Update app.py

app.py

@@ -1,8 +1,44 @@
+import streamlit as st
+import os
 from pinecone import Pinecone, ServerlessSpec
+from sentence_transformers import SentenceTransformer
+import numpy as np
+from datasets import load_dataset
 
-# Initialize Pinecone
+# ✅ Step 1: Fetch Pinecone API key from Hugging Face secrets
+if "PINECONE_API_KEY" not in st.secrets:
+    st.error("🚨 Pinecone API key not found! Please set it in Hugging Face secrets.")
+    st.stop()
+
+pinecone_api_key = st.secrets["PINECONE_API_KEY"]  # ✅ Now it's properly defined
+
+# ✅ Step 2: Initialize Pinecone client
 pc = Pinecone(api_key=pinecone_api_key)
 
-# Connect to your existing index
+# ✅ Step 3: Connect to your existing Pinecone index
 index_name = "legal-docs-index-dji2ip8"
 index = pc.Index(index_name)
+
+# ✅ Step 4: Load embedding model
+model = SentenceTransformer("all-MiniLM-L6-v2")
+
+# ✅ Step 5: Load dataset (for reference)
+dataset = load_dataset("macadeliccc/US-LegalKit", split="train")
+law_texts = [item['text'] for item in dataset if 'text' in item]
+
+# ✅ Step 6: Function to search Pinecone index
+def search_pinecone(query, top_k=5):
+    query_embedding = model.encode([query]).tolist()
+    results = index.query(query_embedding, top_k=top_k, include_metadata=True)
+    return [match['metadata']['text'] for match in results['matches']]
+
+# ✅ Step 7: Streamlit UI
+st.title("🔍 Legal AI Assistant (US-LegalKit)")
+
+query = st.text_input("📌 Enter your legal query:")
+
+if query:
+    results = search_pinecone(query)
+    st.write("### 📄 Relevant Legal Documents:")
+    for i, doc in enumerate(results, 1):
+        st.write(f"**{i}.** {doc[:500]}...")