app.py

import streamlit as st
from transformers import AutoModelForTokenClassification, AutoTokenizer
import torch

# Load the model and tokenizer from Hugging Face
model_name = "fajjos/Keyword_v1"  # Replace with the actual model name
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForTokenClassification.from_pretrained(model_name)

# Streamlit interface
st.title("Keyword Extractor")
user_input = st.text_area("Enter text for keyword extraction")

if user_input:
    # Tokenize the input
    inputs = tokenizer(user_input, return_tensors="pt")
    
    # Get model predictions
    with torch.no_grad():
        outputs = model(**inputs)
    
    # Process the predictions (this will depend on your specific model output)
    tokens = tokenizer.convert_ids_to_tokens(inputs['input_ids'][0])
    predictions = torch.argmax(outputs.logits, dim=2)

    # Display extracted keywords
    st.write("Extracted Keywords:")
    for token, pred in zip(tokens, predictions[0]):
        if pred == 1:  # Assuming label '1' corresponds to a keyword
            st.write(token)

# # Add a slider for interaction (example)
# x = st.slider('Select a value')
# st.write(f"{x} squared is {x * x}")