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# app.py
import streamlit as st
from transformers import AutoTokenizer, AutoModel
# Load the model and tokenizer
@st.cache_resource
import streamlit as st
from transformers import AutoTokenizer, AutoModel
@st.cache_resource
def load_model():
model_name = "mradermacher/Indian_Legal_Assistant-GGUF"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_auth_token=token)
model = AutoModel.from_pretrained(model_name, use_auth_token=token)
return tokenizer, model
tokenizer, model = load_model()
tokenizer, model = load_model()
# Streamlit App Layout
st.title("Indian Legal Assistant - Hugging Face Spaces Deployment")
st.write("This app provides answers to legal questions using the Indian Legal Assistant model.")
# User input for a legal query
user_input = st.text_area("Enter your legal question:")
if st.button("Generate Response"):
if user_input:
# Tokenize the input
inputs = tokenizer(user_input, return_tensors="pt")
# Generate response
outputs = model.generate(**inputs, max_length=150)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
# Display the response
st.write("### Response:")
st.write(response)
else:
st.write("Please enter a question to get a response.")
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