Update app.py

app.py

@@ -1,5 +1,6 @@
 import streamlit as st
 import pandas as pd
+import io
 import os
 from dotenv import load_dotenv
 from llama_index.core import Settings, VectorStoreIndex, SimpleDirectoryReader
@@ -18,6 +19,7 @@ from langchain_openai import OpenAIEmbeddings, ChatOpenAI
 import faiss
 
 # Load environment variables
+load_dotenv()
 os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY")
 
 # Global settings for LlamaIndex
@@ -26,96 +28,99 @@ Settings.llm = OpenAI(model="gpt-3.5-turbo")
 Settings.embed_model = OpenAIEmbedding(model="text-embedding-3-small", dimensions=EMBED_DIMENSION)
 
 # Streamlit app
-st.title("Chat w CSV Files - LangChain Vs LlamaIndex ")
+st.title("Chat with CSV Files - LangChain vs LlamaIndex")
 
 # File uploader
 uploaded_file = st.file_uploader("Upload a CSV file", type=["csv"])
 if uploaded_file:
-    # Save the uploaded file temporarily
-    temp_file_path = f"temp_{uploaded_file.name}"
-    with open(temp_file_path, "wb") as temp_file:
-        temp_file.write(uploaded_file.getbuffer())
-
-    # Read and preview CSV data
-    data = pd.read_csv(temp_file_path)
-    st.write("Preview of uploaded data:")
-    st.dataframe(data)
-
-    # Tabs
-    tab1, tab2 = st.tabs(["Chat w CSV using LangChain", "Chat w CSV using LlamaIndex"])
-
-    # LangChain Tab
-    with tab1:
-        st.subheader("LangChain Query")
-        loader = CSVLoader(file_path=temp_file_path)
-        docs = loader.load_and_split()
-
-        # Preview the first document
-        if docs:
-            st.write("Preview of a document chunk (LangChain):")
-            st.text(docs[0].page_content)
-
-        # LangChain FAISS VectorStore
-        langchain_index = faiss.IndexFlatL2(EMBED_DIMENSION)
-        langchain_vector_store = LangChainFAISS(
-            embedding_function=OpenAIEmbeddings(),
-            index=langchain_index,
-        )
-        langchain_vector_store.add_documents(docs)
-
-        # LangChain Retrieval Chain
-        retriever = langchain_vector_store.as_retriever()
-        system_prompt = (
-            "You are an assistant for question-answering tasks. "
-            "Use the following pieces of retrieved context to answer "
-            "the question. If you don't know the answer, say that you "
-            "don't know. Use three sentences maximum and keep the "
-            "answer concise.\n\n{context}"
-        )
-        prompt = ChatPromptTemplate.from_messages(
-            [("system", system_prompt), ("human", "{input}")]
-        )
-        question_answer_chain = create_stuff_documents_chain(ChatOpenAI(), prompt)
-        langchain_rag_chain = create_retrieval_chain(retriever, question_answer_chain)
-
-        # Query input for LangChain
-        query = st.text_input("Ask a question about your data (LangChain):")
-        if query:
-            answer = langchain_rag_chain.invoke({"input": query})
-            st.write(f"Answer: {answer['answer']}")
-
-    # LlamaIndex Tab
-    with tab2:
-        st.subheader("LlamaIndex Query")
-        csv_reader = PagedCSVReader()
-        reader = SimpleDirectoryReader(
-            input_files=[temp_file_path],
-            file_extractor={".csv": csv_reader},
-        )
-        docs = reader.load_data()
-
-        # Preview the first document
-        if docs:
-            st.write("Preview of a document chunk (LlamaIndex):")
-            st.text(docs[0].text)
-
-        # Initialize FAISS Vector Store
-        llama_faiss_index = faiss.IndexFlatL2(EMBED_DIMENSION)
-        llama_vector_store = FaissVectorStore(faiss_index=llama_faiss_index)
-
-        # Create the ingestion pipeline and process the data
-        pipeline = IngestionPipeline(vector_store=llama_vector_store, documents=docs)
-        nodes = pipeline.run()
-
-        # Create a query engine
-        llama_index = VectorStoreIndex(nodes)
-        query_engine = llama_index.as_query_engine(similarity_top_k=3)
-
-        # Query input for LlamaIndex
-        query = st.text_input("Ask a question about your data (LlamaIndex):")
-        if query:
-            response = query_engine.query(query)
-            st.write(f"Answer: {response.response}")
