Update app.py

app.py

@@ -1,31 +1,40 @@
 import streamlit as st
+import tempfile
+import os
 
-# ✅ Correct imports (new structure)
+# LangChain imports (new structure)
 from langchain_community.document_loaders import PyPDFLoader, TextLoader
 from langchain_text_splitters import RecursiveCharacterTextSplitter
 from langchain_community.embeddings import HuggingFaceEmbeddings
 from langchain_community.vectorstores import FAISS
 from langchain_community.llms import HuggingFacePipeline
-
 from langchain.chains import RetrievalQA
+
 from transformers import pipeline
 
 
 # -------------------------------
-# Load Documents
+# Load Documents (FIXED temp file handling)
 # -------------------------------
 def load_documents(uploaded_files):
     documents = []
+
     for file in uploaded_files:
-        with open(file.name, "wb") as f:
-            f.write(file.getbuffer())
+        suffix = file.name.split(".")[-1]
 
-        if file.name.endswith(".pdf"):
-            loader = PyPDFLoader(file.name)
+        with tempfile.NamedTemporaryFile(delete=False, suffix=f".{suffix}") as tmp:
+            tmp.write(file.getbuffer())
+            tmp_path = tmp.name
+
+        if suffix == "pdf":
+            loader = PyPDFLoader(tmp_path)
         else:
-            loader = TextLoader(file.name)
+            loader = TextLoader(tmp_path)
 
         documents.extend(loader.load())
+
+        os.remove(tmp_path)  # cleanup
+
     return documents
 
 
@@ -51,19 +60,21 @@ def create_vectorstore(chunks):
 
 
 # -------------------------------
-# Load Local LLM (FREE)
+# Load Local LLM (STABLE VERSION)
 # -------------------------------
+@st.cache_resource
 def load_llm():
     pipe = pipeline(
-        "text2text-generation",   # ✅ FIXED
+        task="text2text-generation",
         model="google/flan-t5-base",
-        max_length=512
+        max_length=512,
+        do_sample=False
     )
     return HuggingFacePipeline(pipeline=pipe)
 
 
 # -------------------------------
-# Build QA Chain (with strict prompt)
+# Build QA Chain
 # -------------------------------
 def build_qa(vectorstore):
     llm = load_llm()
@@ -94,12 +105,12 @@ if uploaded_files:
         vectorstore = create_vectorstore(chunks)
         qa_chain = build_qa(vectorstore)
 
-    st.success("Documents ready!")
+    st.success("✅ Documents ready!")
 
     query = st.text_input("Ask a question from your documents")
 
     if query:
         with st.spinner("Generating answer..."):
-            result = qa_chain.run(query)
+            result = qa_chain.invoke({"query": query})
             st.write("### Answer:")
-            st.write(result)
+            st.write(result["result"])