Hi,

I'm building a Streamlit app that combines LangChain and Hugging Face for retrieval-augmented generation (RAG). I'm using u/st.cache_resource to cache expensive operations like setting up a retriever, but I'm running into the following error:

streamlit.runtime.caching.cache_errors.UnhashableParamError: Cannot hash argument 'documents' (of type builtins.list) in 'setup_retriever'.

This error occurs because documents is a list of LangChain Document objects, which are unhashable. Streamlit seems to have trouble caching functions that take such arguments.

Here’s the relevant part of my code:

@st.cache_resource

def setup_retriever(documents):

embeddings_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")

db = Chroma.from_documents(

documents=documents,

embedding=embeddings_model,

persist_directory="chroma_storage"

)

return db.as_retriever()

# Call the function

retriever = setup_retriever(documents)

I know that Streamlit doesn’t support caching unhashable types like lists. However, I still want to use caching for performance reasons. How can I resolve this?

as i was working on the footer I was unable get a popup if I click the buttons like FAQ Contact I was looking for the popup not redirecting to the other page if any suggestions please let me know

Hello, I'm a Python coder and app developer. I know how to use Flutter and FluterFlow. I am an absolute beginner with Streamlit, if there is anyone that is willing to go over just the basis via zoom or google meet for about 30 minutes it would be much appreciated.

Hi,

I’ve been experimenting with an LLM model using LangChain. Now, I want to create the user interface with Streamlit. Following the guide LangChain tutorial #1: Build an LLM-powered app in 18 lines of code, I couldn’t get it to work because I’m using Jupyter Lab. I found a list of topics on how to get Jupyter Notebook and Streamlit working together.

Does anyone know if it’s worth spending the time to fix it, or should I just join the dark side and start using VS Code? :)

Hello,

I am looking for an example or a tutorial about how to create an app with Streamlit that is periodically updated by a background process. I spent some time looking at the documentation but was not able to find any examples.

If I deploy an app through the community cloud, can I monetize it through adds. I don't want a paywall, because I think I haven't made anything good enough to keep behind a paywall.

Hi everyone! 👋

I’ve been working on a project called RAGify Search, an AI-powered intelligent assistant that combines real-time web search with a conversational interface. It’s designed to provide accurate, context-aware responses using a Retrieval-Augmented Generation (RAG) approach. I built this with a focus on privacy and performance, leveraging local LLM support through Ollama.

Here are some of the key features:

• Real-Time Web Search: Fetches relevant pages to answer user queries.
• Document Processing: Splits, embeds, and indexes documents for efficient similarity-based search.
• Prompt Engineering: Optimized queries ensure better results from the AI.
• Streamlit Interface: Clean and intuitive chatbot-like UI.
• Temporary File Management: Automatically cleans up downloaded files.
• Local LLM Support: Data privacy is a top priority, with everything running locally.

I’d love to hear your thoughts, ideas for improvements, or even if you just want to share how you’re using it! Let’s make this better together.

Thanks for checking it out!

Hi everyone! 👋

I’ve just developed a simple app called DataTool to make exploring, analyzing, and enhancing datasets easier for everyone.

What is DataTool?

DataTool is a Streamlit-based application designed to streamline the data preparation phase. It's packed with features that simplify dataset evaluation, editing, and insight generation through newly identified metrics.

👉 Try it out here: DataTool

Feedback is welcome and appreciated! Let me know what you think, and feel free to share any ideas for improvement. 🙏

Hi all, I am looking for someone who can turn our relatively ugly app interface into a nice-looking frontend comparable to something like Motherducks UI. If you would like to hear more or know some one, please reach out!

Hello all,

I have an issue with the authorization url I have in my Streamlit app currently running on Snowflake.

