I am not able to deploy my app through remote server however it was working fine with local system

Summary

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Steps to reproduce

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Expected behavior:

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Actual behavior:

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Debug info

  • Streamlit version: (get it with $ streamlit version)
  • Python version: (get it with $ python --version)
  • Using Conda? PipEnv? PyEnv? Pex?
  • OS version:
  • Browser version:

Requirements file

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Links

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Additional information

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i tried copying the url and pasting it is saying page can’t be reached tried every thing on internet please help.

Hi @Abhishek_kumar_singh :wave:,

Thanks for posting!

Could you provide more information by sharing your code? Please fill out the other portions of your initial forum post to help us replicate the issue and provide a solution.

Happy Streamlit-ing :balloon:

1 Like

i am not able to run my same streamlit app on remote ssh which is easily runable on my local system, when i tried opening it gives a url on which if i go displays page is not rechable. please tell the solution.

import numpy as np
import pandas as pd
import streamlit as st
#user_input = st.text_input(“please provide a query”, “two times 4.”)
###################################
with st.form(key=‘my_form’):
user_input = st.text_input(label=‘Enter some text’)
submit_button = st.form_submit_button(label=‘Submit’)
###################################
import os
import openai

openai.api_key = ‘###############################################’
start_sequence = “\nAI:”
restart_sequence = "\nHuman: "

response = openai.Completion.create(
model=“text-davinci-002”,
prompt=f"The following is a conversation with an AI assistant. The assistant is helpful, creative, clever, and very friendly.\n\nHuman: Hello, who are you?\nAI: I am an AI created by OpenAI. How can I help you today?\nHuman:{user_input} “,
temperature=0.9,
max_tokens=150,
top_p=1,
frequency_penalty=0,
presence_penalty=0.6,
stop=[” Human:“, " AI:”]
)
content = response.choices[0].text.split(’ ')[-1]
var = int(content)

querys = “number after 5.”

response = gpt3(querys)

var=response

print(var)

# type(var)

# z

In[122]:

response[‘choices’][0][‘text’]

In[19]:

import numpy as np

import matplotlib.pyplot as plt

In[2]:

from keras.datasets import mnist
from keras.models import Sequential, Model
from keras.layers import Dense, LeakyReLU, BatchNormalization
from keras.layers import Input, Flatten, Embedding, multiply, Dropout
from keras.optimizers import Adam
from keras import initializers

In[3]:

model = Sequential()
model.add(Embedding(10, 2))

the model will take as input an integer matrix of size (batch, input_length).

the largest integer (i.e. word index) in the input should be no larger than 999 (vocabulary size).

now model.output_shape == (None, 10, 64), where None is the batch dimension.

input_array = np.random.randint(10, size=(1, 10))

input_array = np.arange(0, 10).reshape(1, -1)
model.compile(‘rmsprop’, ‘mse’)
output_array = model.predict(input_array)
print(output_array)

print(output_array.shape)

plt.scatter(output_array[0, :, 0], output_array[0, :, 1])

In[4]:

load dataset

(X_train, y_train), (X_test, y_test) = mnist.load_data()

In[5]:

fig = plt.figure()
for i in range(10):
plt.subplot(2, 5, i+1)
x_y = X_train[y_train == i]
plt.imshow(x_y[0], cmap=‘gray’, interpolation=‘none’)
plt.title(“Class %d” % (i))
plt.xticks()
plt.yticks()

plt.tight_layout()

In[6]:

print(‘X_train.shape’, X_train.shape)
print(‘y_train.shape’, y_train.shape)

reshaping the inputs

X_train = X_train.reshape(60000, 28*28)

normalizing the inputs (-1, 1)

X_train = (X_train.astype(‘float32’) / 255 - 0.5) * 2

print(‘X_train reshape:’, X_train.shape)

In[7]:

latent space dimension

latent_dim = 100

imagem dimension 28x28

img_dim = 784

init = initializers.RandomNormal(stddev=0.02)

Generator network

generator = Sequential()

Input layer and hidden layer 1

generator.add(Dense(128, input_shape=(latent_dim,), kernel_initializer=init))
generator.add(LeakyReLU(alpha=0.2))
generator.add(BatchNormalization(momentum=0.8))

Hidden layer 2

generator.add(Dense(256))
generator.add(LeakyReLU(alpha=0.2))
generator.add(BatchNormalization(momentum=0.8))

Hidden layer 3

generator.add(Dense(512))
generator.add(LeakyReLU(alpha=0.2))
generator.add(BatchNormalization(momentum=0.8))

Output layer

generator.add(Dense(img_dim, activation=‘tanh’))

In[8]:

prints a summary representation of your model

generator.summary()

In[9]:

Embedding condition in input layer

num_classes = 10

Create label embeddings

label = Input(shape=(1,), dtype=‘int32’)
label_embedding = Embedding(num_classes, latent_dim)(label)
label_embedding = Flatten()(label_embedding)

latent space

z = Input(shape=(latent_dim,))

