Update app.py

app.py

@@ -1,92 +1,3 @@
-
-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
@@ -96,7 +7,7 @@ import streamlit as st
 # Load the datasets from Hugging Face
 datasets_dict = {
     "BillSum": load_dataset("billsum"),
-    "EurLex": load_dataset("eurlex")
+    "EurLex": load_dataset("eurlex", trust_remote_code=True)  # Set trust_remote_code=True
 }
 
 # Load the T5 model and tokenizer for summarization
@@ -146,7 +57,7 @@ def summarize_cases(cases):
         summaries.append(summary)
     return summaries
 
-# Step 3: Streamlit App Code
+# Streamlit App Code
 st.title("Legal Case Summarizer")
 st.write("Select a dataset and enter keywords to retrieve and summarize relevant cases.")