How to Fix PyTorch and Streamlit Errors in GitHub Codespaces

Introduction

Are you struggling to run Streamlit with PyTorch in GitHub Codespaces? I recently faced several frustrating issues while building my Healthcare Assistant Chatbot, from PyTorch runtime errors to Streamlit port issues.

After hours of debugging, I finally got everything working! 🎉 In this guide, I’ll Walk you through:
✅ Common errors when using PyTorch with Streamlit
✅ Step-by-step fixes for runtime errors
✅ How to switch from PyTorch to TensorFlow for Transformers models

Let’s dive in! 🚀

1️⃣ Streamlit Runs, But No App on the Provided Port

After running:

shCopyEditstreamlit run app.py --server.port 8501 --server.address 0.0.0.0

I couldn’t access the app in my browser. 😓

✅ Fix: Check Port Forwarding in GitHub Codespaces

Open the PORTS tab in Codespaces.
Ensure Port 8501 is listed and set to Public.
Use the GitHub-generated URL instead of localhost:8501.

2️⃣ PyTorch Runtime Error: "Tried to instantiate class 'path._path'"

This error appeared when running Streamlit:

plaintextCopyEditRuntimeError: Tried to instantiate class '__path__._path', but it does not exist!

It turns out Streamlit’s file watcher conflicts with PyTorch.

✅ Fix: Disable Streamlit File Watcher

Running Streamlit with this flag helped reduce errors:

shCopyEditstreamlit run app.py --server.port 8501 --server.address 0.0.0.0 --global.developmentMode=false

3️⃣ PyTorch CUDA Error in a CPU Environment

I was seeing CUDA-related errors, even though I wasn’t using a GPU! 🤯

✅ Fix: Install the CPU-Compatible PyTorch Version

If you’re in Codespaces (which doesn’t support CUDA), uninstall PyTorch and reinstall the CPU version:

shCopyEditpip uninstall torch -y
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu

4️⃣ Final Solution: Switching from PyTorch to TensorFlow

Even after fixing PyTorch, I kept running into conflicts with the Transformers library.
💡 Solution? Bypass PyTorch and use TensorFlow instead!

✅ Fix: Modify `app.py` to Use TensorFlow Instead of PyTorch

I updated my app.py like this:

pythonCopyEditimport os
os.environ["TRANSFORMERS_NO_TORCH"] = "1"  # Disable PyTorch in Transformers

import streamlit as st
from transformers import pipeline

chatbot = pipeline("text-generation", model="distilgpt2", framework="tf")  # Force TensorFlow

def main():
    st.title("Healthcare Assistant Chatbot")
    user_input = st.text_input("How can I help you today?")
    if st.button("Submit"):
        if user_input:
            response = chatbot(user_input, max_length=50)
            st.write("Bot:", response[0]['generated_text'])
        else:
            st.write("Please enter a message to get a response")

main()

Then, I installed TensorFlow:

shCopyEditpip install tensorflow

And finally, it worked! 🎉

💡 Key Lessons Learned

This debugging journey taught me:
✅ How to fix PyTorch conflicts with Streamlit
✅ The importance of checking CUDA vs. CPU compatibility
✅ How to force Transformers to use TensorFlow instead of PyTorch

🚀 Final Thoughts

If you’re struggling with PyTorch and Streamlit issues in GitHub Codespaces, I hope this guide saves you time! Debugging can be frustrating, but breaking the problem into smaller steps makes it manageable.

💬 Have you faced similar errors? Let’s discuss in the comments below!

How I Fixed PyTorch and Streamlit Errors in GitHub Codespaces (Step-by-Step Guide)