streamlit / demo-self-driving

Streamlit app demonstrating an image browser for the Udacity self-driving-car dataset with realtime object detection using YOLO.

Streamlit Demo: The Udacity Self-driving Car Image Browser

This project demonstrates the Udacity self-driving-car dataset and YOLO object detection into an interactive Streamlit app.

The complete demo is implemented in less than 300 lines of Python and illustrates all the major building blocks of Streamlit.

How to run this demo

pip install --upgrade streamlit opencv-python
streamlit run https://raw.githubusercontent.com/streamlit/demo-self-driving/master/streamlit_app.py

Questions? Comments?

Please ask in the Streamlit community.

demo-self-driving's Issues

AttributeError: module 'PIL.TiffTags' has no attribute 'IFD'

Summary

Hi. I am trying to run the app in https://github.com/streamlit/demo-self-driving, an example for streamlit application in object detection.

However, when running the app, I got an AttributeError. I have not downloaded the repo. Although, I am lauching the app directly from the github repo.

Steps to reproduce

I have followed the instructions in the the README demo:

1. Run "pip install --upgrade streamlit opencv-python" on linux terminal.
2. Run "streamlit run https://raw.githubusercontent.com/streamlit/demo-self-driving/master/streamlit_app.py"
   * on the README, "streamlit_app.py" is actually just "app.py", but I changed it since the file name in the repo has changed to "streamlit_app.py"
3. Open "localhost:8501" in browser.
4. Select "Run the app" in the sidebar in the left.

Expected behavior:

It is expected that the app will run and display the images with the object detection. Instead, I am getting this error.

Actual behavior:

When I select the "Run the app" option in the left sidebar, I get the following error:

Debug info

• Streamlit version: 0.69.2
• Python version: 3.8.3
• Using Conda 4.9.0
• OS version: Ubuntu 20.04

I'll be thankful if anyone can help.

Expired SSL Certificate

I was experimenting this project for the first time and got this error:

  You can now view your Streamlit app in your browser.

  Local URL: http://localhost:8501
  Network URL: http://192.168.2.201:8501

2022-06-11 02:58:54.238 Uncaught app exception
Traceback (most recent call last):
  File "C:\Python39\lib\urllib\request.py", line 1346, in do_open
    h.request(req.get_method(), req.selector, req.data, headers,
  File "C:\Python39\lib\http\client.py", line 1285, in request
    self._send_request(method, url, body, headers, encode_chunked)
  File "C:\Python39\lib\http\client.py", line 1331, in _send_request
    self.endheaders(body, encode_chunked=encode_chunked)
  File "C:\Python39\lib\http\client.py", line 1280, in endheaders
    self._send_output(message_body, encode_chunked=encode_chunked)
  File "C:\Python39\lib\http\client.py", line 1040, in _send_output
    self.send(msg)
  File "C:\Python39\lib\http\client.py", line 980, in send
    self.connect()
  File "C:\Python39\lib\http\client.py", line 1454, in connect
    self.sock = self._context.wrap_socket(self.sock,
  File "C:\Python39\lib\ssl.py", line 501, in wrap_socket
    return self.sslsocket_class._create(
  File "C:\Python39\lib\ssl.py", line 1041, in _create
    self.do_handshake()
  File "C:\Python39\lib\ssl.py", line 1310, in do_handshake
    self._sslobj.do_handshake()
ssl.SSLCertVerificationError: [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: certificate has expired (_ssl.c:1129)

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "C:\Python39\lib\site-packages\streamlit\scriptrunner\script_runner.py", line 554, in _run_script
    exec(code, module.__dict__)
  File "C:\Users\Helio\AppData\Local\Temp\tmps_c7nr8c\streamlit_app.py", line 294, in <module>
    main()
  File "C:\Users\Helio\AppData\Local\Temp\tmps_c7nr8c\streamlit_app.py", line 32, in main
    download_file(filename)
  File "C:\Users\Helio\AppData\Local\Temp\tmps_c7nr8c\streamlit_app.py", line 62, in download_file
    with urllib.request.urlopen(EXTERNAL_DEPENDENCIES[file_path]["url"]) as response:
  File "C:\Python39\lib\urllib\request.py", line 214, in urlopen
    return opener.open(url, data, timeout)
  File "C:\Python39\lib\urllib\request.py", line 517, in open
    response = self._open(req, data)
  File "C:\Python39\lib\urllib\request.py", line 534, in _open
    result = self._call_chain(self.handle_open, protocol, protocol +
  File "C:\Python39\lib\urllib\request.py", line 494, in _call_chain
    result = func(*args)
  File "C:\Python39\lib\urllib\request.py", line 1389, in https_open
    return self.do_open(http.client.HTTPSConnection, req,
  File "C:\Python39\lib\urllib\request.py", line 1349, in do_open
    raise URLError(err)
urllib.error.URLError: <urlopen error [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: certificate has expired (_ssl.c:1129)>

