Kamanda Wycliffe commited on
Commit
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1 Parent(s): 3ecd607

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CatDog.ipynb ADDED
The diff for this file is too large to render. See raw diff
Model Deployment CatDog.ipynb ADDED
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1
+ {
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+ "cells": [
3
+ {
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+ "cell_type": "code",
5
+ "execution_count": 1,
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+ "metadata": {},
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+ "outputs": [
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+ {
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+ "name": "stdout",
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+ "output_type": "stream",
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+ "text": [
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+ "Overwriting app.py\n"
13
+ ]
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+ }
15
+ ],
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+ "source": [
17
+ "%%writefile app.py\n",
18
+ "import streamlit as st\n",
19
+ "import tensorflow as tf\n",
20
+ "import cv2\n",
21
+ "from PIL import Image, ImageOps\n",
22
+ "import numpy as np\n",
23
+ "import matplotlib.pyplot as plt\n",
24
+ "import numpy as np\n",
25
+ "import os\n",
26
+ "import PIL\n",
27
+ "import tensorflow as tf\n",
28
+ "\n",
29
+ "from tensorflow import keras\n",
30
+ "from tensorflow.keras import layers\n",
31
+ "from tensorflow.keras.models import Sequential\n",
32
+ "\n",
33
+ "st.title(\"Cat-Dog Classification\")\n",
34
+ "st.header(\"Please input an image to be classified:\")\n",
35
+ "st.text(\"Created by Saksham Gulati\")\n",
36
+ "\n",
37
+ "@st.cache(allow_output_mutation=True)\n",
38
+ "\n",
39
+ "def teachable_machine_classification(img, weights_file):\n",
40
+ " # Load the model\n",
41
+ " model = keras.models.load_model(weights_file)\n",
42
+ "\n",
43
+ " # Create the array of the right shape to feed into the keras model\n",
44
+ " data = np.ndarray(shape=(1, 200, 200, 3), dtype=np.float32)\n",
45
+ " image = img\n",
46
+ " #image sizing\n",
47
+ " size = (200, 200)\n",
48
+ " image = ImageOps.fit(image, size, Image.ANTIALIAS)\n",
49
+ "\n",
50
+ " #turn the image into a numpy array\n",
51
+ " image_array = np.asarray(image)\n",
52
+ " # Normalize the image\n",
53
+ " normalized_image_array = (image_array.astype(np.float32) / 255)\n",
54
+ "\n",
55
+ " # Load the image into the array\n",
56
+ " data[0] = normalized_image_array\n",
57
+ "\n",
58
+ " # run the inference\n",
59
+ " prediction_percentage = model.predict(data)\n",
60
+ " prediction=prediction_percentage.round()\n",
61
+ " \n",
62
+ " return prediction,prediction_percentage\n",
63
+ "\n",
64
+ "\n",
65
+ "uploaded_file = st.file_uploader(\"Choose an Cat or Dog Image...\", type=\"jpg\")\n",
66
+ "\n",
67
+ "if uploaded_file is not None:\n",
68
+ " image = Image.open(uploaded_file)\n",
69
+ " st.image(image, caption='Uploaded file', use_column_width=True)\n",
70
+ " st.write(\"\")\n",
71
+ " st.write(\"Classifying...\")\n",
72
+ " label,perc = teachable_machine_classification(image, 'catdog.h5')\n",
73
+ " if label == 1:\n",
74
+ " st.write(\"Its a Dog, confidence level:\",perc)\n",
75
+ " else:\n",
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+ " st.write(\"Its a Cat, confidence level:\",1-perc)"
77
+ ]
78
+ },
79
+ {
80
+ "cell_type": "code",
81
+ "execution_count": null,
82
+ "metadata": {},
83
+ "outputs": [],
84
+ "source": []
85
+ }
86
+ ],
87
+ "metadata": {
88
+ "kernelspec": {
89
+ "display_name": "Python 3 (ipykernel)",
90
+ "language": "python",
91
+ "name": "python3"
92
+ },
93
+ "language_info": {
94
+ "codemirror_mode": {
95
+ "name": "ipython",
96
+ "version": 3
97
+ },
98
+ "file_extension": ".py",
99
+ "mimetype": "text/x-python",
100
+ "name": "python",
101
+ "nbconvert_exporter": "python",
102
+ "pygments_lexer": "ipython3",
103
+ "version": "3.9.12"
104
+ }
105
+ },
106
+ "nbformat": 4,
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+ "nbformat_minor": 2
108
+ }
Streamlit/app.py ADDED
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1
+ import streamlit as st
2
+ st.set_option('deprecation.showPyplotGlobalUse', False)
3
+ #st.markdown()
4
+ st.title("Document Title")
5
+ st.header("Article header")
6
+ st.subheader("Article subheader")
7
+ st.code("y = mx + c")
8
+ st.latex("\ int a y^2 \ , dy")
9
+ st.text("This is a chair!")
10
+ st.markdown('Staying hydrated is **_very_ cool**.')
