I’m working on a Streamlit application that uses LangChain with OpenAI to analyze CSV data. The app runs without errors, but when I ask for visualizations, the charts don’t appear in the interface.
My Current Code:
import os
import streamlit as st
import pandas as pd
from langchain.agents import create_csv_agent
from langchain.llms import OpenAI
def setup_csv_agent(api_key, file_path, debug_mode=False):
llm_agent = create_csv_agent(OpenAI(temperature=0, openai_api_key=api_key), file_path, verbose=debug_mode)
return llm_agent
def run_app():
st.set_page_config(page_title="Data Analysis with AI")
st.title("CSV Data Analyzer")
# API key input
api_key = st.sidebar.text_input("Enter OpenAI API Key", type="password")
if not api_key:
st.info("Please provide your OpenAI API key.")
st.stop()
# File upload
data_file = st.file_uploader("Choose a CSV file", type=["csv"])
if not data_file:
st.info("Please upload a CSV file.")
st.stop()
# Process uploaded file
dataset = pd.read_csv(data_file)
st.write("Data Preview:")
st.dataframe(dataset.head())
# Create temporary file
csv_file_path = "uploaded_data.csv"
dataset.to_csv(csv_file_path, index=False)
csv_agent = setup_csv_agent(api_key, csv_file_path)
# User input
question = st.text_input("Enter your question:")
if question:
answer = csv_agent.run(question)
st.write("Answer:")
st.write(answer)
# Cleanup
os.remove(csv_file_path)
if __name__ == "__main__":
run_app()
Expected Behavior
When I ask for charts or graphs, I expect them to display in the Streamlit interface like they would in a Jupyter notebook environment. The text responses work fine, but visual outputs are missing.
Has anyone successfully implemented LangChain visualization rendering in Streamlit? What am I missing here?
This happens because LangChain agents create matplotlib code behind the scenes, but Streamlit can’t automatically capture those plots. The charts exist in memory but never reach your interface.
I’ve hit this same issue multiple times. You could try monkey patching matplotlib or capturing figure objects, but it’s a pain to maintain.
I ended up moving this workflow to an automation platform instead. Rather than forcing LangChain and Streamlit to work together, I built the data analysis separately and fed clean results to my frontend.
My current workflow:
CSV upload triggers automated analysis
OpenAI processes data and generates insights
Charts get created and saved properly
Results push back to the display
Way more reliable than intercepting matplotlib figures in real time. You get proper chart rendering, better error handling, and can cache results for faster responses.
Latenode handles this workflow perfectly - it connects APIs, processes data, and manages file outputs without the headache of patching different Python libraries together.
Been dealing with this exact problem for years. The matplotlib capture methods other folks mentioned work okay, but I’ve found a cleaner approach.
Instead of trying to intercept plots after they’re made, modify your agent to stream the actual code it generates. Most LangChain agents spit out pandas or matplotlib commands as text before executing them.
Catch that generated code, parse it for plotting commands, and run those separately through Streamlit. You get way better control over chart styling and can handle different plot types properly.
Here’s the key part - add display_intermediate_steps=True to your agent creation:
Then extract the code from the agent’s thought process and execute plotting commands manually. I usually look for anything with .plot(), plt.show(), or similar patterns.
This tutorial covers streaming LangChain responses in Streamlit really well and shows how to capture intermediate outputs:
Way more reliable than hoping matplotlib figures stick around long enough to grab them. Plus you can customize charts to match your app’s theme instead of getting whatever default styling the agent produces.
matplotlib patches are a pain. I just skip plots entirely and ask users for raw data instead, then use Streamlit’s built-in charts. When someone wants a bar chart, I change their request to “give me the data for a bar chart” and feed it straight into st.bar_chart(). way cleaner than wrestling with figure capture.
LangChain’s CSV agent creates matplotlib plots behind the scenes, but they won’t show up in Streamlit automatically. I hit this same issue last month.
Here’s what fixed it for me - you need to grab the matplotlib figure after the agent runs and push it to Streamlit manually. Drop this code after your agent.run() call:
import matplotlib.pyplot as plt
if question:
answer = csv_agent.run(question)
st.write("Answer:")
st.write(answer)
# Capture any matplotlib figures
fig = plt.gcf()
if fig.get_axes():
st.pyplot(fig)
plt.clf() # Clear figure for next use
This catches whatever plots LangChain makes and displays them in Streamlit. Use plt.gcf() to snag the current figure and st.pyplot() to show it. Don’t forget plt.clf() at the end or you’ll get overlapping charts when you run new queries.
I hit this same issue a few months ago. The matplotlib capture approach works sometimes, but it’s unreliable - LangChain uses different backends and figures don’t stick around long enough to catch. What worked better: intercept the plot generation commands directly. Override the agent’s execution environment to capture plotting calls before they run. I modified my agent setup to redirect plot commands through Streamlit’s native charting instead. Here’s the key - LangChain usually generates pandas plotting code, not pure matplotlib. Add this after creating your agent but before running queries:
Then check if your dataset has plot methods being called in the agent response. You’ll probably need to parse the generated code and execute it manually with proper Streamlit chart functions like st.line_chart() or st.bar_chart() depending on what the agent’s trying to create. This gives you way more control over chart formatting and ensures everything plays nice with Streamlit’s rendering.