Windows 10 64bit, Python 3.9.13

The Side Bar to Run the project was not rendered.

design a White Wine classification machine learning web APP

python -m streamlit run 10890091_Stella_hw2.py

pip install streamlit

import pandas as pd
import streamlit as st
from sklearn import datasets
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.datasets import make_moons
import numpy as np
import math
from sklearn.svm import SVC

df = pd.read_csv("./winequality-white.csv", sep=';')
print(df.head(5))
print()

X = df.drop('quality', axis=1)
y = df['quality']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=8)

Suppose we train 3 models

model_list = ['Decision Tree (DT)', 'Support Vector Machine (SVM)', 'RandomForest(RF)']

we can create a selection box inside the APP

classifiers = st.selectbox("Which machine learning classifier model is used?", model_list)

Now, let' trained the model selected by the user from the model list

if classifiers == 'Decision Tree (DT)':
# train the decision tree
dt = DecisionTreeClassifier()
# we train the model with fit() function
dt.fit(X_train, y_train)
# after training the model, we can evaluate the model performance with test dataset
# we can calculate the prediction accuracy
accuracy = dt.score(X_test, y_test)
# we can also show the evaluation results on the APP
st.write("Classification accuracy: ", accuracy)

# we can make prediction with trained model
pred_dt = dt.predict(X_test)

# then, we can calculate the confusion matrix with predicted values and real values
con_matrix_dt = accuracy_score(y_test, pred_dt)
# lastly, we can show the confusion matrix results on the APP
st.write("Confusion Matrix: ", con_matrix_dt)
print("The decision tree model is trained successfully!")
print()

elif classifiers == 'Support Vector Machine (SVM)':
# train the SVM
svc = SVC()
# we train the model with fit() function
svc.fit(X_train, y_train)
# after training the model, we can evaluate the model performance with test dataset
# we can calculate the prediction accuracy
accuracy = svc.score(X_test, y_test)
# we can also show the evaluation results on the APP
st.write("Classification accuracy: ", accuracy)

# we can make prediction with trained model
pred_svc = svc.predict(X_test)

# then, we can calculate the confusion matrix with predicted values and real values
con_matrix_svc = accuracy_score(y_test, pred_svc)
# lastly, we can show the confusion matrix results on the APP
st.write("Confusion Matrix: ", con_matrix_svc)
print("SVC is trained successfully!")
print()

else:
# train an ensemble bagging learner (RandomForest)
rf_model = RandomForestClassifier()
# we train the model with fit() function
rf_model.fit(X_train, y_train)
# after training the model, we can evaluate the model performance with test dataset
# we can calculate the prediction accuracy
accuracy = rf_model.score(X_test, y_test)
# we can also show the evaluation results on the APP
st.write("Classification accuracy: ", accuracy)
pred_rf_model = rf_model.predict(X_test)
con_matrix_rf = accuracy_score(y_test, pred_rf_model)
st.write("Confusion Matrix: ", con_matrix_rf)
print("The ensemble bagging learner - RandomForrest is trained successfully!")
print()

main panel

st.title("White wine machine learning prediction!")

side panel

st.sidebar.header("User Input Parameters.")

def user_input_features():
fixed_acidity = st.sidebar.slider("Fixed Acidity", 6.0, 9.0, 7.0)
volatile_acidity = st.sidebar.slider("Volatile Acidity", 0.2, 0.4, 0.27)
citric_acid = st.sidebar.slider("Citric Acid", 0.3, 0.5, 0.36)
residual_sugar = st.sidebar.slider("Residual Sugar", 0.1, 25.0, 20.7)
chlorides = st.sidebar.slider("Chlorides", 0.04, 0.06)
free_sulfur_dioxide = st.sidebar.slider("Free Sulfur Dioxide", 17, 200, 170)
total_sulfur_dioxide = st.sidebar.slider("Total Sulfur Dioxide", 0.1, 3.0, 1.001)
density = st.sidebar.slider("Density", 0.8, 2.0, 1.001)
pH = st.sidebar.slider("pH", 1.0, 7.0, 3.0)
sulphates = st.sidebar.slider("Sulphates", 0.3, 0.5, 0.45)
alcohol = st.sidebar.slider("Alcohol", 1, 10, 6)

