File "/home/appuser/venv/lib/python3.9/site-packages/streamlit/runtime/scriptrunner/script_runner.py", line 565, in _run_script
exec(code, module.__dict__)File "/app/ipl-first-innings-score-prediction/app.py", line 6, in <module>
pipe = pickle.load(open('E:/projects/ipl project/pipe.pkl','rb'))
please give solution for this error
If your streamlit app runs on a hosted environment, you don’t have access to the Client’s computer.
This error occurs while running a Streamlit app because the code is trying to load a pickle file from a specific location “E:/projects/ipl project/pipe.pkl”, but that location may not be accessible or may not exist in the current environment where the app is being run.
There are a few potential reasons why this error might be occurring:
To fix this error, you can try the following steps:
thanks sir i changed the file path because this problem is while deploy so i used only ‘pipe.pkl’ and in requirements.txt i added the scikit-learn because of pkl model file… issue is solved…thanks for helping i just made silly mistake.
Hey @Sarang_Gami,
Happy to help, Don’t worry for making mistake. Nobody is perfect. Keep up your work.
I am getting same error while working a lot on this, still it is not resolving. As I have check path of file , persmmission of files also. All of this are ok.
Please help i have deployed my clg project and same issue is showing it is not resolving at all tried so many times but the problem does not solve
i have given the correct path still ,Please help i have deployed my clg project and same issue is showing it is not resolving at all tried so many times but the problem does not solve 
Once again: If your streamlit app runs on a hosted environment, you don’t have access to the client’s computer, therefore you cannot use any paths pointing to a local file.
Thanks for reply but is there any way to solve this as I need to deploy the project it runs perfectly on my pc through cmd but can’t deploy
import numpy as np
import pickle
import streamlit as st
loaded_model = pickle.load(open(r"C:\Users\Admin\Desktop\ML MODEL\Vibration_Model.sav", ‘rb’))
scaler = pickle.load(open(r"C:\Users\Admin\Desktop\ML MODEL\scaler.sav", ‘rb’))
def vibration_prediction(input_data):
# Standardize the input data using the loaded scaler
input_data_reshaped = np.array(input_data).reshape(1, -1)
std_data = scaler.transform(input_data_reshaped)
# Predict using the loaded model
prediction = loaded_model.predict(std_data)
if prediction[0] == 0:
return 'Manmade'
else:
return 'Natural'
def main():
# Set background color to blue
st.markdown(
“”"
body {
background-color: #00BFFF;
color: #FFFFFF;
text-align: center;
}
.title {
color: #0000FF; /* Blue color */
font-size: 36px;
margin-bottom: 30px;
}
“”",
unsafe_allow_html=True
)
# Giving a title
st.markdown("<h1 class='title'>VIBRATION SOURCE PREDICTION</h1>", unsafe_allow_html=True)
# Getting input data from user
Vibration = st.text_input('Enter Vibration')
Amplitude = st.text_input('Enter Amplitude')
Duration = st.text_input('Enter Duration')
Peak_to_Peak_Vibration = st.text_input('Enter Peak-to-Peak Vibration')
# Creating button for prediction
if st.button('Predict Vibration Source', key='prediction_button'):
# Convert input data to float
try:
input_data = [float(Vibration), float(Amplitude), float(Duration), float(Peak_to_Peak_Vibration)]
diagnosis = vibration_prediction(input_data)
st.success(f"The vibration source is predicted to be: {diagnosis} origin ")
except ValueError:
st.error('Please enter valid numerical values for all input fields.')
if name == ‘main’:
main()
this the code working perfectly on local host but showing file not found error wheni deploy pls help me out
Put your model files also under git control and load them from there with a relative path instead of your local files absolute path. This will work both on your local computer and on streamlit cloud.
Thanks for replying i am just a beginner can you pls help me out this is my repository GitHub - larvae1/Vibration_ML_Model: ML Model to analyse the source of vibration
full code is :
import streamlit as st
import numpy as np
import pickle
loaded_model = pickle.load(open(r"C:\Users\Admin\Desktop\ML MODEL\Vibration_Model.sav", ‘rb’))
scaler = pickle.load(open(r"C:\Users\Admin\Desktop\ML MODEL\scaler.sav", ‘rb’))
def vibration_prediction(input_data):
# Standardize the input data using the loaded scaler
input_data_reshaped = np.array(input_data).reshape(1, -1)
std_data = scaler.transform(input_data_reshaped)
# Predict using the loaded model
prediction = loaded_model.predict(std_data)
if prediction[0] == 0:
return 'Manmade'
else:
return 'Natural'
def main():
# Set background color to blue
st.markdown(
“”"
body {
background-color: #00BFFF;
color: #FFFFFF;
text-align: center;
}
.title {
color: #0000FF; /* Blue color */
font-size: 36px;
margin-bottom: 30px;
}
“”",
unsafe_allow_html=True
)
# Giving a title
st.markdown("<h1 class='title'>VIBRATION SOURCE PREDICTION</h1>", unsafe_allow_html=True)
# Getting input data from user
Vibration = st.text_input('Enter Vibration')
Amplitude = st.text_input('Enter Amplitude')
Duration = st.text_input('Enter Duration')
Peak_to_Peak_Vibration = st.text_input('Enter Peak-to-Peak Vibration')
# Creating button for prediction
if st.button('Predict Vibration Source', key='prediction_button'):
# Convert input data to float
try:
input_data = [float(Vibration), float(Amplitude), float(Duration), float(Peak_to_Peak_Vibration)]
diagnosis = vibration_prediction(input_data)
st.success(f"The vibration source is predicted to be: {diagnosis} origin ")
except ValueError:
st.error('Please enter valid numerical values for all input fields.')
if name == ‘main’:
main()
very much thank you
Don’t use absolute paths.
Just use the relative paths to your model files e.g.
loaded_model = pickle.load(open("Vibration_Model.sav", "rb"))
...
scaler = pickle.load(open("scaler.sav", "rb"))
This works both local and on streamlit cloud.
please help it is really needed
typo: requirements.txt
can you elaborate pls? i did not understood
sir pls help me in its deployment
corrected the typo but still showing module not found sklearn but i have installed it