11
+
12
+ students = ["Amelia Kami", "Antoinne Mark", "Peter Zen", "North Kim"]
13
+
14
+ marks = [82, 76, 96, 68]
15
+
16
+ import pandas as pd
17
+
18
+ df = pd.DataFrame()
19
+
20
+ df["Student Name"] = students
21
+
22
+ df["Marks"] = marks
23
+ #save to dataframe
24
+ df.to_csv("students.csv", index = False)
25
+ #display
26
+ st.dataframe(df)
27
+
28
+ #Static table
29
+ st.table(df)
30
+
31
+ #Metrics
32
+ st.metric("KPI", 56, 3)
33
+ #Json
34
+ st.json(df.to_dict())
35
+
36
+ #Code
37
+ #average of a list
38
+ code = '''def cal_average(numbers):
39
+ sum_number = 0
40
+ for t in numbers:
41
+ sum_number = sum_number + t
42
+
43
+ average = sum_number / len(numbers)
44
+ return average'''
45
+ st.code(code, language='python')
46
+ #progress bar
47
+
48
+ import streamlit as st
49
+ import time
50
+
51
+ # Sample Progress bar
52
+ #bar_p = st.progress(0)
53
+
54
+ #for percentage_complete in range(100):
55
+ #time.sleep(0.1)
56
+ #bar_p.progress(percentage_complete + 1)
57
+
58
+ #with st.spinner('Please wait...'):
59
+ #time.sleep(5)
60
+ #st.write('Complete!')
61
+
62
+
63
+ #Displaying an image using Streamlit
64
+ from PIL import Image
65
+ image = Image.open('media/ann-savchenko-H0h_89iFsWs-unsplash.jpg')
66
+
67
+ #st.image(image, caption='Sunset grass backgrounds')
68
+
69
+ #plotly
70
+ import plotly.express as px
71
+ # This dataframe has 244 rows, but 4 unique entries for the `day` variable
72
+ df = px.data.tips()
73
+ figx = px.pie(df, values='tip', names='day', title='Tips per day')
74
+ # Plot!
75
+ st.plotly_chart(figx, use_container_width=True)
76
+
77
+ #Altair
78
+ import altair as alt
79
+ import streamlit as st
80
+ import numpy as np
81
+
82
+ df = pd.DataFrame(
83
+ np.random.randn(300, 4),
84
+ columns=['a', 'b', 'c', 'd'])
85
+
86
+ chrt = alt.Chart(df).mark_circle().encode(
87
+ x='a', y='b', size='c', color='c', tooltip=['a', 'b', 'c', 'd'])
88
+
89
+ st.altair_chart(chrt, use_container_width=True)
90
+
91
+ #Matplotlib
92
+ import matplotlib.pyplot as plt
93
+ import numpy as np
94
+
95
+ arr = np.random.normal(1, 1, size=1000)
96
+ fig, ax = plt.subplots()
97
+ ax.hist(arr, bins=30)
98
+ plt.grid()
99
+ st.pyplot(fig)
100
+
101
+ #Interactive widgets
102
+ st.button("Click here")
103
+ #st.download_button("Download audio", file)
104
+ selected = st.checkbox("Accept terms")
105
+ choice = st.radio("Select one", ["Apples", "Oranges"])
106
+
107
+
108
+ option = st.selectbox(
109
+ 'How would you like to receive your package?',
110
+ ('By air', 'By sea', 'By rail'))
111
+
112
+ st.write('You selected:', option)
113
+ import datetime
114
+ day = st.date_input(
115
+ "When is your birthday?",
116
+ datetime.date(2022, 7, 6))
117
+ st.write('Your birthday is:', day)
118
+
119
+ color = st.color_picker('Choose A Color', '#00FFAA')
120
+ st.write('The selected color is', color)
121
+
122
+
123
+ @st.cache
124
+ def fetch_data():
125
+ df = pd.read_csv("students.csv")
126
+ return df
127
+
128
+ #data = fetch_data()
129
+
130
+ #Visualization
131
+
132
+ import matplotlib.pyplot as plt
133
+ import numpy as np
134
+
135
+ #Matplotlib
136
+
137
+ import matplotlib.pyplot as plt
138
+ import numpy as np
139
+ fig = plt.figure()
140
+ ax = fig.add_axes([0,0,1,1])
141
+ animals = ["Zebras", "Elephants", "Rhinos", "Leopards"]
142
+ number = [65, 72, 77, 59]
143
+ ax.bar(animals, number)
144
+ fig = plt.show()
145
+ st.pyplot(fig)
146
+ #Seaborn
147
+ import seaborn as sns
148
+ fig = plt.figure()
149
+ ax = sns.barplot(x = animals, y = number)
150
+ fig = plt.show()
151
+ st.pyplot(fig)
152
+
153
+ #Altair
154
+ #define data
155
+ df = pd.DataFrame()
156
+
157
+ df["Animals"] = animals
158
+ df["Number"] = number
159
+ #create chart
160
+ chrt = alt.Chart(df, title="Ploting using Altair in Streamlit").mark_bar().encode(
161
+ x='Animals',
162
+ y='Number'
163
+ )
164
+ #render with Streamlit
165
+ st.altair_chart(chrt, use_container_width=True)
166
+ #Plotly
167
+ #define data
168
+ df = pd.DataFrame()
169
+ df["Animals"] = animals
170
+ df["Number"] = number
171
+ #create plot
172
+ fig1 = px.bar(df, x='Animals', y='Number', title="Ploting using Plotly in Streamlit")
173
+ # Plot!
