Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,49 @@
-import altair as alt
-import numpy as np
-import pandas as pd
+# app_hf_space.py
 import streamlit as st
-
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_community.vectorstores.faiss import FAISS
+from langchain.chains import RetrievalQA
+from langchain_huggingface.llms import HuggingFacePipeline
+
+# --- Configuration ---
+# Use Hugging Face inference API via the `pipeline` from langchain-huggingface
+EMBED_MODEL_ID = "sentence-transformers/all-MiniLM-L6-v2"
+LLM_MODEL_ID = "google/flan-t5-small"
+INDEX_DIR = "faiss_index"
+
+# 1. Initialize embeddings via HF Inference API
+embeddings = HuggingFaceEmbeddings(
+    model_name=EMBED_MODEL_ID,
+    cache_dir=".hf_cache"
+)
+
+# 2. Load FAISS index
+store = FAISS.load_local(
+    INDEX_DIR,
+    embeddings
+)
+
+# 3. Initialize HF LLM via pipeline (inference API)
+llm = HuggingFacePipeline.from_model_id(
+    model_id=LLM_MODEL_ID,
+    task="text2text-generation"
+)
+
+# 4. Build RetrievalQA chain
+aqa_chain = RetrievalQA.from_chain_type(
+    llm=llm,
+    chain_type="stuff",
+    retriever=store.as_retriever()
+)
+
+# 5. Streamlit UI
+st.title("🦜🔗 RAG App via HF Spaces")
+query = st.text_input("Ask a question about your docs:")
+
+if query:
+    with st.spinner("Generating answer via HF Space..."):
+        answer = aqa_chain.run(query)
+    st.markdown(f"**Answer:** {answer}")
+
+# NOTE: Deploy this to Hugging Face Spaces for fully-managed hosting.
+# Just push this file to your repo on HF and enable Streamlit space.