Merge inputs (z x label)

input_generator = multiply([z, label_embedding])

Output image

img = generator(input_generator)

Generator with condition input

generator = Model([z, label], img)

In[10]:

prints a summary representation of your model

generator.summary()

In[11]:

Discriminator network

discriminator = Sequential()

Input layer and hidden layer 1

discriminator.add(Dense(128, input_shape=(img_dim,), kernel_initializer=init))
discriminator.add(LeakyReLU(alpha=0.2))

Hidden layer 2

discriminator.add(Dense(256))
discriminator.add(LeakyReLU(alpha=0.2))

Hidden layer 3

discriminator.add(Dense(512))
discriminator.add(LeakyReLU(alpha=0.2))

Output layer

discriminator.add(Dense(1, activation=‘sigmoid’))

In[12]:

prints a summary representation of your model

discriminator.summary()

In[13]:

Embedding condition in input layer

Create label embeddings

label_d = Input(shape=(1,), dtype=‘int32’)
label_embedding_d = Embedding(num_classes, img_dim)(label_d)
label_embedding_d = Flatten()(label_embedding_d)

imagem dimension 28x28

img_d = Input(shape=(img_dim,))

Merge inputs (img x label)

input_discriminator = multiply([img_d, label_embedding_d])

Output image

validity = discriminator(input_discriminator)

Discriminator with condition input

discriminator = Model([img_d, label_d], validity)

In[14]:

prints a summary representation of your model

discriminator.summary()

In[15]:

Optimizer

optimizer = Adam(lr=0.0002, beta_1=0.5)

discriminator.compile(optimizer=optimizer, loss=‘binary_crossentropy’, metrics=[‘binary_accuracy’])

In[16]:

discriminator.trainable = False

validity = discriminator([generator([z, label]), label])

d_g = Model([z, label], validity)

d_g.compile(optimizer=optimizer, loss=‘binary_crossentropy’, metrics=[‘binary_accuracy’])

In[17]:

prints a summary representation of your model

d_g.summary()

In[134]:

epochs = 100
batch_size = 64
smooth = 0.1

real = np.ones(shape=(batch_size, 1))
fake = np.zeros(shape=(batch_size, 1))

d_loss =
d_g_loss =

for e in range(epochs + 1):
for i in range(len(X_train) // batch_size):

    # Train Discriminator weights
    discriminator.trainable = True
    
    # Real samples
    X_batch = X_train[i*batch_size:(i+1)*batch_size]
    real_labels = y_train[i*batch_size:(i+1)*batch_size].reshape(-1, 1)
    
    d_loss_real = discriminator.train_on_batch(x=[X_batch, real_labels], y=real * (1 - smooth))
    
    # Fake Samples
    z = np.random.normal(loc=0, scale=1, size=(batch_size, latent_dim))
    random_labels = np.random.randint(0, 10, batch_size).reshape(-1, 1)
    X_fake = generator.predict_on_batch([z, random_labels])
    
    d_loss_fake = discriminator.train_on_batch(x=[X_fake, random_labels], y=fake)
     
    # Discriminator loss
    d_loss_batch = 0.5 * (d_loss_real[0] + d_loss_fake[0])
    
    # Train Generator weights
    discriminator.trainable = False
    
    z = np.random.normal(loc=0, scale=1, size=(batch_size, latent_dim))
    random_labels = np.random.randint(0, 10, batch_size).reshape(-1, 1)
    d_g_loss_batch = d_g.train_on_batch(x=[z, random_labels], y=real)

    print(
        'epoch = %d/%d, batch = %d/%d, d_loss=%.3f, g_loss=%.3f' % (e + 1, epochs, i, len(X_train) // batch_size, d_loss_batch, d_g_loss_batch[0]),
        100*' ',
        end='\r'
    )

d_loss.append(d_loss_batch)
d_g_loss.append(d_g_loss_batch[0])
print('epoch = %d/%d, d_loss=%.3f, g_loss=%.3f' % (e + 1, epochs, d_loss[-1], d_g_loss[-1]), 100*' ')

if e % 10 == 0:
    samples = 10
    z = np.random.normal(loc=0, scale=1, size=(samples, latent_dim))
    # labels = np.arange(0, 1).reshape(-1, 1)
    y=[]
    yes = []
    yes.append(var)
    for i in range(10):
      y.append(yes)
    q = np.array(y)
    x_fake = generator.predict([z, q])

    for k in range(samples):
        fig = plt.figure(figsize=(12,6))
        plt.subplot(2, 5, k+1)
        plt.imshow(x_fake[k].reshape(28, 28), cmap='gray')
        plt.xticks([])
        plt.yticks([])

    plt.tight_layout()
    # plt.show()
    st.pyplot(fig)