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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")
import pandas as pd
import gradio as gr
from transformers import AutoModel, AutoTokenizer
import torch
# Load the dataset containing PEC numbers and names
def load_dataset(file_path='PEC_Numbers_and_Names.xlsx'):
if not os.path.exists(file_path):
raise FileNotFoundError(f"File not found: {file_path}")
df = pd.read_excel(file_path)
return df
# Debugging function to get PEC number based on the name
def get_pec_number(name, df):
print("Column names in DataFrame:", df.columns.tolist()) # Print the column names
print(f"Looking for Name: '{name}'")
# Normalize the input and dataset
df['Name'] = df['Name'].str.strip().str.lower()
name = name.strip().str.lower()
result = df[df['Name'] == name]
if not result.empty:
print(f"Found PEC Number: {result.iloc[0]['PEC No.']}")
return result.iloc[0]['PEC No.']
else:
print("Name not found.")
return "Name not found."
# Function to process the name using the Hugging Face model
def process_with_model(name):
inputs = tokenizer(name, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
return outputs.last_hidden_state.mean(dim=1).squeeze().tolist()
# Combine both functions to create a prediction
def predict(name, file):
try:
# Load the dataset from the uploaded file if provided
if file is not None:
df = pd.read_excel(file.name)
else:
df = load_dataset()
pec_number = get_pec_number(name, df)
model_output = process_with_model(name)
return f"PEC Number: {pec_number}\nModel Output: {model_output}"
except FileNotFoundError as e:
return str(e)
# 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)
# Build the Gradio interface with file upload option
iface = gr.Interface(
fn=predict,
inputs=[
gr.Textbox(lines=1, placeholder="Enter Name..."),
gr.File(label="Upload PEC Numbers and Names file (optional)")
],
outputs="text",
title="Name to PEC Number Lookup with Model Integration",
description="Enter a name to retrieve the corresponding PEC number and process it with a Hugging Face model. Optionally, upload the Excel file if not found."
)
# Run the Gradio interface
if __name__ == "__main__":
iface.launch()
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