Create app.py

app.py

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+import streamlit as st
+from model import load_vectorstore, ask_question
+import os
+
+st.set_page_config(page_title="RAG with Mistral AI", layout="centered")
+
+st.title("RAG Q&A App with Mistral AI")
+st.write("Upload a document and ask questions about it.")
+
+uploaded_file = st.file_uploader("Upload PDF", type=["pdf"])
+
+if uploaded_file:
+    with open("document.pdf", "wb") as f:
+        f.write(uploaded_file.read())
+    st.success("File uploaded!")
+
+    if st.button("Index Document"):
+        with st.spinner("Processing..."):
+            load_vectorstore("document.pdf")
+            st.success("Vectorstore ready.")
+
+query = st.text_input("Enter your question")
+if st.button("Ask") and query:
+    with st.spinner("Generating answer..."):
+        answer = ask_question(query)
+        st.write("**Answer:**", answer)
+
+# api.py
+from fastapi import FastAPI, UploadFile, File, Form
+from model import load_vectorstore, ask_question
+import shutil
+
+app = FastAPI()
+
+@app.post("/upload")
+async def upload(file: UploadFile = File(...)):
+    with open("document.pdf", "wb") as buffer:
+        shutil.copyfileobj(file.file, buffer)
+    load_vectorstore("document.pdf")
+    return {"message": "File indexed successfully."}
+
+@app.post("/ask")
+async def ask(question: str = Form(...)):
+    answer = ask_question(question)
+    return {"question": question, "answer": answer}
+