174
+ st.plotly_chart(fig1, use_container_width=True)
175
+
176
+ #data
177
+ df = pd.DataFrame()
178
+ df["Animals"] = animals
179
+ df["Number"] = number
180
+ #visualization
181
+ st.vega_lite_chart(df, {
182
+ 'mark': {'type': 'bar', 'tooltip': True},
183
+ 'encoding': {
184
+ 'x': {'field': 'Animals', 'type': 'nominal'},
185
+ 'y': {'field': 'Number', 'type': 'quantitative'},
186
+ },
187
+ }, use_container_width=True)
188
+
189
+ #Maps
190
+ import pandas as pd
191
+ states = pd.read_html('https://developers.google.com/public-data/docs/canonical/states_csv')[0]
192
+ states.columns = ['state', 'lat', 'lon', 'name']
193
+ states = states.drop(['state', 'name'], axis = 1)
194
+
195
+ st.map(states)
196
+
197
+ #Components
198
+ from st_aggrid import AgGrid
199
+ AgGrid(df)
200
+
201
+ #Statefulnness
202
+ import streamlit as st
203
+
204
+ st.title('Streamlit Counter Example')
205
+ count = 0
206
+
207
+ add = st.button('Addition')
208
+ if add:
209
+ count += 1
210
+
211
+ st.write('Count = ', count)
212
+
213
+
214
+ import streamlit as st
215
+
216
+ st.title('Counter Session State')
217
+ if 'count' not in st.session_state:
218
+ st.session_state.count = 0
219
+
220
+ increment = st.button('Add')
221
+ if increment:
222
+ st.session_state.count += 1
223
+
224
+ st.write('Count = ', st.session_state.count)
225
+
226
+ #Layout
227
+ col1, col2 = st.columns(2)
228
+
229
+ with col1:
230
+ st.altair_chart(chrt)
231
+ with col2:
232
+ st.plotly_chart(fig1, use_container_width=True)
233
+ with st.beta_container():
234
+ st.plotly_chart(figx, use_container_width=True)
235
+
236
+
237
+
238
+ #Add side widget
239
+
240
+ def your_widget(key):
241
+ st.subheader('Hi! Welcome')
242
+ return st.button(key + "Step")
243
+
244
+ # Displayed in the main area
245
+ clicked = your_widget("First")
246
+
247
+ # Shown within an expander
248
+ your_expander = st.expander("Expand", expanded=True)
249
+ with your_expander:
250
+ clicked = your_widget("Second")
251
+
252
+ # Shown in the st.sidebar!
253
+ with st.sidebar:
254
+ clicked = your_widget("Last")
255
+ #Session State
256
+ # Initialization
257
+ if 'key' not in st.session_state:
258
+ st.session_state['key'] = 'value'
259
+
260
+
261
+
262
+ # Session State also supports attribute based syntax
263
+ if 'key' not in st.session_state:
264
+ st.session_state.key = 'value'
265
+
266
+ st.session_state.key = 'value x' # New Attribute API
267
+ st.session_state['key'] = 'value x' # New Dictionary like API
268
+
269
+ st.write(st.session_state)
270
+
271
+ #Uploading files
272
+
273
+ import streamlit as st
274
+
275
+ #upload single file
276
+ file = st.file_uploader("Please select a file to upload")
277
+ if file is not None:
278
+ #Can be used wherever a "file-like" object is accepted:
279
+ df= pd.read_csv(file)
280
+ st.dataframe(df)
281
+
282
+ #Multiple files
283
+ #adding a file uploader to accept multiple CSV file
284
+ uploaded_files = st.file_uploader("Please select a CSV file", accept_multiple_files=True)
285
+ for file in uploaded_files:
286
+ df = pd.read_csv(file)
287
+ st.write("File uploaded:", file.name)
288
+ st.dataframe(df)
289
+ #Uploading and Processing
290
+ #upload single file
291
+ from PIL import Image
292
+ from PIL import ImageEnhance
293
+ def load_image(image):
294
+ img = Image.open(image)
295
+ return img
296
+
297
+ file = st.file_uploader("Please select image to upload and process")
298
+ if file is not None:
299
+ image = Image.open(file)
300
+ fig = plt.figure()
301
+ st.subheader("Original Image")
302
+ plt.imshow(image)
303
+ st.pyplot(fig)
304
+ fig = plt.figure()
305
+ contrast = ImageEnhance.Contrast(image).enhance(12)
306
+ plt.imshow(contrast)
307
+ st.subheader("Preprocessed Image")
308
+ st.pyplot(fig)
309
+
310
+
311
+ #Image classification
312
+ import keras
313
+ from PIL import Image, ImageOps
314
+ import numpy as np
315
+
316
+
317
+
318
+ import streamlit as st
319
+ import streamlit as st
320
+ from transformers import pipeline
321
+
322
+ '''Hugging Face'''
323
+
324
+ import streamlit as st
325
+ from transformers import pipeline
326
+
327
+ if __name__ == "__main__":
328
+
329
+ # Define the title of the and its description
330
+ st.title("Answering questions using NLP through Streamlit interface")
331
+ st.write("Pose questions, get answers")
332
+
333
+ # Load file
334
+
335
+ raw_text = st.text_area(label="Enter a text here")
336
