first commit

Model Deployment CatDog.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Overwriting app.py\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%writefile app.py\n",
+    "import streamlit as st\n",
+    "import tensorflow as tf\n",
+    "import cv2\n",
+    "from PIL import Image, ImageOps\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import PIL\n",
+    "import tensorflow as tf\n",
+    "\n",
+    "from tensorflow import keras\n",
+    "from tensorflow.keras import layers\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "\n",
+    "st.title(\"Cat-Dog Classification\")\n",
+    "st.header(\"Please input an image to be classified:\")\n",
+    "st.text(\"Created by Saksham Gulati\")\n",
+    "\n",
+    "@st.cache(allow_output_mutation=True)\n",
+    "\n",
+    "def teachable_machine_classification(img, weights_file):\n",
+    "    # Load the model\n",
+    "    model = keras.models.load_model(weights_file)\n",
+    "\n",
+    "    # Create the array of the right shape to feed into the keras model\n",
+    "    data = np.ndarray(shape=(1, 200, 200, 3), dtype=np.float32)\n",
+    "    image = img\n",
+    "    #image sizing\n",
+    "    size = (200, 200)\n",
+    "    image = ImageOps.fit(image, size, Image.ANTIALIAS)\n",
+    "\n",
+    "    #turn the image into a numpy array\n",
+    "    image_array = np.asarray(image)\n",
+    "    # Normalize the image\n",
+    "    normalized_image_array = (image_array.astype(np.float32) / 255)\n",
+    "\n",
+    "    # Load the image into the array\n",
+    "    data[0] = normalized_image_array\n",
+    "\n",
+    "    # run the inference\n",
+    "    prediction_percentage = model.predict(data)\n",
+    "    prediction=prediction_percentage.round()\n",
+    "    \n",
+    "    return  prediction,prediction_percentage\n",
+    "\n",
+    "\n",
+    "uploaded_file = st.file_uploader(\"Choose an Cat or Dog Image...\", type=\"jpg\")\n",
+    "\n",
+    "if uploaded_file is not None:\n",
+    "    image = Image.open(uploaded_file)\n",
+    "    st.image(image, caption='Uploaded file', use_column_width=True)\n",
+    "    st.write(\"\")\n",
+    "    st.write(\"Classifying...\")\n",
+    "    label,perc = teachable_machine_classification(image, 'catdog.h5')\n",
+    "    if label == 1:\n",
+    "        st.write(\"Its a Dog, confidence level:\",perc)\n",
+    "    else:\n",
+    "        st.write(\"Its a Cat, confidence level:\",1-perc)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

