from transformers import AutoTokenizer, T5ForConditionalGeneration
from datasets import load_dataset
import faiss
import numpy as np
import streamlit as st

# Load the datasets from Hugging Face
datasets_dict = {
    "BillSum": load_dataset("billsum"),
    "EurLex": load_dataset("eurlex")
}

# Load the T5 model and tokenizer for summarization
t5_tokenizer = AutoTokenizer.from_pretrained("t5-base")
t5_model = T5ForConditionalGeneration.from_pretrained("t5-base")

# Initialize variables for the selected dataset
selected_dataset = "BillSum"
documents = []
titles = []

# Prepare the dataset for retrieval based on user selection
def prepare_dataset(dataset_name):
    global documents, titles
    dataset = datasets_dict[dataset_name]
    documents = dataset['train']['text'][:100]  # Use a subset for demo purposes
    titles = dataset['train']['title'][:100]  # Get corresponding titles

prepare_dataset(selected_dataset)

# Function to embed text for retrieval
def embed_text(text):
    input_ids = t5_tokenizer.encode(text, return_tensors="pt", max_length=512, truncation=True)
    with torch.no_grad():
        outputs = t5_model.encoder(input_ids)
    return outputs.last_hidden_state.mean(dim=1).numpy()

# Create embeddings for the documents
doc_embeddings = np.vstack([embed_text(doc) for doc in documents]).astype(np.float32)

# Initialize FAISS index
index = faiss.IndexFlatL2(doc_embeddings.shape[1])
index.add(doc_embeddings)

# Define functions for retrieving and summarizing cases
def retrieve_cases(query, top_k=3):
    query_embedding = embed_text(query)
    distances, indices = index.search(query_embedding, top_k)
    return [(documents[i], titles[i]) for i in indices[0]]  # Return documents and their titles

def summarize_cases(cases):
    summaries = []
    for case, _ in cases:
        input_ids = t5_tokenizer.encode(case, return_tensors="pt", max_length=512, truncation=True)
        outputs = t5_model.generate(input_ids, max_length=60, min_length=30, length_penalty=2.0, num_beams=4, early_stopping=True)
        summary = t5_tokenizer.decode(outputs[0], skip_special_tokens=True)
        summaries.append(summary)
    return summaries

# Step 3: Streamlit App Code
st.title("Legal Case Summarizer")
st.write("Select a dataset and enter keywords to retrieve and summarize relevant cases.")

# Dropdown for selecting dataset
dataset_options = list(datasets_dict.keys())
selected_dataset = st.selectbox("Choose a dataset:", dataset_options)

# Prepare the selected dataset
prepare_dataset(selected_dataset)

query = st.text_input("Enter search keywords:", "healthcare")

if st.button("Retrieve and Summarize Cases"):
    with st.spinner("Retrieving and summarizing cases..."):
        cases = retrieve_cases(query)
        if cases:
            summaries = summarize_cases(cases)
            for i, (case, title) in enumerate(cases):
                summary = summaries[i]
                st.write(f"### Case {i + 1}")
                st.write(f"**Title:** {title}")
                st.write(f"**Case Text:** {case}")
                st.write(f"**Summary:** {summary}")
        else:
            st.write("No cases found for the given query.")

st.write("Using T5 for summarization and retrieval.")
import torch
from transformers import AutoTokenizer, T5ForConditionalGeneration
from datasets import load_dataset
import faiss
import numpy as np
import streamlit as st

# Load the datasets from Hugging Face
datasets_dict = {
    "BillSum": load_dataset("billsum"),
    "EurLex": load_dataset("eurlex")
}

# Load the T5 model and tokenizer for summarization
t5_tokenizer = AutoTokenizer.from_pretrained("t5-base")
t5_model = T5ForConditionalGeneration.from_pretrained("t5-base")

# Initialize variables for the selected dataset
selected_dataset = "BillSum"
documents = []
titles = []

# Prepare the dataset for retrieval based on user selection
def prepare_dataset(dataset_name):
    global documents, titles
    dataset = datasets_dict[dataset_name]
    documents = dataset['train']['text'][:100]  # Use a subset for demo purposes
    titles = dataset['train']['title'][:100]  # Get corresponding titles

prepare_dataset(selected_dataset)

# Function to embed text for retrieval
def embed_text(text):
    input_ids = t5_tokenizer.encode(text, return_tensors="pt", max_length=512, truncation=True)
    with torch.no_grad():
        outputs = t5_model.encoder(input_ids)
    return outputs.last_hidden_state.mean(dim=1).numpy()

# Create embeddings for the documents
doc_embeddings = np.vstack([embed_text(doc) for doc in documents]).astype(np.float32)

# Initialize FAISS index
index = faiss.IndexFlatL2(doc_embeddings.shape[1])
index.add(doc_embeddings)

# Define functions for retrieving and summarizing cases
def retrieve_cases(query, top_k=3):
    query_embedding = embed_text(query)
    distances, indices = index.search(query_embedding, top_k)
    return [(documents[i], titles[i]) for i in indices[0]]  # Return documents and their titles

def summarize_cases(cases):
    summaries = []
    for case, _ in cases:
        input_ids = t5_tokenizer.encode(case, return_tensors="pt", max_length=512, truncation=True)
        outputs = t5_model.generate(input_ids, max_length=60, min_length=30, length_penalty=2.0, num_beams=4, early_stopping=True)
        summary = t5_tokenizer.decode(outputs[0], skip_special_tokens=True)
        summaries.append(summary)
    return summaries

# Step 3: Streamlit App Code
st.title("Legal Case Summarizer")
st.write("Select a dataset and enter keywords to retrieve and summarize relevant cases.")

# Dropdown for selecting dataset
dataset_options = list(datasets_dict.keys())
selected_dataset = st.selectbox("Choose a dataset:", dataset_options)

# Prepare the selected dataset
prepare_dataset(selected_dataset)

query = st.text_input("Enter search keywords:", "healthcare")

if st.button("Retrieve and Summarize Cases"):
    with st.spinner("Retrieving and summarizing cases..."):
        cases = retrieve_cases(query)
        if cases:
            summaries = summarize_cases(cases)
            for i, (case, title) in enumerate(cases):
                summary = summaries[i]
                st.write(f"### Case {i + 1}")
                st.write(f"**Title:** {title}")
                st.write(f"**Case Text:** {case}")
                st.write(f"**Summary:** {summary}")
        else:
            st.write("No cases found for the given query.")

st.write("Using T5 for summarization and retrieval.")