Update app.py

app.py

@@ -2,18 +2,16 @@ import os
 import gradio as gr
 from groq import Groq
 
-from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain_community.document_loaders import PyPDFLoader
 from langchain_community.vectorstores import FAISS
 from langchain_community.embeddings import HuggingFaceEmbeddings
 
 
 # ------------------------------
-# ENVIRONMENT
+# API KEY
 # ------------------------------
 
-# GROQ_API_KEY = os.environ.get("GROQ_API_KEY")
-
 client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
 
 vector_db = None
@@ -29,7 +27,7 @@ embedding_model = HuggingFaceEmbeddings(
 
 
 # ------------------------------
-# DOCUMENT PROCESSING
+# BUILD KNOWLEDGE BASE
 # ------------------------------
 
 def build_knowledge_base(files):
@@ -37,17 +35,20 @@ def build_knowledge_base(files):
     global vector_db
 
     if not files:
-        return "Upload at least one PDF."
+        return "Please upload at least one PDF."
 
     all_docs = []
 
     for file in files:
 
-        loader = PyPDFLoader(file.name)
+        file_path = file.name
+
+        loader = PyPDFLoader(file_path)
+
         pages = loader.load()
 
         for page in pages:
-            page.metadata["source"] = os.path.basename(file.name)
+            page.metadata["source"] = os.path.basename(file_path)
             page.metadata["page"] = page.metadata.get("page", 0)
 
         all_docs.extend(pages)
@@ -64,7 +65,7 @@ def build_knowledge_base(files):
         embedding_model
     )
 
-    return f"Knowledge base ready with {len(chunks)} chunks."
+    return f"Knowledge base created with {len(chunks)} chunks."
 
 
 # ------------------------------
@@ -97,7 +98,7 @@ def ask_question(question):
     global vector_db
 
     if vector_db is None:
-        yield "Please upload documents first."
+        yield "Please upload and build the knowledge base first."
         return
 
     docs = vector_db.similarity_search(question, k=5)
@@ -134,6 +135,7 @@ Answer:
         if chunk.choices[0].delta.content:
 
             token = chunk.choices[0].delta.content
+
             response += token
 
             yield response
@@ -147,16 +149,14 @@ Answer:
 
 
 # ------------------------------
-# PROFESSIONAL UI
+# UI
 # ------------------------------
 
 with gr.Blocks(theme=gr.themes.Soft()) as app:
 
     gr.Markdown("# 📚 AI Knowledge Base Assistant")
 
-    gr.Markdown(
-        "Upload documents and ask questions about them."
-    )
+    gr.Markdown("Upload PDFs and ask questions about them.")
 
     with gr.Row():
 
@@ -178,7 +178,7 @@ with gr.Blocks(theme=gr.themes.Soft()) as app:
     gr.Markdown("## Ask Questions")
 
     question = gr.Textbox(
-        placeholder="Ask a question about the documents..."
+        placeholder="Ask something about the documents..."
     )
 
     ask_btn = gr.Button("Ask AI")