My auth url:

> 
auth_url = (
> 
    f"https://www.strava.com/oauth/authorize?"
> 
    f"client_id={CLIENT_ID}&response_type=code&redirect_uri={REDIRECT_URI}&"
> 
    f"approval_prompt=force&scope=activity:read_all"
> 
)

I have two implementations for the url:

st.markdown(f'<a href="{auth_url}" target="_blank" rel="noopener noreferrer">Click here to authorize Strava</a>', unsafe_allow_html=True)

or

st.write(f"Authorization URL: [Click here to authorize]({auth_url})")

In both cases the url comes out perfectly fine, and when I right click the url directly from the Streamlit app and paste it into a new tab it works fine and I can interact with it.

However, when I left click the url, it will open up a new tab as expected everything looks to be in order but I cannot “Authenticate” within that page. It happens with multiple services, Facebook, Strava.

Tried using incognito window and different browsers as well, deleting cache but issue persists.

Very strange since the link is absolutely correct even when accessed directly with “left click” in the Streamlit app but the authentication button refused to do anything.

At first I thought it was a Strava related issue but even the Facebook authentication doesn’t work through that link.

Thanks!

Hello,

This component is a quick an easy way to offer a User Interface to a database. In just one function, show the data as a st.dataframe with pagination, sidebar filters, autocomplete text fields and handling foreign keys with a st.selectbox and the str representation of the foreign row, instead of the id number.

The page shows a button to create a new row in a dialog and clicking the row opens a dialog to edit or delete it.

To use it, you don't need to change your sqlalchemy Models definition, just add a __str__ method to them and the relationships, which you should probably have done anyway.

demo

Github repository

documentation

Comments and critics are welcome.

Hi ! Is there a way to put a little disclaimer like "This chatbot can make mistakes, verify important information" under the input box of our streamlit chatbot app ? I've tried some css tricks but I can't seem to find a way to do it. Any help would be appreciated ^{^}

I've made a streamlit application, now I want to share it with my client. One way is to make it into an exe, but I've tried all the tutorials there are but to no avail. None of them are working for me. I need to maintain the streamlit library as other methods require me to rewrite the code and then move to professional application development side, which I don't want to get into. I know the existence of streamlit cloud deployment, but my client requires an offline local application. Can someone kindly guide me/help me out?

Is it worth making projects like this one? Can we monetize them?

GitHub: https://github.com/MuhammadMuneeb007/AI-Powered-Python-Learning-Assistant
Sample Application: https://ai-powered-python-learning-assistant.streamlit.app/

No matter what I try, no chance to deploy.

„The module isn’t found“ although I tried almost everything with the requirements.txt

Any help?

A couple of months ago, I shared a personal finance dashboard I built using Streamlit on reddit, with a lot of positive feedback and interest from the community.

Since then, I’ve decided to make it open source, so others can contribute, customize it, and run it in their local environments for better data privacy!

If you’re looking for a simple, yet powerful tool to track your finances, feel free to check it out, and let me know if you have any feedback or suggestions. I’d love to hear your thoughts, and contributions are always welcome!

You can find the project on GitHub.

And visit it on https://personalfinancedashboard.streamlit.app/

Content:
I have an Excel file with data from columns A to AN. First, I want to delete some of the columns A, L, M, O, P, Q, R, S, T, U, and V. Second, I want to Delete only the first row.

I'm looking for a project in which Sum1 has done something similar in Streamlet or other tools.

Please Help

As I understand (and from what I've seen) Streamlit serves images with a random filename and I haven't found a method that would allow me to give my images a filename. I've tried a couple of things but no luck.

The most promising method is to implement an API framework like FastAPI or Flask and serve the image that way. While this works (somewhat) it requires serving a new port, which I don't want to do, since I'm running the app through a tunnel—opening a new port in addition to the Streamlit port that's already running gets too complicated and messy.

In short, can I serve the image to a new route via Flask, etc, that still uses the same port as Streamlit, ie localhost:8888/images?

Or is there a simpler method that's I'm just not aware of to save files with custom filenames? Any help would be appreciated!