Streamlit/app.py

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+import streamlit as st
+st.set_option('deprecation.showPyplotGlobalUse', False)
+#st.markdown()
+st.title("Document Title")
+st.header("Article header")
+st.subheader("Article subheader")
+st.code("y = mx + c")
+st.latex("\ int a y^2 \ , dy")
+st.text("This is a chair!")
+st.markdown('Staying hydrated is **_very_ cool**.')
+
+students = ["Amelia Kami", "Antoinne Mark", "Peter Zen", "North Kim"]
+
+marks = [82, 76, 96, 68]
+
+import pandas as pd
+
+df = pd.DataFrame()
+
+df["Student Name"] = students
+
+df["Marks"] = marks
+#save to dataframe
+df.to_csv("students.csv", index = False)
+#display
+st.dataframe(df)
+
+#Static table
+st.table(df)
+
+#Metrics
+st.metric("KPI", 56, 3)
+#Json
+st.json(df.to_dict())
+
+#Code
+#average of a list
+code = '''def cal_average(numbers):
+    sum_number = 0
+    for t in numbers:
+        sum_number = sum_number + t           
+
+    average = sum_number / len(numbers)
+    return average'''
+st.code(code, language='python')
+#progress bar
+
+import streamlit as st
+import time
+
+# Sample Progress bar
+#bar_p = st.progress(0)
+
+#for percentage_complete in range(100):
+    #time.sleep(0.1)
+    #bar_p.progress(percentage_complete + 1)
+
+#with st.spinner('Please wait...'):
+    #time.sleep(5)
+#st.write('Complete!')
+
+
+#Displaying an image using Streamlit
+from PIL import Image
+image = Image.open('media/ann-savchenko-H0h_89iFsWs-unsplash.jpg')
+
+#st.image(image, caption='Sunset grass backgrounds')
+
+#plotly
+import plotly.express as px
+# This dataframe has 244 rows, but 4 unique entries for the `day` variable
+df = px.data.tips()
+figx = px.pie(df, values='tip', names='day', title='Tips per day')
+# Plot!
+st.plotly_chart(figx, use_container_width=True)
+
+#Altair
+import altair as alt
+import streamlit as st
+import numpy as np
+
+df = pd.DataFrame(
+     np.random.randn(300, 4),
+     columns=['a', 'b', 'c', 'd'])
+
+chrt = alt.Chart(df).mark_circle().encode(
+     x='a', y='b', size='c', color='c', tooltip=['a', 'b', 'c', 'd'])
+
+st.altair_chart(chrt, use_container_width=True)
+
+#Matplotlib
+import matplotlib.pyplot as plt
+import numpy as np
+
+arr = np.random.normal(1, 1, size=1000)
+fig, ax = plt.subplots()
+ax.hist(arr, bins=30)
+plt.grid()
+st.pyplot(fig)
+
+#Interactive widgets
+st.button("Click here")
+#st.download_button("Download audio", file)
+selected = st.checkbox("Accept terms")
+choice = st.radio("Select one", ["Apples", "Oranges"])
+
+
+option = st.selectbox(
+     'How would you like to receive your package?',
+     ('By air', 'By sea', 'By rail'))
+
+st.write('You selected:', option)
+import datetime
+day = st.date_input(
+     "When is your birthday?",
+     datetime.date(2022, 7, 6))
+st.write('Your birthday is:', day)
+
+color = st.color_picker('Choose A Color', '#00FFAA')
+st.write('The selected color is', color)
+
+
+@st.cache
+def fetch_data():
+    df = pd.read_csv("students.csv")
+    return df
+
+#data = fetch_data()
+
+#Visualization
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+#Matplotlib
+
+import matplotlib.pyplot as plt
+import numpy as np
+fig = plt.figure()
+ax = fig.add_axes([0,0,1,1])
+animals = ["Zebras", "Elephants", "Rhinos", "Leopards"]
+number = [65, 72, 77, 59]
+ax.bar(animals, number)
+fig = plt.show()
+st.pyplot(fig)
+#Seaborn
+import seaborn as sns
+fig = plt.figure()
+ax = sns.barplot(x = animals, y = number)
+fig = plt.show()
+st.pyplot(fig)
+
+#Altair
+#define data
+df = pd.DataFrame()
+
+df["Animals"] = animals
+df["Number"] = number
+#create chart
+chrt = alt.Chart(df, title="Ploting using Altair in Streamlit").mark_bar().encode(
+    x='Animals',
+    y='Number'
+)
+#render with Streamlit
+st.altair_chart(chrt, use_container_width=True)
+#Plotly
+#define data
+df = pd.DataFrame()
+df["Animals"] = animals
+df["Number"] = number
+#create plot
+fig1 = px.bar(df, x='Animals', y='Number', title="Ploting using Plotly in Streamlit")
+# Plot!
+st.plotly_chart(fig1, use_container_width=True)
+
+#data
+df = pd.DataFrame()
+df["Animals"] = animals
+df["Number"] = number
+#visualization
+st.vega_lite_chart(df, {
+     'mark': {'type': 'bar', 'tooltip': True},
