Update app.py

app.py

@@ -11,14 +11,16 @@ import os
 import nltk
 nltk.download('punkt')
 
+# Install Poppler in the runtime environment
+os.system("apt-get update && apt-get install -y poppler-utils")
 
-import os
 secret = os.getenv('Groq_api')
 
 working_dir = os.path.dirname(os.path.abspath(__file__))
 
 def load_documents(file_path):
-    loader = UnstructuredPDFLoader(file_path)
+    # Specify poppler_path to ensure compatibility in Spaces
+    loader = UnstructuredPDFLoader(file_path, poppler_path="/usr/bin")
     documents = loader.load()
     return documents
 
@@ -26,52 +28,52 @@ def setup_vectorstore(documents):
     embeddings = HuggingFaceEmbeddings()
     text_splitter = CharacterTextSplitter(
         separator="/n", 
-        chunk_size = 1000,
-        chunk_overlap = 200
+        chunk_size=1000,
+        chunk_overlap=200
     )
     doc_chunks = text_splitter.split_documents(documents)
-    vectorstores = FAISS.from_documents(doc_chunks,embeddings)
+    vectorstores = FAISS.from_documents(doc_chunks, embeddings)
     return vectorstores
 
 def create_chain(vectorstores):
     llm = ChatGroq(
-        api_key = secret,
+        api_key=secret,
         model="llama-3.3-70b-versatile",
         temperature=0
     )
     retriever = vectorstores.as_retriever()
     memory = ConversationBufferMemory(
-        llm = llm,
-        output_key= "answer",
-        memory_key = "chat_history",
+        llm=llm,
+        output_key="answer",
+        memory_key="chat_history",
         return_messages=True
-
     )
     chain = ConversationalRetrievalChain.from_llm(
-        llm = llm,
-        retriever = retriever,
-        memory = memory,
-        verbose = True
+        llm=llm,
+        retriever=retriever,
+        memory=memory,
+        verbose=True
     )
     return chain
 
+# Streamlit page configuration
 st.set_page_config(
-    page_title= "Chat with your documents",
-    page_icon= "📑",
+    page_title="Chat with your documents",
+    page_icon="📑",
     layout="centered"
-
 )
 
 st.title("📝Chat With your docs 😎")
 
+# Initialize session states
 if "chat_history" not in st.session_state:
     st.session_state.chat_history = []
 
 uploaded_file = st.file_uploader(label="Upload your PDF")
 
 if uploaded_file:
-    file_path = f"{working_dir}{uploaded_file.name}"
-    with open(file_path,"wb") as f:
+    file_path = f"{working_dir}/{uploaded_file.name}"
+    with open(file_path, "wb") as f:
         f.write(uploaded_file.getbuffer())
 
     if "vectorstores" not in st.session_state:
@@ -80,20 +82,21 @@ if uploaded_file:
     if "conversation_chain" not in st.session_state:
         st.session_state.conversation_chain = create_chain(st.session_state.vectorstores)
 
-    
+# Display chat history
 for message in st.session_state.chat_history:
     with st.chat_message(message["role"]):
         st.markdown(message["content"])
 
+# User input handling
 user_input = st.chat_input("Ask any questions relevant to uploaded pdf")
 
 if user_input:
-    st.session_state.chat_history.append({"role":"user","content":user_input})
+    st.session_state.chat_history.append({"role": "user", "content": user_input})
     with st.chat_message("user"):
         st.markdown(user_input)
 
     with st.chat_message("assistant"):
-        response = st.session_state.conversation_chain({"question":user_input})
+        response = st.session_state.conversation_chain({"question": user_input})
         assistant_response = response["answer"]
         st.markdown(assistant_response)
-        st.session_state.chat_history.append({"role":"assistant","content":assistant_response})
+        st.session_state.chat_history.append({"role": "assistant", "content": assistant_response})