+ if raw_text != None and raw_text != '':
337
+
338
+ # Display text
339
+ with st.expander("Show question"):
340
+ st.write(raw_text)
341
+
342
+ # Conduct question answering using the pipeline
343
+ question_answerer = pipeline('question-answering')
344
+
345
+ answer = ''
346
+ question = st.text_input('Ask a question')
347
+
348
+ if question != '' and raw_text != '':
349
+ answer = question_answerer({
350
+ 'question': question,
351
+ 'context': raw_text
352
+ })
353
+
354
+ st.write(answer)
355
+
356
+
357
+
358
+
359
+
360
+
361
+
362
+
363
+
364
+
365
+
Streamlit/hugginface.py ADDED
@@ -0,0 +1,33 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ '''Hugging Face'''
2
+
3
+ import streamlit as st
4
+ from transformers import pipeline
5
+
6
+ if __name__ == "__main__":
7
+
8
+ # Define the title of the and its description
9
+ st.title("Answering questions using NLP through Streamlit interface")
10
+ st.write("Pose questions, get answers")
11
+
12
+ # Load file
13
+
14
+ raw_text = st.text_area(label="Enter a text here")
15
+ if raw_text != None and raw_text != '':
16
+
17
+ # Display text
18
+ with st.expander("Show question"):
19
+ st.write(raw_text)
20
+
21
+ # Conduct question answering using the pipeline
22
+ question_answerer = pipeline('question-answering')
23
+
24
+ answer = ''
25
+ question = st.text_input('Ask a question')
26
+
27
+ if question != '' and raw_text != '':
28
+ answer = question_answerer({
29
+ 'question': question,
30
+ 'context': raw_text
31
+ })
32
+
33
+ st.write(answer)
Untitled.ipynb ADDED
@@ -0,0 +1,358 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "cells": [
3
+ {
4
+ "cell_type": "markdown",
5
+ "id": "f2054953",
6
+ "metadata": {},
7
+ "source": [
8
+ "### Introduction"
9
+ ]
10
+ },
11
+ {
12
+ "cell_type": "markdown",
13
+ "id": "defdb7b1",
14
+ "metadata": {},
15
+ "source": [
16
+ "#### Installing streamlit\n",
17
+ "Streamlit allows fast conversion of data scripts into web apps.\n",
18
+ "\n",
19
+ "TO get started you first need to have Streamlit installed. To do so, just run the command below:"
20
+ ]
21
+ },
22
+ {
23
+ "cell_type": "code",
24
+ "execution_count": null,
25
+ "id": "b89854fb",
26
+ "metadata": {},
27
+ "outputs": [],
28
+ "source": [
29
+ "pip install streamlit\n",
30
+ "#using conda\n",
31
+ "conda install streamlit"
32
+ ]
33
+ },
34
+ {
35
+ "cell_type": "markdown",
36
+ "id": "1506fa44",
37
+ "metadata": {},
38
+ "source": [
39
+ "#### How to run Streamlit applications\n",
40
+ "\n",
41
+ "To execute Streamlit apps, the following command is used:"
42
+ ]
43
+ },
44
+ {
45
+ "cell_type": "code",
46
+ "execution_count": null,
47
+ "id": "e9c700f5",
48
+ "metadata": {},
49
+ "outputs": [],
50
+ "source": [
51
+ "\n",
52
+ "file = st.file_uploader(\"Please select a file to upload\")\n",
53
+ "\n",
54
+ "if file is not None:\n",
55
+ "\n",
56
+ " #Read the file as bytes:\n",
57
+ "\n",
58
+ " bytes_df = file.getvalue()\n",
59
+ "\n",
60
+ " st.write(bytes_df)\n",
61
+ "\n",
62
+ " #Convert the file to a string-based IO:\n",
63
+ "\n",
64
+ " stringio = StringIO(file.getvalue().decode(\"utf-8\"))\n",
65
+ "\n",
66
+ " st.write(stringio)\n",
67
+ "\n",
68
+ " #Read the file as a string file:\n",
69
+ "\n",
70
+ " string_df = stringio.read()\n",
71
+ "\n",
72
+ " st.write(string_df)\n",
73
+ "\n",
74
+ " #Usable whenever a \"file-like\" object is accepted:\n",
75
+ "\n",
76
+ " data = pd.read_csv(file)\n",
77
+ "\n",
78
+ " st.write(data)\n"
79
+ ]
80
+ },
81
+ {
82
+ "cell_type": "code",
83
+ "execution_count": null,
84
+ "id": "ea8d494d",
85
+ "metadata": {},
86
+ "outputs": [],
87
+ "source": [
88
+ "#Add side widget\n",
89
+ "\n",
90
+ "def your_widget(key):\n",
91
+ " st.subheader('Hi! Welcome')\n",
92
+ " return st.button(key + \"Step\")\n",
93
+ "\n",
94
+ "# Displayed in the main area\n",
95
+ "clicked = your_widget(\"First\")\n",
96
+ "\n",
97
+ "# Shown within an expander\n",
98
+ "your_expander = st.expander(\"Expand\", expanded=True)\n",
99
+ "with your_expander:\n",
100
+ " clicked = your_widget(\"Second\")\n",
101
+ "\n",
102
+ "# Shown in the st.sidebar!\n",
103
+ "with st.sidebar:\n",
104
+ " clicked = your_widget(\"Last\")\n"
105
+ ]
106
+ },
107
+ {
108
+ "cell_type": "markdown",
109
+ "id": "ad037296",
110
+ "metadata": {},
111
+ "source": [
112
+ "### Streamlit Widgets"
113
+ ]
114
+ },
115
+ {
116
+ "cell_type": "code",
117
+ "execution_count": 3,
118