+     'encoding': {
+         'x': {'field': 'Animals', 'type': 'nominal'},
+         'y': {'field': 'Number', 'type': 'quantitative'},
+     },
+ }, use_container_width=True)
+
+#Maps
+import pandas as pd
+states = pd.read_html('https://developers.google.com/public-data/docs/canonical/states_csv')[0]
+states.columns = ['state', 'lat', 'lon', 'name']
+states = states.drop(['state', 'name'], axis = 1)
+
+st.map(states)
+
+#Components
+from st_aggrid import AgGrid
+AgGrid(df)
+
+#Statefulnness
+import streamlit as st
+
+st.title('Streamlit Counter Example')
+count = 0
+
+add = st.button('Addition')
+if add:
+    count += 1
+
+st.write('Count = ', count)
+
+
+import streamlit as st
+
+st.title('Counter Session State')
+if 'count' not in st.session_state:
+    st.session_state.count = 0
+
+increment = st.button('Add')
+if increment:
+    st.session_state.count += 1
+
+st.write('Count = ', st.session_state.count)
+
+#Layout
+col1, col2 = st.columns(2)
+
+with col1:
+    st.altair_chart(chrt)
+with col2:
+    st.plotly_chart(fig1, use_container_width=True)
+with st.beta_container():
+    st.plotly_chart(figx, use_container_width=True)
+
+
+
+#Add side widget
+
+def your_widget(key):
+    st.subheader('Hi! Welcome')
+    return st.button(key + "Step")
+
+# Displayed in the main area
+clicked = your_widget("First")
+
+# Shown within an expander
+your_expander = st.expander("Expand", expanded=True)
+with your_expander:
+    clicked = your_widget("Second")
+
+# Shown in the st.sidebar!
+with st.sidebar:
+    clicked = your_widget("Last")
+#Session State
+# Initialization
+if 'key' not in st.session_state:
+    st.session_state['key'] = 'value'
+   
+    
+
+# Session State also supports attribute based syntax
+if 'key' not in st.session_state:
+    st.session_state.key = 'value'
+    
+st.session_state.key = 'value x'     # New Attribute API
+st.session_state['key'] = 'value x'  # New Dictionary like API
+
+st.write(st.session_state)
+
+#Uploading files
+
+import streamlit as st
+
+#upload single file
+file = st.file_uploader("Please select a file to upload")
+if file is not None:
+    #Can be used wherever a "file-like" object is accepted:
+    df= pd.read_csv(file)
+    st.dataframe(df)
+
+#Multiple files
+#adding a file uploader to accept multiple CSV file
+uploaded_files = st.file_uploader("Please select a CSV file", accept_multiple_files=True)
+for file in uploaded_files:
+    df = pd.read_csv(file)
+    st.write("File uploaded:", file.name)
+    st.dataframe(df)
+#Uploading and Processing
+#upload single file
+from PIL import Image
+from PIL import ImageEnhance
+def load_image(image):
+    img = Image.open(image)
+    return img
+
+file = st.file_uploader("Please select image to upload and process")
+if file is not None:
+    image = Image.open(file) 
+    fig = plt.figure()
+    st.subheader("Original Image")
+    plt.imshow(image)
+    st.pyplot(fig)
+    fig = plt.figure()
+    contrast = ImageEnhance.Contrast(image).enhance(12)
+    plt.imshow(contrast)
+    st.subheader("Preprocessed Image")
+    st.pyplot(fig)
+
+    
+#Image classification
+import keras
+from PIL import Image, ImageOps
+import numpy as np
+
+
+
+import streamlit as st
+import streamlit as st
+from transformers import pipeline
+
+'''Hugging Face'''
+
+import streamlit as st
+from transformers import pipeline
+
+if __name__ == "__main__":
+
+    # Define the title of the and its description
+    st.title("Answering questions using NLP through Streamlit interface")
+    st.write("Pose questions, get answers")
+
+    # Load file
+    
+    raw_text = st.text_area(label="Enter a text here")
+    if raw_text != None and raw_text != '':
+
+        # Display text
+        with st.expander("Show question"):
+            st.write(raw_text)
+
+        # Conduct question answering using the pipeline
+        question_answerer = pipeline('question-answering')
+
+        answer = ''
+        question = st.text_input('Ask a question')
+
+        if question != '' and raw_text != '':
+            answer = question_answerer({
+                'question': question,
+                'context': raw_text
+            })
+
+        st.write(answer)
+
+        
+        
+
+
+
+
+
+
+
+    