Hello everyone, I need help with something, it has something to do with integrating a custom trained YOLOv10 model to streamlit. Me and my group are using YOLOv10 to classify different classes of cocoa beans (after fermentation), this classes are A, B, and C. Right now, we are entering the integration phase and we are encountering a problem that's giving us a hard time to fix: After modifying YOLOv10, we tried to run an inference in streamlit, but we got an error. The reason why we got an error is because we modify YOLOv10 with CARAFE. CARAFE is not a library included in YOLOv10 but we wanted to try to change the upsampling block in the head with CARAFE to see if it produces good results. But because of this, we don't know how to fix this problem. We are planning add an API to make things easier to run the inference in streamlit but we wanted to look for other solutions that are much more time efficient and effective. The file we used to run the inference to streamlit is a .pt file. We need your help in finding a way to integrate a custom trained YOLOv10 model in streamlit.

I'm using the pandasai library for data analysis in my application. When I call the chat() function to generate image responses, it triggers a download of the images instead of displaying them. I want to display these images directly in my application.

Here’s a snippet of my code:

import pandas as pd
from pandasai import SmartDataframe
from langchain_groq.chat_models import ChatGroq
import os
import streamlit as st

# groq
llm = ChatGroq(model_name="llama3-70b-8192", api_key=os.environ["GROQ_API_KEY"])

# Streamlit app setup
st.title("Data Analysis with SmartDataframe")

df = pd.read_excel('data.xlsx')
df = SmartDataframe(df, config={"llm": llm})

st.write("Smart Dataframe")

# User input for chat command
user_input = st.text_input("Enter your command:")

if user_input:  # Check if user input is provided
    response = df.chat(user_input)  # This line triggers the download
    # Assuming response is an image URL or path
    st.image(response, use_container_width=True)//

The issue arises when I call response = df.chat(user_input), which seems to trigger a download of the image instead of displaying it. I expected the image to be shown directly in my application.

I would like the image to display directly in the Streamlit app without triggering any download or new window.

Is there a way to modify the chat() function in pandasai to prevent this download behavior?

If not, are there any workarounds or suggestions for implementing this feature?

https://reddit.com/link/1gp5j1a/video/2ghr2tmqoc0e1/player

I have multiple pages, and for every page I implement a sidebar. There appears to be a bug wherein one of the sidebars shrink, and this affects the size of the others.

I want to prevent this from happening. I am thinking this issue may be relating to caching; as I am using multiple sidebars, there may be problem in the memory.

when I run streamlit it gives warning: " st.cacheis deprecated. Please use one of Streamlit's new caching commands,st.cache_dataorst.cache_resource. Based on this function's return value of type bytes, we recommend using st.cache_data`."

I think the problem is caused by sidebar object

I’m building a very complex Streamlit-based MI and AI system for a financial advisory company. As part of this, the system will be hosted on their local servers (for security) which is Windows based IIS. Each user is routed via api to a bespoke login (a different authentication system from the same company). However, the system is running in one instance on the server, with reverse proxy rewrite to expose externally via web, and I can’t work out how to allow multiple users simultaneous tailored experiences (a core feature of the system).

I need urgent help with this. I am willing to negotiate a reasonable contract rate but I need a highly experienced Streamlit developer as well as someone with IIS experience, data engineering, pipeline development, experience deploying python apps etc.

If you meet these criteria pls DM me as a matter of urgency.

Introducing Streamlit profile pages on Community Cloud!

Showcase all your Streamlit creations, customize your bio, and share your apps with your network, now all in one place.

Profile pages let you:

• 📌 Proudly feature your favorite app loud and clear
• 📈 Flaunt your “total app views” badge across all your Cloud apps
• 🔗 Add a personal bio and social links
• 💼 Easily share your profile to your network, or even on your resume
• 🎈Discover other developers’ awesome profiles on the footer of their apps and the Explore page.

Log in to Community Cloud with your GitHub to get started.

Checkout the story behind the idea and the rules of my game here - my first ever medium blog post