+ "id": "c05b2e95",
119
+ "metadata": {},
120
+ "outputs": [
121
+ {
122
+ "name": "stderr",
123
+ "output_type": "stream",
124
+ "text": [
125
+ "2022-08-07 19:15:58.314 INFO numexpr.utils: NumExpr defaulting to 4 threads.\n"
126
+ ]
127
+ }
128
+ ],
129
+ "source": [
130
+ "import streamlit as st"
131
+ ]
132
+ },
133
+ {
134
+ "cell_type": "code",
135
+ "execution_count": null,
136
+ "id": "839cd69d",
137
+ "metadata": {},
138
+ "outputs": [
139
+ {
140
+ "name": "stdout",
141
+ "output_type": "stream",
142
+ "text": [
143
+ "2022-08-08 14:43:23.739 INFO numexpr.utils: NumExpr defaulting to 4 threads.\n",
144
+ "\u001b[0m\n",
145
+ "\u001b[34m\u001b[1m You can now view your Streamlit app in your browser.\u001b[0m\n",
146
+ "\u001b[0m\n",
147
+ "\u001b[34m Local URL: \u001b[0m\u001b[1mhttp://localhost:8501\u001b[0m\n",
148
+ "\u001b[34m Network URL: \u001b[0m\u001b[1mhttp://10.16.0.5:8501\u001b[0m\n",
149
+ "\u001b[0m\n",
150
+ "app.py:144: UserWarning:\n",
151
+ "\n",
152
+ "Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.\n",
153
+ "\n",
154
+ "app.py:144: UserWarning:\n",
155
+ "\n",
156
+ "Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.\n",
157
+ "\n",
158
+ "app.py:150: UserWarning:\n",
159
+ "\n",
160
+ "Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.\n",
161
+ "\n",
162
+ "app.py:150: UserWarning:\n",
163
+ "\n",
164
+ "Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.\n",
165
+ "\n",
166
+ "2022-08-08 14:44:02.778127: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
167
+ "2022-08-08 14:44:02.778176: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n",
168
+ "2022-08-08 14:44:09.662 Uncaught app exception\n",
169
+ "Traceback (most recent call last):\n",
170
+ " File \"/home/kamanda/anaconda/lib/python3.9/site-packages/streamlit/scriptrunner/script_runner.py\", line 557, in _run_script\n",
171
+ " exec(code, module.__dict__)\n",
172
+ " File \"app.py\", line 320, in <module>\n",
173
+ " from transformers import pipeline\n",
174
+ "ImportError: cannot import name 'pipeline' from 'transformers' (/home/kamanda/anaconda/lib/python3.9/site-packages/transformers/__init__.py)\n"
175
+ ]
176
+ }
177
+ ],
178
+ "source": [
179
+ " #run an app\n",
180
+ "!streamlit run app.py"
181
+ ]
182
+ },
183
+ {
184
+ "cell_type": "code",
185
+ "execution_count": 4,
186
+ "id": "393dfc57",
187
+ "metadata": {},
188
+ "outputs": [
189
+ {
190
+ "data": {
191
+ "text/html": [
192
+ "<div>\n",
193
+ "<style scoped>\n",
194
+ " .dataframe tbody tr th:only-of-type {\n",
195
+ " vertical-align: middle;\n",
196
+ " }\n",
197
+ "\n",
198
+ " .dataframe tbody tr th {\n",
199
+ " vertical-align: top;\n",
200
+ " }\n",
201
+ "\n",
202
+ " .dataframe thead th {\n",
203
+ " text-align: right;\n",
204
+ " }\n",
205
+ "</style>\n",
206
+ "<table border=\"1\" class=\"dataframe\">\n",
207
+ " <thead>\n",
208
+ " <tr style=\"text-align: right;\">\n",
209
+ " <th></th>\n",
210
+ " <th>Student Name</th>\n",
211
+ " <th>Marks</th>\n",
212
+ " </tr>\n",
213
+ " </thead>\n",
214
+ " <tbody>\n",
215
+ " <tr>\n",
216
+ " <th>0</th>\n",
217
+ " <td>Amelia Kami</td>\n",
218
+ " <td>82</td>\n",
219
+ " </tr>\n",
220
+ " <tr>\n",
221
+ " <th>1</th>\n",
222
+ " <td>Antoinne Mark</td>\n",
223
+ " <td>76</td>\n",
224
+ " </tr>\n",
225
+ " <tr>\n",
226
+ " <th>2</th>\n",
227
+ " <td>Peter Zen</td>\n",
228
+ " <td>96</td>\n",
229
+ " </tr>\n",
230
+ " <tr>\n",
231
+ " <th>3</th>\n",
232
+ " <td>North Kim</td>\n",
233
+ " <td>68</td>\n",
234
+ " </tr>\n",
235
+ " </tbody>\n",
236
+ "</table>\n",
237
+ "</div>"
238
+ ],
239
+ "text/plain": [
240
+ " Student Name Marks\n",
241
+ "0 Amelia Kami 82\n",
242
+ "1 Antoinne Mark 76\n",
243
+ "2 Peter Zen 96\n",
244
+ "3 North Kim 68"
245
+ ]
246
+ },
247
+ "execution_count": 4,
248
+ "metadata": {},
249
+ "output_type": "execute_result"
250
+ }
251
+ ],
252
+ "source": [
253
+ "students = [\"Amelia Kami\", \"Antoinne Mark\", \"Peter Zen\", \"North Kim\"]\n",
254
+ "\n",
255
+ "marks = [82, 76, 96, 68]\n",
256
+ "\n",
257
+ "import pandas as pd\n",
258
+ "\n",
259
+ "df = pd.DataFrame()\n",
260
+ "\n",
261
+ "df[\"Student Name\"] = students\n",
262
+ "\n",
263
+ "df[\"Marks\"] = marks\n",
264
+ "df"
265
+ ]
266
+ },
267
+ {
268
+ "cell_type": "code",
269
+ "execution_count": 16,
270
+ "id": "e0965273",
271
+ "metadata": {},
272
+ "outputs": [
273
+ {
274
+ "data": {
275
+ "text/plain": [
276