Streamlit/hugginface.py

@@ -0,0 +1,33 @@
+'''Hugging Face'''
+
+import streamlit as st
+from transformers import pipeline
+
+if __name__ == "__main__":
+
+    # Define the title of the and its description
+    st.title("Answering questions using NLP through Streamlit interface")
+    st.write("Pose questions, get answers")
+
+    # Load file
+    
+    raw_text = st.text_area(label="Enter a text here")
+    if raw_text != None and raw_text != '':
+
+        # Display text
+        with st.expander("Show question"):
+            st.write(raw_text)
+
+        # Conduct question answering using the pipeline
+        question_answerer = pipeline('question-answering')
+
+        answer = ''
+        question = st.text_input('Ask a question')
+
+        if question != '' and raw_text != '':
+            answer = question_answerer({
+                'question': question,
+                'context': raw_text
+            })
+
+        st.write(answer)

Untitled.ipynb

@@ -0,0 +1,358 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f2054953",
+   "metadata": {},
+   "source": [
+    "### Introduction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "defdb7b1",
+   "metadata": {},
+   "source": [
+    "#### Installing streamlit\n",
+    "Streamlit allows fast conversion of data scripts into web apps.\n",
+    "\n",
+    "TO get started you first need to have Streamlit installed. To do so, just run the command below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b89854fb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pip install streamlit\n",
+    "#using conda\n",
+    "conda install streamlit"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1506fa44",
+   "metadata": {},
+   "source": [
+    "#### How to run Streamlit applications\n",
+    "\n",
+    "To execute Streamlit apps, the following command is used:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e9c700f5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "file = st.file_uploader(\"Please select a file to upload\")\n",
+    "\n",
+    "if file is not None:\n",
+    "\n",
+    "    #Read the file as bytes:\n",
+    "\n",
+    "    bytes_df = file.getvalue()\n",
+    "\n",
+    "    st.write(bytes_df)\n",
+    "\n",
+    "    #Convert the file to a string-based IO:\n",
+    "\n",
+    "    stringio = StringIO(file.getvalue().decode(\"utf-8\"))\n",
+    "\n",
+    "    st.write(stringio)\n",
+    "\n",
+    "    #Read the file as a string file:\n",
+    "\n",
+    "    string_df = stringio.read()\n",
+    "\n",
+    "    st.write(string_df)\n",
+    "\n",
+    "    #Usable whenever a \"file-like\" object is accepted:\n",
+    "\n",
+    "    data = pd.read_csv(file)\n",
+    "\n",
+    "    st.write(data)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ea8d494d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Add side widget\n",
+    "\n",
+    "def your_widget(key):\n",
+    "    st.subheader('Hi! Welcome')\n",
+    "    return st.button(key + \"Step\")\n",
+    "\n",
+    "# Displayed in the main area\n",
+    "clicked = your_widget(\"First\")\n",
+    "\n",
+    "# Shown within an expander\n",
+    "your_expander = st.expander(\"Expand\", expanded=True)\n",
+    "with your_expander:\n",
+    "    clicked = your_widget(\"Second\")\n",
+    "\n",
+    "# Shown in the st.sidebar!\n",
+    "with st.sidebar:\n",
+    "    clicked = your_widget(\"Last\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ad037296",
+   "metadata": {},
+   "source": [
+    "### Streamlit Widgets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c05b2e95",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-08-07 19:15:58.314 INFO    numexpr.utils: NumExpr defaulting to 4 threads.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import streamlit as st"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "839cd69d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2022-08-08 14:43:23.739 INFO    numexpr.utils: NumExpr defaulting to 4 threads.\n",
+      "\u001b[0m\n",
+      "\u001b[34m\u001b[1m  You can now view your Streamlit app in your browser.\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[34m  Local URL: \u001b[0m\u001b[1mhttp://localhost:8501\u001b[0m\n",
+      "\u001b[34m  Network URL: \u001b[0m\u001b[1mhttp://10.16.0.5:8501\u001b[0m\n",
+      "\u001b[0m\n",
+      "app.py:144: UserWarning:\n",
+      "\n",
+      "Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.\n",
+      "\n",
+      "app.py:144: UserWarning:\n",
+      "\n",
+      "Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.\n",
+      "\n",
+      "app.py:150: UserWarning:\n",
+      "\n",
+      "Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.\n",
+      "\n",
+      "app.py:150: UserWarning:\n",
+      "\n",
+      "Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.\n",
+      "\n",
+      "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",
+      "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",
+      "2022-08-08 14:44:09.662 Uncaught app exception\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/home/kamanda/anaconda/lib/python3.9/site-packages/streamlit/scriptrunner/script_runner.py\", line 557, in _run_script\n",
+      "    exec(code, module.__dict__)\n",
+      "  File \"app.py\", line 320, in <module>\n",
+      "    from transformers import pipeline\n",
+      "ImportError: cannot import name 'pipeline' from 'transformers' (/home/kamanda/anaconda/lib/python3.9/site-packages/transformers/__init__.py)\n"
+     ]
+    }
+   ],
+   "source": [
+    "    #run an app\n",
+    "!streamlit run app.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "393dfc57",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Student Name</th>\n",
+       "      <th>Marks</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Amelia Kami</td>\n",
+       "      <td>82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Antoinne Mark</td>\n",
+       "      <td>76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Peter Zen</td>\n",
+       "      <td>96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>North Kim</td>\n",
+       "      <td>68</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Student Name  Marks\n",
+       "0    Amelia Kami     82\n",
+       "1  Antoinne Mark     76\n",
+       "2      Peter Zen     96\n",
+       "3      North Kim     68"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "students = [\"Amelia Kami\", \"Antoinne Mark\", \"Peter Zen\", \"North Kim\"]\n",
+    "\n",
+    "marks = [82, 76, 96, 68]\n",
+    "\n",
+    "import pandas as pd\n",
+    "\n",
+    "df = pd.DataFrame()\n",
+    "\n",
+    "df[\"Student Name\"] = students\n",
+    "\n",
+    "df[\"Marks\"] = marks\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "e0965273",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AgGridReturn(data=     Animals  Number\n",
+       "0     Zebras      65\n",
+       "1  Elephants      72\n",
+       "2     Rhinos      77\n",
+       "3   Leopards      59, selected_rows=[])"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "fig = plt.figure()\n",
+    "ax = fig.add_axes([0,0,1,1])\n",
+    "animals = [\"Zebras\", \"Elephants\", \"Rhinos\", \"Leopards\"]\n",
+    "number = [65, 72, 77, 59]\n",
+    "ax.bar(animals, number)\n",
+    "fig = plt.show()\n",
+    "st.pyplot(fig)\n",
+    "#Seaborn\n",
+    "import seaborn as sns\n",
+    "fig = plt.figure()\n",
+    "ax = sns.barplot(x = animals, y = number)\n",
+    "fig = plt.show()\n",
+    "st.pyplot(fig)\n",
+    "\n",
+    "#Altair\n",
+    "#define data\n",
+    "df = pd.DataFrame()\n",
+    "\n",
+    "df[\"Animals\"] = animals\n",
+    "df[\"Number\"] = number\n",
+    "#Components\n",
+    "from st_aggrid import AgGrid\n",
+    "AgGrid(df)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "c21f896b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-08-07 19:30:27.154 \n",
+      "  \u001b[33m\u001b[1mWarning:\u001b[0m to view this Streamlit app on a browser, run it with the following\n",
+      "  command:\n",
+      "\n",
+      "    streamlit run /home/kamanda/anaconda/lib/python3.9/site-packages/ipykernel_launcher.py [ARGUMENTS]\n"
+     ]
+    }
+   ],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Untitled1.ipynb

