Update app.py

app.py

@@ -1,60 +1,60 @@
-import os
-import json
-from langchain_core.documents import Document
-from langchain_text_splitters import RecursiveCharacterTextSplitter
-from langchain_community.vectorstores import FAISS
-from langchain_huggingface import HuggingFaceEmbeddings
-from langchain_openai import ChatOpenAI
-from langchain.chains import RetrievalQA
-import chainlit as cl
-
-# === Load and prepare data ===
-with open("combined_data.json", "r") as f:
-    raw_data = json.load(f)
-
-all_docs = [
-    Document(page_content=entry["content"], metadata=entry["metadata"])
-    for entry in raw_data
-]
-
-# === Split documents into chunks ===
-splitter = RecursiveCharacterTextSplitter(chunk_size=800, chunk_overlap=50)
-chunked_docs = splitter.split_documents(all_docs)
-
-# === Use your fine-tuned Hugging Face embeddings ===
-embedding_model = HuggingFaceEmbeddings(
-    model_name="AneetaXavier/reformer-pilates-embed-ft-49fc1835-9968-433d-9c45-1538ea91dcc9"
-)
-
-# === Set up FAISS vector store ===
-vectorstore = FAISS.from_documents(chunked_docs, embedding_model)
-retriever = vectorstore.as_retriever(search_kwargs={"k": 5})
-
-# === Load LLM ===
-llm = ChatOpenAI(model_name="gpt-4.1-mini", temperature=0)
-qa_chain = RetrievalQA.from_chain_type(llm=llm, retriever=retriever)
-
-# === Chainlit start event ===
-@cl.on_chat_start
-async def start():
-    await cl.Message(content = 
-"""👋 Welcome to your Reformer Pilates AI!
-
-Here’s what you can do:
-• Ask questions about Reformer Pilates
-• Get individualized workouts based on your level, goals, and equipment
-• Get instant exercise modifications based on injuries or limitations
-
-Let’s get started! 🚀""").send()
-    cl.user_session.set("qa_chain", qa_chain)
-
-# === Chainlit message handler ===
-@cl.on_message
-async def handle_message(message: cl.Message):
-    chain = cl.user_session.get("qa_chain")
-    if chain:
-        try:
-            response = chain.run(message.content)
-        except Exception as e:
-            response = f"⚠️ Error: {str(e)}"
-        await cl.Message(response).send()
+import os
+import json
+from langchain_core.documents import Document
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_community.vectorstores import FAISS
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_openai import ChatOpenAI
+from langchain.chains import RetrievalQA
+import chainlit as cl
+
+# === Load and prepare data ===
+with open("combined_data.json", "r") as f:
+    raw_data = json.load(f)
+
+all_docs = [
+    Document(page_content=entry["content"], metadata=entry["metadata"])
+    for entry in raw_data
+]
+
+# === Split documents into chunks ===
+splitter = RecursiveCharacterTextSplitter(chunk_size=800, chunk_overlap=50)
+chunked_docs = splitter.split_documents(all_docs)
+
+# === Use your fine-tuned Hugging Face embeddings ===
+embedding_model = HuggingFaceEmbeddings(
+    model_name="bsmith3715/legal-ft-demo_final"
+)
+
+# === Set up FAISS vector store ===
+vectorstore = FAISS.from_documents(chunked_docs, embedding_model)
+retriever = vectorstore.as_retriever(search_kwargs={"k": 5})
+
+# === Load LLM ===
+llm = ChatOpenAI(model_name="gpt-4.1-mini", temperature=0)
+qa_chain = RetrievalQA.from_chain_type(llm=llm, retriever=retriever)
+
+# === Chainlit start event ===
+@cl.on_chat_start
+async def start():
+    await cl.Message(content = 
+"""👋 Welcome to your Reformer Pilates AI!
+
+Here’s what you can do:
+• Ask questions about Reformer Pilates
+• Get individualized workouts based on your level, goals, and equipment
+• Get instant exercise modifications based on injuries or limitations
+
+Let’s get started! 🚀""").send()
+    cl.user_session.set("qa_chain", qa_chain)
+
+# === Chainlit message handler ===
+@cl.on_message
+async def handle_message(message: cl.Message):
+    chain = cl.user_session.get("qa_chain")
+    if chain:
+        try:
+            response = chain.run(message.content)
+        except Exception as e:
+            response = f"⚠️ Error: {str(e)}"
+        await cl.Message(response).send()