+ "AgGridReturn(data= Animals Number\n",
277
+ "0 Zebras 65\n",
278
+ "1 Elephants 72\n",
279
+ "2 Rhinos 77\n",
280
+ "3 Leopards 59, selected_rows=[])"
281
+ ]
282
+ },
283
+ "execution_count": 16,
284
+ "metadata": {},
285
+ "output_type": "execute_result"
286
+ }
287
+ ],
288
+ "source": [
289
+ "import matplotlib.pyplot as plt\n",
290
+ "import numpy as np\n",
291
+ "fig = plt.figure()\n",
292
+ "ax = fig.add_axes([0,0,1,1])\n",
293
+ "animals = [\"Zebras\", \"Elephants\", \"Rhinos\", \"Leopards\"]\n",
294
+ "number = [65, 72, 77, 59]\n",
295
+ "ax.bar(animals, number)\n",
296
+ "fig = plt.show()\n",
297
+ "st.pyplot(fig)\n",
298
+ "#Seaborn\n",
299
+ "import seaborn as sns\n",
300
+ "fig = plt.figure()\n",
301
+ "ax = sns.barplot(x = animals, y = number)\n",
302
+ "fig = plt.show()\n",
303
+ "st.pyplot(fig)\n",
304
+ "\n",
305
+ "#Altair\n",
306
+ "#define data\n",
307
+ "df = pd.DataFrame()\n",
308
+ "\n",
309
+ "df[\"Animals\"] = animals\n",
310
+ "df[\"Number\"] = number\n",
311
+ "#Components\n",
312
+ "from st_aggrid import AgGrid\n",
313
+ "AgGrid(df)\n"
314
+ ]
315
+ },
316
+ {
317
+ "cell_type": "code",
318
+ "execution_count": 6,
319
+ "id": "c21f896b",
320
+ "metadata": {},
321
+ "outputs": [
322
+ {
323
+ "name": "stderr",
324
+ "output_type": "stream",
325
+ "text": [
326
+ "2022-08-07 19:30:27.154 \n",
327
+ " \u001b[33m\u001b[1mWarning:\u001b[0m to view this Streamlit app on a browser, run it with the following\n",
328
+ " command:\n",
329
+ "\n",
330
+ " streamlit run /home/kamanda/anaconda/lib/python3.9/site-packages/ipykernel_launcher.py [ARGUMENTS]\n"
331
+ ]
332
+ }
333
+ ],
334
+ "source": []
335
+ }
336
+ ],
337
+ "metadata": {
338
+ "kernelspec": {
339
+ "display_name": "Python 3 (ipykernel)",
340
+ "language": "python",
341
+ "name": "python3"
342
+ },
343
+ "language_info": {
344
+ "codemirror_mode": {
345
+ "name": "ipython",
346
+ "version": 3
347
+ },
348
+ "file_extension": ".py",
349
+ "mimetype": "text/x-python",
350
+ "name": "python",
351
+ "nbconvert_exporter": "python",
352
+ "pygments_lexer": "ipython3",
353
+ "version": "3.9.12"
354
+ }
355
+ },
356
+ "nbformat": 4,
357
+ "nbformat_minor": 5
358
+ }
Untitled1.ipynb ADDED
@@ -0,0 +1,6 @@
 
 
 
 
 
 
1
+ {
2
+ "cells": [],
3
+ "metadata": {},
4
+ "nbformat": 4,
5
+ "nbformat_minor": 5
6
+ }
Untitled2.ipynb ADDED
@@ -0,0 +1,6 @@
 
 
 
 
 
 
1
+ {
2
+ "cells": [],
3
+ "metadata": {},
4
+ "nbformat": 4,
5
+ "nbformat_minor": 5
6
+ }
app.py ADDED
@@ -0,0 +1,365 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import streamlit as st
2
+ st.set_option('deprecation.showPyplotGlobalUse', False)
3
+ #st.markdown()
4
+ st.title("Document Title")
5
+ st.header("Article header")
6
+ st.subheader("Article subheader")
7
+ st.code("y = mx + c")
8
+ st.latex("\ int a y^2 \ , dy")
9
+ st.text("This is a chair!")
10
+ st.markdown('Staying hydrated is **_very_ cool**.')
11
+
12
+ students = ["Amelia Kami", "Antoinne Mark", "Peter Zen", "North Kim"]
13
+
14
+ marks = [82, 76, 96, 68]
15
+
16
+ import pandas as pd
17
+
18
+ df = pd.DataFrame()
19
+
20
+ df["Student Name"] = students
21
+
22
+ df["Marks"] = marks
23
+ #save to dataframe
24
+ df.to_csv("students.csv", index = False)
25
+ #display
26
+ st.dataframe(df)
27
+
28
+ #Static table
29
+ st.table(df)
30
+
31
+ #Metrics
32
+ st.metric("KPI", 56, 3)
33
+ #Json
34
+ st.json(df.to_dict())
35
+
36
+ #Code
37
+ #average of a list
38
+ code = '''def cal_average(numbers):
39
+ sum_number = 0
40
+ for t in numbers:
41
+ sum_number = sum_number + t
42
+
43
+ average = sum_number / len(numbers)
44
+ return average'''
45
+ st.code(code, language='python')
46
+ #progress bar
47
+
48
+ import streamlit as st
49
+ import time
50
+
51
+ # Sample Progress bar
52
+ #bar_p = st.progress(0)
53
+
54
+ #for percentage_complete in range(100):
55
+ #time.sleep(0.1)
56
+ #bar_p.progress(percentage_complete + 1)
57
+
58
+ #with st.spinner('Please wait...'):
59
+ #time.sleep(5)
60
+ #st.write('Complete!')
61
+
62
+
63
+ #Displaying an image using Streamlit
64
+ from PIL import Image
65
+ image = Image.open('media/ann-savchenko-H0h_89iFsWs-unsplash.jpg')
66
+
67
+ #st.image(image, caption='Sunset grass backgrounds')
68
+
69
+ #plotly
70
+ import plotly.express as px
71
+ # This dataframe has 244 rows, but 4 unique entries for the `day` variable
72
+ df = px.data.tips()
73
+ figx = px.pie(df, values='tip', names='day', title='Tips per day')
74
+ # Plot!