-
-    # Cleanup temporary file
-    os.remove(temp_file_path)
+    try:
+        # Load and preview CSV data using pandas
+        data = pd.read_csv(uploaded_file)
+        st.write("Preview of uploaded data:")
+        st.dataframe(data)
+
+        # Tabs
+        tab1, tab2 = st.tabs(["Chat w CSV using LangChain", "Chat w CSV using LlamaIndex"])
+
+        # LangChain Tab
+        with tab1:
+            st.subheader("LangChain Query")
+            try:
+                # Use CSVLoader directly with file-like object
+                loader = CSVLoader(file_path=io.BytesIO(uploaded_file.getvalue()))
+                docs = loader.load_and_split()
+
+                # Preview the first document
+                if docs:
+                    st.write("Preview of a document chunk (LangChain):")
+                    st.text(docs[0].page_content)
+
+                # LangChain FAISS VectorStore
+                langchain_index = faiss.IndexFlatL2(EMBED_DIMENSION)
+                langchain_vector_store = LangChainFAISS(
+                    embedding_function=OpenAIEmbeddings(),
+                    index=langchain_index,
+                )
+                langchain_vector_store.add_documents(docs)
+
+                # LangChain Retrieval Chain
+                retriever = langchain_vector_store.as_retriever()
+                system_prompt = (
+                    "You are an assistant for question-answering tasks. "
+                    "Use the following pieces of retrieved context to answer "
+                    "the question. If you don't know the answer, say that you "
+                    "don't know. Use three sentences maximum and keep the "
+                    "answer concise.\n\n{context}"
+                )
+                prompt = ChatPromptTemplate.from_messages(
+                    [("system", system_prompt), ("human", "{input}")]
+                )
+                question_answer_chain = create_stuff_documents_chain(ChatOpenAI(), prompt)
+                langchain_rag_chain = create_retrieval_chain(retriever, question_answer_chain)
+
+                # Query input for LangChain
+                query = st.text_input("Ask a question about your data (LangChain):")
+                if query:
+                    answer = langchain_rag_chain.invoke({"input": query})
+                    st.write(f"Answer: {answer['answer']}")
+            except Exception as e:
+                st.error(f"Error processing with LangChain: {e}")
+
+        # LlamaIndex Tab
+        with tab2:
+            st.subheader("LlamaIndex Query")
+            try:
+                # Use PagedCSVReader for LlamaIndex
+                csv_reader = PagedCSVReader()
+                reader = SimpleDirectoryReader(
+                    input_files=[uploaded_file.name],
+                    file_extractor={".csv": csv_reader},
+                )
+                docs = reader.load_data()
+
+                # Preview the first document
+                if docs:
+                    st.write("Preview of a document chunk (LlamaIndex):")
+                    st.text(docs[0].text)
+
+                # Initialize FAISS Vector Store
+                llama_faiss_index = faiss.IndexFlatL2(EMBED_DIMENSION)
+                llama_vector_store = FaissVectorStore(faiss_index=llama_faiss_index)
+
+                # Create the ingestion pipeline and process the data
+                pipeline = IngestionPipeline(vector_store=llama_vector_store, documents=docs)
+                nodes = pipeline.run()
+
+                # Create a query engine
+                llama_index = VectorStoreIndex(nodes)
+                query_engine = llama_index.as_query_engine(similarity_top_k=3)
+
+                # Query input for LlamaIndex
+                query = st.text_input("Ask a question about your data (LlamaIndex):")
+                if query:
+                    response = query_engine.query(query)
+                    st.write(f"Answer: {response.response}")
+            except Exception as e:
+                st.error(f"Error processing with LlamaIndex: {e}")
+    except Exception as e:
+        st.error(f"Error reading uploaded file: {e}")