Create app.py

app.py

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+import pandas as pd
+from rank_bm25 import BM25Okapi
+from transformers import pipeline
+import streamlit as st
+from datasets import load_dataset
+
+# Load Dataset from Hugging Face with Error Handling
+def load_huggingface_dataset(dataset_name, config=None, split="train"):
+    try:
+        if config:
+            dataset = load_dataset(dataset_name, config, split=split)
+        else:
+            dataset = load_dataset(dataset_name, split=split)
+        
+        data = pd.DataFrame(dataset)  # Convert to pandas DataFrame
+        return data
+    except Exception as e:
+        st.error(f"Failed to load dataset '{dataset_name}' with config '{config}'. Please try 'lex_glue' or 'eurlex' with appropriate config.")
+        st.error(f"Error details: {e}")
+        return None
+
+# Prepare the Retrieval Model (BM25)
+def prepare_bm25(corpus):
+    tokenized_corpus = [doc.split(" ") for doc in corpus]
+    bm25 = BM25Okapi(tokenized_corpus)
+    return bm25
+
+# Search for Similar Documents
+def search_documents(bm25, query, corpus, top_n=5):
+    tokenized_query = query.split(" ")
+    scores = bm25.get_top_n(tokenized_query, corpus, n=top_n)
+    return scores
+
+# Summarization Model
+def summarize_text(text):
+    try:
+        # Use a public model for summarization
+        summarizer = pipeline("summarization", model="t5-base")  # Change to a public model
+        summary = summarizer(text, max_length=130, min_length=30, do_sample=False)
+        return summary[0]['summary_text']
+    except Exception as e:
+        st.error(f"Error in summarization: {e}")
+        return "Summary could not be generated."
+
+# Streamlit App
+def main():
+    st.title("Legal Case Summarizer")
+    
+    # Dataset Selection
+    dataset_name = st.selectbox("Choose Hugging Face dataset", ["lex_glue", "eurlex"])
+    config = None
+    
+    # Config Selection for lex_glue
+    if dataset_name == "lex_glue":
+        config = st.selectbox("Select config for lex_glue", ["case_hold", "ecthr_a", "ecthr_b", "eurlex", "ledgar", "scotus", "unfair_tos"])
+    
+    split = st.selectbox("Choose dataset split", ["train", "validation", "test"])
+    
+    if dataset_name:
+        st.write("Loading dataset from Hugging Face...")
+        data = load_huggingface_dataset(dataset_name, config=config, split=split)
+        
+        if data is not None:
+            corpus = data['text'].tolist() if 'text' in data.columns else data.iloc[:, 0].tolist()
+            titles = data['title'].tolist() if 'title' in data.columns else ["Title " + str(i) for i in range(len(corpus))]
+
+            # Prepare BM25 Model
+            bm25 = prepare_bm25(corpus)
+
+            # User Input
+            query = st.text_input("Enter keywords for case search:")
+            num_results = st.slider("Number of results to display", 1, 10, 5)
+
+            if query:
+                st.write("Searching for relevant cases...")
+                results = search_documents(bm25, query, corpus, top_n=num_results)
+
+                for idx, result in enumerate(results):
+                    st.write(f"### Case {idx+1}: {titles[corpus.index(result)]}")
+                    st.write(result)
+                    
+                    # Summarize the case
+                    st.write("Summary:")
+                    summary = summarize_text(result)
+                    st.write(summary)
+
+if __name__ == "__main__":
+    main()