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# Install necessary libraries
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("scikit-learn")
install("gradio")
# 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
import pandas as pd
import gradio as gr
import os
# 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():
# Use the uploaded file path
file_path = "Valid-part-2.xlsx"
if not os.path.exists(file_path):
raise FileNotFoundError(f"Dataset not found. Please ensure that '{file_path}' exists.")
df = pd.read_excel(file_path) # Load the Excel file
print("Columns in the dataset:", df.columns.tolist())
return df
# 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 number'].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__":
try:
iface = build_interface()
iface.launch()
except FileNotFoundError as e:
print(str(e))
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