75
+ st.plotly_chart(figx, use_container_width=True)
76
+
77
+ #Altair
78
+ import altair as alt
79
+ import streamlit as st
80
+ import numpy as np
81
+
82
+ df = pd.DataFrame(
83
+ np.random.randn(300, 4),
84
+ columns=['a', 'b', 'c', 'd'])
85
+
86
+ chrt = alt.Chart(df).mark_circle().encode(
87
+ x='a', y='b', size='c', color='c', tooltip=['a', 'b', 'c', 'd'])
88
+
89
+ st.altair_chart(chrt, use_container_width=True)
90
+
91
+ #Matplotlib
92
+ import matplotlib.pyplot as plt
93
+ import numpy as np
94
+
95
+ arr = np.random.normal(1, 1, size=1000)
96
+ fig, ax = plt.subplots()
97
+ ax.hist(arr, bins=30)
98
+ plt.grid()
99
+ st.pyplot(fig)
100
+
101
+ #Interactive widgets
102
+ st.button("Click here")
103
+ #st.download_button("Download audio", file)
104
+ selected = st.checkbox("Accept terms")
105
+ choice = st.radio("Select one", ["Apples", "Oranges"])
106
+
107
+
108
+ option = st.selectbox(
109
+ 'How would you like to receive your package?',
110
+ ('By air', 'By sea', 'By rail'))
111
+
112
+ st.write('You selected:', option)
113
+ import datetime
114
+ day = st.date_input(
115
+ "When is your birthday?",
116
+ datetime.date(2022, 7, 6))
117
+ st.write('Your birthday is:', day)
118
+
119
+ color = st.color_picker('Choose A Color', '#00FFAA')
120
+ st.write('The selected color is', color)
121
+
122
+
123
+ @st.cache
124
+ def fetch_data():
125
+ df = pd.read_csv("students.csv")
126
+ return df
127
+
128
+ #data = fetch_data()
129
+
130
+ #Visualization
131
+
132
+ import matplotlib.pyplot as plt
133
+ import numpy as np
134
+
135
+ #Matplotlib
136
+
137
+ import matplotlib.pyplot as plt
138
+ import numpy as np
139
+ fig = plt.figure()
140
+ ax = fig.add_axes([0,0,1,1])
141
+ animals = ["Zebras", "Elephants", "Rhinos", "Leopards"]
142
+ number = [65, 72, 77, 59]
143
+ ax.bar(animals, number)
144
+ fig = plt.show()
145
+ st.pyplot(fig)
146
+ #Seaborn
147
+ import seaborn as sns
148
+ fig = plt.figure()
149
+ ax = sns.barplot(x = animals, y = number)
150
+ fig = plt.show()
151
+ st.pyplot(fig)
152
+
153
+ #Altair
154
+ #define data
155
+ df = pd.DataFrame()
156
+
157
+ df["Animals"] = animals
158
+ df["Number"] = number
159
+ #create chart
160
+ chrt = alt.Chart(df, title="Ploting using Altair in Streamlit").mark_bar().encode(
161
+ x='Animals',
162
+ y='Number'
163
+ )
164
+ #render with Streamlit
165
+ st.altair_chart(chrt, use_container_width=True)
166
+ #Plotly
167
+ #define data
168
+ df = pd.DataFrame()
169
+ df["Animals"] = animals
170
+ df["Number"] = number
171
+ #create plot
172
+ fig1 = px.bar(df, x='Animals', y='Number', title="Ploting using Plotly in Streamlit")
173
+ # Plot!
174
+ st.plotly_chart(fig1, use_container_width=True)
175
+
176
+ #data
177
+ df = pd.DataFrame()
178
+ df["Animals"] = animals
179
+ df["Number"] = number
180
+ #visualization
181
+ st.vega_lite_chart(df, {
182
+ 'mark': {'type': 'bar', 'tooltip': True},
183
+ 'encoding': {
184
+ 'x': {'field': 'Animals', 'type': 'nominal'},
185
+ 'y': {'field': 'Number', 'type': 'quantitative'},
186
+ },
187
+ }, use_container_width=True)
188
+
189
+ #Maps
190
+ import pandas as pd
191
+ states = pd.read_html('https://developers.google.com/public-data/docs/canonical/states_csv')[0]
192
+ states.columns = ['state', 'lat', 'lon', 'name']
193
+ states = states.drop(['state', 'name'], axis = 1)
194
+
195
+ st.map(states)
196
+
197
+ #Components
198
+ from st_aggrid import AgGrid
199
+ AgGrid(df)
200
+
201
+ #Statefulnness
202
+ import streamlit as st
203
+
204
+ st.title('Streamlit Counter Example')
205
+ count = 0
206
+
207
+ add = st.button('Addition')
208
+ if add:
209
+ count += 1
210
+
211
+ st.write('Count = ', count)
212
+
213
+
214
+ import streamlit as st
215
+
216
+ st.title('Counter Session State')
217
+ if 'count' not in st.session_state:
218
+ st.session_state.count = 0
219
+
220
+ increment = st.button('Add')
221
+ if increment:
222
+ st.session_state.count += 1
223
+
224
+ st.write('Count = ', st.session_state.count)
225
+
226
+ #Layout
227
+ col1, col2 = st.columns(2)
228
+
229
+ with col1:
230
+ st.altair_chart(chrt)
231
+ with col2:
232
+ st.plotly_chart(fig1, use_container_width=True)
233
+ with st.beta_container():
234
+ st.plotly_chart(figx, use_container_width=True)
235
+
236
+
237
+
238
+ #Add side widget
239
+
240
+ def your_widget(key):
241
+ st.subheader('Hi! Welcome')
242
+ return st.button(key + "Step")
243
+
244
+ # Displayed in the main area
245
+ clicked = your_widget("First")
246
+
247
+ # Shown within an expander
248
+ your_expander = st.expander("Expand", expanded=True)
249
+ with your_expander:
250
+ clicked = your_widget("Second")
251
+
252
+ # Shown in the st.sidebar!
