Create app.py

app.py

@@ -0,0 +1,44 @@
+import gradio as gr
+from datasets import load_dataset
+from sentence_transformers import SentenceTransformer
+import faiss
+import numpy as np
+from transformers import pipeline
+
+# Load dataset
+dataset = load_dataset("lex_glue", "scotus")
+corpus = [doc['text'] for doc in dataset['train'].select(range(200))]  # just 200 to keep it light
+
+# Embedding model
+embedder = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
+corpus_embeddings = embedder.encode(corpus, convert_to_numpy=True)
+
+# Build FAISS index
+dimension = corpus_embeddings.shape[1]
+index = faiss.IndexFlatL2(dimension)
+index.add(corpus_embeddings)
+
+# Text generation model
+gen_pipeline = pipeline("text2text-generation", model="facebook/bart-large-cnn")
+
+# RAG-like query function
+def rag_query(user_question):
+    question_embedding = embedder.encode([user_question])
+    _, indices = index.search(np.array(question_embedding), k=3)
+    context = " ".join([corpus[i] for i in indices[0]])
+
+    prompt = f"Question: {user_question}\nContext: {context}\nAnswer:"
+    result = gen_pipeline(prompt, max_length=250, do_sample=False)[0]['generated_text']
+    return result
+
+# Gradio UI
+def chatbot_interface(query):
+    return rag_query(query)
+
+iface = gr.Interface(fn=chatbot_interface,
+                     inputs="text",
+                     outputs="text",
+                     title="🧑‍⚖️ Legal Assistant Chatbot",
+                     description="Ask legal questions based on case data (LexGLUE - SCOTUS subset)")
+
+iface.launch()