name']}") # Debugging output | |
| return result.iloc[0]['Name'] | |
| else: | |
| print("PEC Number not is found.") # Debugging output | |
| return "PEC Number not is found." | |
| # Function to check if the PEC number is attached | |
| def check_pec_number(pec_number, df): | |
| df['PEC No.'] = df['PEC No.'].str.strip().str.upper() | |
| pec_number = pec_number.strip().upper() | |
| if pec_number in df['PEC No.'].values: | |
| return "Your PEC Number is NOT Attached." | |
| else: | |
| return "Your PEC Number is Not Attached." | |
| # Combine the functions to create a prediction | |
| def predict(pec_number): | |
| try: | |
| # Load the dataset from the root directory | |
| df = load_dataset() | |
| name = get_name(pec_number, df) | |
| pec_status = check_pec_number(pec_number, df) | |
| return f"Your Name Is: {name}\n{pec_status}" # Return name and PEC status | |
| except Exception as e: | |
| print(f"An error occurred: {e}") | |
| return f"Error: {e}" | |
| # Build the Gradio interface without the file upload option | |
| iface = gr.Interface( | |
| fn=predict, | |
| inputs=gr.Textbox(lines=1, label="**PEC Number**"), # Bold label for PEC Number | |
| outputs=gr.Textbox(label="Your Name Is:"), # Custom label for the output | |
| title="PEC Number to Name Lookup", | |
| description="Enter a PEC number , Your PEC number is attached with Firm or not" | |
| ) | |
| # Run the Gradio interface | |
| if __name__ == "__main__": | |
| iface.launch() | |