app.py

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

# Load model and tokenizer (make sure the model path is correct)
model_name = "ipc_refined_approach_model"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Example sections list (ensure it's aligned with your model)
sections = ['465', '395', '332', '353', '467']

# Save labels to a file
with open("labels.txt", "w") as f:
    f.write("\n".join(sections))  # 'sections' should be a list of section names like ['465', '395', ...].

# Ensure consistency in device setup
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Move the model to the correct device
model = model.to(device)

# Function for prediction
def predict_text(text):
    # Tokenize and encode input text
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
    
    # Move inputs to the same device as the model
    inputs = {key: value.to(device) for key, value in inputs.items()}
    
    # Perform inference
    model.eval()
    with torch.no_grad():
        outputs = model(**inputs)
        logits = outputs.logits  # Ensure logits are accessed correctly
    
    # Apply sigmoid to get probabilities
    probs = torch.sigmoid(logits).detach().cpu().numpy()  # Move to CPU for processing
    
    # Convert probabilities to binary predictions (threshold 0.5)
    predictions = {section: int(prob > 0.5) for section, prob in zip(sections, probs[0])}
    
    # Print the sections the case belongs to
    sections_belongs_to = [section for section, pred in predictions.items() if pred == 1]
    if sections_belongs_to:
        st.write(f"This case belongs to Section(s): **{', '.join(sections_belongs_to)}**")
    else:
        st.write("This case does not belong to any known section.")
    
    return predictions
with open("style.css") as f:
      st.markdown(f"<style>{f.read()}</style>", unsafe_allow_html=True)
# Streamlit app interface
st.title("Legal Section Classification for FIR")
st.write("Enter the text for case classification:")

# Text input for case description
sample_text = st.text_area("Case Description", "")

# Button to make prediction
if st.button("Classify Case"):
    predictions = predict_text(sample_text)
    st.write(predictions)