app.py

import os
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'):
    df = pd.read_excel(file_path)
    return df

# 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)

# Define the function to get the name based on the PEC number
def get_name(pec_number, df):
    result = df[df['PEC No.'] == pec_number]
    if not result.empty:
        return result.iloc[0]['Name']
    else:
        return "PEC Number not found."

# Function to process the PEC number using the Hugging Face model
def process_with_model(pec_number):
    inputs = tokenizer(pec_number, return_tensors="pt")
    with torch.no_grad():
        outputs = model(**inputs)
    # Here, we simply return the last hidden state as a string representation
    # In a real application, you might want to use this in a more meaningful way
    return outputs.last_hidden_state.mean(dim=1).squeeze().tolist()

# Combine both functions to create a prediction
def predict(pec_number):
    name = get_name(pec_number, df)
    model_output = process_with_model(pec_number)
    return f"Name: {name}\nModel Output: {model_output}"

# Load the dataset
df = load_dataset()

# Build the Gradio interface
iface = gr.Interface(
    fn=predict,
    inputs=gr.Textbox(lines=1, placeholder="Enter PEC Number..."),
    outputs="text",
    title="PEC Number Lookup with Model Integration",
    description="Enter a PEC number to retrieve the corresponding name and process it with a Hugging Face model."
)

# Run the Gradio interface
if __name__ == "__main__":
    iface.launch()