Update app.py

app.py

@@ -1,3 +1,4 @@
+import torch
 from transformers import AutoTokenizer, T5ForConditionalGeneration
 from datasets import load_dataset
 import faiss
@@ -5,10 +6,23 @@ import numpy as np
 import streamlit as st
 
 # Load the datasets from Hugging Face
-datasets_dict = {
-    "BillSum": load_dataset("billsum"),
-    "EurLex": load_dataset("eurlex", trust_remote_code=True)  # Set trust_remote_code=True
-}
+datasets_dict = {}
+
+# Function to load datasets safely
+def load_datasets():
+    global datasets_dict
+    try:
+        datasets_dict["BillSum"] = load_dataset("billsum")
+    except Exception as e:
+        st.error(f"Error loading BillSum dataset: {e}")
+    
+    try:
+        datasets_dict["EurLex"] = load_dataset("eurlex", trust_remote_code=True)  # Set trust_remote_code=True
+    except Exception as e:
+        st.error(f"Error loading EurLex dataset: {e}")
+
+# Load datasets at the start
+load_datasets()
 
 # Load the T5 model and tokenizer for summarization
 t5_tokenizer = AutoTokenizer.from_pretrained("t5-base")
@@ -26,37 +40,6 @@ def prepare_dataset(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
-
 # Streamlit App Code
 st.title("Legal Case Summarizer")
 st.write("Select a dataset and enter keywords to retrieve and summarize relevant cases.")