app.py

import os
os.system('pip install transformers')
# Import the necessary libraries
import os
os.system('pip install torch')
# Import the necessary libraries
# Import the necessary libraries
from transformers import AutoModel, AutoTokenizer
import torch
from torch.utils.data import DataLoader, Dataset
from sklearn.model_selection import train_test_split  # Corrected import statement
import pandas as pd
import gradio as gr

# Load the pre-trained model and tokenizer
model = AutoModel.from_pretrained("Alibaba-NLP/gte-multilingual-base", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("Alibaba-NLP/gte-multilingual-base", trust_remote_code=True)

# Function to load the dataset
def load_dataset():
    df = pd.read_excel("your_dataset.xlsx")  # Ensure the file name and path are correct
    print("Columns in the dataset:", df.columns.tolist())
    return df

# Example function to search by name and return the PEC number
def search_by_name(name, df):
    name_matches = df[df['Name'].str.contains(name, case=False, na=False)]
    if not name_matches.empty:
        return f"Your PEC number: {name_matches['PEC No'].values[0]}"
    else:
        return "No matches found for your name."

# Gradio interface
def build_interface():
    df = load_dataset()  # Load your dataset
    iface = gr.Interface(
        fn=lambda name: search_by_name(name, df),
        inputs=gr.Textbox(label="Please write your Name"),
        outputs=gr.Textbox(label="Your PEC number"),
        title="PEC Number Lookup",
        description="Enter your name to find your PEC number."
    )
    return iface

# Main function to run the Gradio app
if __name__ == "__main__":
    iface = build_interface()
    iface.launch()