Add project files including app.py, data, and documentation

Dockerfile-2.txt

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+FROM ghcr.io/astral-sh/uv:python3.13-bookworm-slim
+
+RUN useradd -m -u 1000 user
+USER user
+
+ENV HOME=/home/user \
+    PATH=/home/user/.local/bin:$PATH \
+    UVICORN_WS_PROTOCOL=websockets
+
+WORKDIR $HOME/app
+
+COPY --chown=user . $HOME/app
+
+RUN uv sync
+
+EXPOSE 7860
+
+CMD ["uv", "run", "chainlit", "run", "app.py", "--host", "0.0.0.0", "--port", "7860"]

LICENSE.txt

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+MIT License
+
+Copyright (c) 2025 Aneeta Xavier
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README-2.md

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+---
+title : Pilates App Fine_Tuned
+emoji: 📚
+colorFrom: red
+colorTo: red
+sdk: docker
+app_file: app.py
+pinned: false
+license: mit
+short_description: Tool to provide users reformer exercises
+
+---
+
+# Pilates Reformer RAG App Fine_Tuned
+
+This Chainlit app answers questions using Pilates reformer videos and textbooks. All data is preloaded from `combined_data.json`.
+
+## Run Locally
+
+```bash
+uv run chainlit run app.py
+```
+
+## Or Deploy to Hugging Face Space with Docker
+Just upload this directory and you're done.

app.py

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+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("🤸 Ready! Ask me anything about Reformer Pilates.").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()