Update app.py

app.py

@@ -38,11 +38,17 @@ if uploaded_file:
         st.write("Preview of uploaded data:")
         st.dataframe(data)
 
-        # Clean and save the file to a temporary location
+        # Save the uploaded file to a temporary location
         with tempfile.NamedTemporaryFile(delete=False, suffix=".csv", mode="w", encoding="utf-8") as temp_file:
             temp_file_path = temp_file.name
             data.to_csv(temp_file.name, index=False, encoding="utf-8")
-            temp_file.flush()  # Ensure data is written
+            temp_file.flush()  # Ensure all data is written to the file
+
+        # Debugging: Verify the temporary file
+        st.write("Temporary file path:", temp_file_path)
+        with open(temp_file_path, "r") as f:
+            st.write("Temporary file content:")
+            st.text(f.read())
 
         # Tabs for LangChain and LlamaIndex
         tab1, tab2 = st.tabs(["LangChain", "LlamaIndex"])
@@ -52,12 +58,13 @@ if uploaded_file:
             st.subheader("LangChain Query")
             try:
                 # Use CSVLoader with the temporary file path
+                st.write("Loading file with LangChain CSVLoader...")
                 loader = CSVLoader(file_path=temp_file_path)
                 docs = loader.load_and_split()
 
-                # Preview the first document chunk
+                # Debugging: Preview loaded documents
+                st.write("Successfully loaded documents:")
                 if docs:
-                    st.write("Preview of a document chunk (LangChain):")
                     st.text(docs[0].page_content)
 
                 # Create FAISS VectorStore
@@ -96,12 +103,13 @@ if uploaded_file:
             st.subheader("LlamaIndex Query")
             try:
                 # Use PagedCSVReader directly on the uploaded file
+                st.write("Loading file with LlamaIndex PagedCSVReader...")
                 csv_reader = PagedCSVReader()
                 docs = csv_reader.load_from_file(temp_file_path)
 
-                # Preview the first document chunk
+                # Debugging: Preview loaded documents
+                st.write("Successfully loaded documents:")
                 if docs:
-                    st.write("Preview of a document chunk (LlamaIndex):")
                     st.text(docs[0].text)
 
                 # Initialize FAISS Vector Store