253
+ with st.sidebar:
254
+ clicked = your_widget("Last")
255
+ #Session State
256
+ # Initialization
257
+ if 'key' not in st.session_state:
258
+ st.session_state['key'] = 'value'
259
+
260
+
261
+
262
+ # Session State also supports attribute based syntax
263
+ if 'key' not in st.session_state:
264
+ st.session_state.key = 'value'
265
+
266
+ st.session_state.key = 'value x' # New Attribute API
267
+ st.session_state['key'] = 'value x' # New Dictionary like API
268
+
269
+ st.write(st.session_state)
270
+
271
+ #Uploading files
272
+
273
+ import streamlit as st
274
+
275
+ #upload single file
276
+ file = st.file_uploader("Please select a file to upload")
277
+ if file is not None:
278
+ #Can be used wherever a "file-like" object is accepted:
279
+ df= pd.read_csv(file)
280
+ st.dataframe(df)
281
+
282
+ #Multiple files
283
+ #adding a file uploader to accept multiple CSV file
284
+ uploaded_files = st.file_uploader("Please select a CSV file", accept_multiple_files=True)
285
+ for file in uploaded_files:
286
+ df = pd.read_csv(file)
287
+ st.write("File uploaded:", file.name)
288
+ st.dataframe(df)
289
+ #Uploading and Processing
290
+ #upload single file
291
+ from PIL import Image
292
+ from PIL import ImageEnhance
293
+ def load_image(image):
294
+ img = Image.open(image)
295
+ return img
296
+
297
+ file = st.file_uploader("Please select image to upload and process")
298
+ if file is not None:
299
+ image = Image.open(file)
300
+ fig = plt.figure()
301
+ st.subheader("Original Image")
302
+ plt.imshow(image)
303
+ st.pyplot(fig)
304
+ fig = plt.figure()
305
+ contrast = ImageEnhance.Contrast(image).enhance(12)
306
+ plt.imshow(contrast)
307
+ st.subheader("Preprocessed Image")
308
+ st.pyplot(fig)
309
+
310
+
311
+ #Image classification
312
+ import keras
313
+ from PIL import Image, ImageOps
314
+ import numpy as np
315
+
316
+
317
+
318
+ import streamlit as st
319
+ import streamlit as st
320
+ from transformers import pipeline
321
+
322
+ '''Hugging Face'''
323
+
324
+ import streamlit as st
325
+ from transformers import pipeline
326
+
327
+ if __name__ == "__main__":
328
+
329
+ # Define the title of the and its description
330
+ st.title("Answering questions using NLP through Streamlit interface")
331
+ st.write("Pose questions, get answers")
332
+
333
+ # Load file
334
+
335
+ raw_text = st.text_area(label="Enter a text here")
336
+ if raw_text != None and raw_text != '':
337
+
338
+ # Display text
339
+ with st.expander("Show question"):
340
+ st.write(raw_text)
341
+
342
+ # Conduct question answering using the pipeline
343
+ question_answerer = pipeline('question-answering')
344
+
345
+ answer = ''
346
+ question = st.text_input('Ask a question')
347
+
348
+ if question != '' and raw_text != '':
349
+ answer = question_answerer({
350
+ 'question': question,
351
+ 'context': raw_text
352
+ })
353
+
354
+ st.write(answer)
355
+
356
+
357
+
358
+
359
+
360
+
361
+
362
+
363
+
364
+
365
+
hugginface.py ADDED
@@ -0,0 +1,33 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ '''Hugging Face'''
2
+
3
+ import streamlit as st
4
+ from transformers import pipeline
5
+
6
+ if __name__ == "__main__":
7
+
8
+ # Define the title of the and its description
9
+ st.title("Answering questions using NLP through Streamlit interface")
10
+ st.write("Pose questions, get answers")
11
+
12
+ # Load file
13
+
14
+ raw_text = st.text_area(label="Enter a text here")
15
+ if raw_text != None and raw_text != '':
16
+
17
+ # Display text
18
+ with st.expander("Show question"):
19
+ st.write(raw_text)
20
+
21
+ # Conduct question answering using the pipeline
22
+ question_answerer = pipeline('question-answering')
23
+
24
+ answer = ''
25
+ question = st.text_input('Ask a question')
26
+
27
+ if question != '' and raw_text != '':
28
+ answer = question_answerer({
29
+ 'question': question,
30
+ 'context': raw_text
31
+ })
32
+
33
+ st.write(answer)
media/ann-savchenko-H0h_89iFsWs-unsplash.jpg ADDED
media/data science components ADDED
Binary file (12 kB). View file
media/data science components.png ADDED
students.csv ADDED
@@ -0,0 +1,5 @@
 
 
 
 
 
1
+ Student Name,Marks
2
+ Amelia Kami,82
3
+ Antoinne Mark,76
4
+ Peter Zen,96
5
+ North Kim,68
untitled.txt ADDED
File without changes