Create app.py

app.py

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+import streamlit as st
+import faiss
+import numpy as np
+from sentence_transformers import SentenceTransformer
+import openai
+
+# Load FAISS index
+index = faiss.read_index("faiss_index.bin")
+
+# Load embedding model
+model = SentenceTransformer("all-MiniLM-L6-v2")
+
+# OpenAI API Key (store it as a secret in Hugging Face)
+openai.api_key = st.secrets["GROQ_API_KEY"]
+
+# Load the preprocessed Pile Law dataset (replace with your dataset path)
+law_data = ["Sample Legal Document 1...", "Sample Legal Document 2..."]  # Replace with actual data loading
+
+# Function to search relevant legal documents
+def search_legal_docs(query, top_k=5):
+    query_embedding = model.encode([query])
+    _, idxs = index.search(query_embedding, top_k)
+    return [law_data[i] for i in idxs[0]]
+
+# Streamlit UI
+st.title("🔍 Legal AI Assistant (Pile Law)")
+
+query = st.text_input("📌 Enter your legal query:")
+
+if query:
+    results = search_legal_docs(query)
+    st.write("### 📄 Relevant Legal Documents:")
+    for res in results:
+        st.write(f"- {res}")
+
+    # Generate AI-based legal response
+    response = openai.ChatCompletion.create(
+        model="gpt-4",
+        messages=[{"role": "system", "content": "You are a legal assistant."},
+                  {"role": "user", "content": query}]
+    )
+    st.write("### 🧑‍⚖️ AI Response:")
+    st.write(response['choices'][0]['message']['content'])