# create a data dictionary
data = {
    "fixed_acidity": float(fixed_acidity),
    "volatile_acidity": float(volatile_acidity),
    "citric_acid": float(citric_acid),
    "residual_sugar": float(residual_sugar),
    "chlorides": float(chlorides),
    "free_sulfur_dioxide": float(free_sulfur_dioxide),
    "total_sulfur_dioxide": float(total_sulfur_dioxide),
    "density": float(density),
    "pH": float(pH),
    "sulphates": float(sulphates),
    "alcohol": float(alcohol)
}
# create a dataframe with data stored in the dictionary
features_df = pd.DataFrame(data=data, index=[0])
return features_df

let's call the function and create the users' inputs

user_inputs = user_input_features()

show the inputs on the side panel

st.subheader("Users' Inputs")
st.write(user_inputs)

allow_output_mutation

"The ignore_hash argument has been renamed to allow_output_mutation."

error running app

I tried to use this app in a docker container and when I select the "run the app" app mode it doesn't work.

I get:

UnhashableType: Cannot hash object of type numpy.ufunc

While caching some code, Streamlit encountered an object of type numpy.ufunc. You’ll need to help Streamlit understand how to hash that type with the hash_funcs argument. For example:


@st.cache(hash_funcs={numpy.ufunc: my_hash_func})
def my_func(...):
    ...
Please see the hash_funcs documentation for more details.

Traceback:
  File "/tmp/tmprl6zpn8j/app.py", line 296, in <module>
    main()
  File "/tmp/tmprl6zpn8j/app.py", line 45, in main
    run_the_app()
  File "/tmp/tmprl6zpn8j/app.py", line 116, in run_the_app
    selected_frame_index, selected_frame = frame_selector_ui(summary)
  File "/tmp/tmprl6zpn8j/app.py", line 147, in frame_selector_ui
    selected_frames = get_selected_frames(summary, object_type, min_elts, max_elts)

I am using docker container python:latest and running:
pip install --upgrade streamlit opencv-python as specified in the readme.

Python is: Python 3.8.1

pip is installing numpy: numpy-1.18.1

any ideas?

[Install]

Traceback (most recent call last):
File "/Users/andreosee/anaconda3/bin/streamlit", line 6, in
from streamlit.cli import main
File "/Users/andreosee/anaconda3/lib/python3.7/site-packages/streamlit/init.py", line 99, in
from streamlit.delta_generator import DeltaGenerator as _DeltaGenerator
File "/Users/andreosee/anaconda3/lib/python3.7/site-packages/streamlit/delta_generator.py", line 25, in
from streamlit.proto import BlockPath_pb2
File "/Users/andreosee/anaconda3/lib/python3.7/site-packages/streamlit/proto/BlockPath_pb2.py", line 21, in
create_key=_descriptor._internal_create_key,
AttributeError: module 'google.protobuf.descriptor' has no attribute '_internal_create_key'
(base) ➜ ~

streamlit hello --- error

'gbk' codec can't decode byte 0x94 in position 9229: illegal multibyte sequence

Running streamlit run https://raw.githubusercontent.com/streamlit/demo-self-driving/master/app.py fails

Running streamlit run https://raw.githubusercontent.com/streamlit/demo-self-driving/master/app.py gives me this error:

E:\WPy-3710\python-3.7.1.amd64>streamlit run https://raw.githubusercontent.com/streamlit/demo-self-driving/master/app.py
Traceback (most recent call last):
  File "c:\python27\lib\runpy.py", line 174, in _run_module_as_main
    "__main__", fname, loader, pkg_name)
  File "c:\python27\lib\runpy.py", line 72, in _run_code
    exec code in run_globals
  File "C:\Python27\Scripts\streamlit.exe\__main__.py", line 9, in <module>
  File "c:\python27\lib\site-packages\click\core.py", line 764, in __call__
    return self.main(*args, **kwargs)
  File "c:\python27\lib\site-packages\click\core.py", line 717, in main
    rv = self.invoke(ctx)
  File "c:\python27\lib\site-packages\click\core.py", line 1137, in invoke
    return _process_result(sub_ctx.command.invoke(sub_ctx))
  File "c:\python27\lib\site-packages\click\core.py", line 956, in invoke
    return ctx.invoke(self.callback, **ctx.params)
  File "c:\python27\lib\site-packages\click\core.py", line 555, in invoke
    return callback(*args, **kwargs)
  File "c:\python27\lib\site-packages\streamlit\cli.py", line 149, in main_run
    _main_run(fp.name, args)
  File "c:\python27\lib\site-packages\streamlit\cli.py", line 179, in _main_run
    bootstrap.run(file, command_line, args)
  File "c:\python27\lib\site-packages\streamlit\bootstrap.py", line 181, in run
    server.add_preheated_report_session()
  File "c:\python27\lib\site-packages\streamlit\server\Server.py", line 393, in add_preheated_report_session
    session = self._add_browser_connection(PREHEATED_REPORT_SESSION)
  File "c:\python27\lib\site-packages\streamlit\server\Server.py", line 422, in _add_browser_connection
    command_line=self._command_line,
  File "c:\python27\lib\site-packages\streamlit\ReportSession.py", line 96, in __init__
    self._report, self._on_source_file_changed
  File "c:\python27\lib\site-packages\streamlit\watcher\LocalSourcesWatcher.py", line 87, in __init__
    module_name=None,  # Only the root script has None here.
  File "c:\python27\lib\site-packages\streamlit\watcher\LocalSourcesWatcher.py", line 110, in _register_watcher
    watcher=FileWatcher(filepath, self.on_file_changed), module_name=module_name
  File "c:\python27\lib\site-packages\streamlit\watcher\EventBasedFileWatcher.py", line 86, in __init__
    file_watcher.watch_file(file_path, on_file_changed)
  File "c:\python27\lib\site-packages\streamlit\watcher\EventBasedFileWatcher.py", line 160, in watch_file
    folder_handler.add_file_change_listener(file_path, callback)
  File "c:\python27\lib\site-packages\streamlit\watcher\EventBasedFileWatcher.py", line 248, in add_file_change_listener
    md5 = util.calc_md5_with_blocking_retries(file_path)
  File "c:\python27\lib\site-packages\streamlit\watcher\util.py", line 68, in calc_md5_with_blocking_retries
    raise e
IOError: [Errno 13] Permission denied: 'c:\\users\\user\\appdata\\local\\temp\\tmpi3xndk'

Debug info

Streamlit version: 0.47.2
Python version: 3.7.1
Using Conda? PipEnv? PyEnv? Pex? pip on WinPython
OS version: Windows 10 version 1903
Browser version: Firefox 69.0

ImportError: libGL.so.1: cannot open shared object file: No such file or directory

Short Story

This app doesn't work on streamlit sharing

Long Story

I am working on an object detection app with streamlit it works perfectly on my system but when i try to deploy it on Streamlit Sharing I get this error:

ImportError: libGL.so.1: cannot open shared object file: No such file or directory

Traceback:
File "/usr/local/lib/python3.7/site-packages/streamlit/script_runner.py", line 332, in _run_script
    exec(code, module.__dict__)
File "/app/demo-self-driving/streamlit_app.py", line 23, in <module>
    import os, urllib, cv2
File "/home/appuser/.local/lib/python3.7/site-packages/cv2/__init__.py", line 5, in <module>
    from .cv2 import *
x-special/nautilus-clipboard

I noticed the error had to do with opencv, so i decided to try the demo-self-driving app of streamlit to see how they handled this error, it turns out this app has the very same error, the app{demo-self-driving} works perfectly when i run:

streamlit run https://raw.githubusercontent.com/streamlit/demo-self-driving/master/streamlit_app.py

pip install failed, Failed to build watchdog

I'm trying to pip install streamlit on my local Mac in a new conda virtual environment. I'm getting this error:

Building wheels for collected packages: watchdog
  Building wheel for watchdog (setup.py) ... error
  ERROR: Command errored out with exit status 1:

And 2 more pages on red error logs, which I can provide if need be.

Any advice will be highly appreciated.

add a pipenv file

It would be great to have a pipenv file for this repo so that I can easily get the same dependencies as you did.