@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Untitled2.ipynb

@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

app.py

@@ -0,0 +1,365 @@
+import streamlit as st
+st.set_option('deprecation.showPyplotGlobalUse', False)
+#st.markdown()
+st.title("Document Title")
+st.header("Article header")
+st.subheader("Article subheader")
+st.code("y = mx + c")
+st.latex("\ int a y^2 \ , dy")
+st.text("This is a chair!")
+st.markdown('Staying hydrated is **_very_ cool**.')
+
+students = ["Amelia Kami", "Antoinne Mark", "Peter Zen", "North Kim"]
+
+marks = [82, 76, 96, 68]
+
+import pandas as pd
+
+df = pd.DataFrame()
+
+df["Student Name"] = students
+
+df["Marks"] = marks
+#save to dataframe
+df.to_csv("students.csv", index = False)
+#display
+st.dataframe(df)
+
+#Static table
+st.table(df)
+
+#Metrics
+st.metric("KPI", 56, 3)
+#Json
+st.json(df.to_dict())
+
+#Code
+#average of a list
+code = '''def cal_average(numbers):
+    sum_number = 0
+    for t in numbers:
+        sum_number = sum_number + t           
+
+    average = sum_number / len(numbers)
+    return average'''
+st.code(code, language='python')
+#progress bar
+
+import streamlit as st
+import time
+
+# Sample Progress bar
+#bar_p = st.progress(0)
+
+#for percentage_complete in range(100):
+    #time.sleep(0.1)
+    #bar_p.progress(percentage_complete + 1)
+
+#with st.spinner('Please wait...'):
+    #time.sleep(5)
+#st.write('Complete!')
+
+
+#Displaying an image using Streamlit
+from PIL import Image
+image = Image.open('media/ann-savchenko-H0h_89iFsWs-unsplash.jpg')
+
+#st.image(image, caption='Sunset grass backgrounds')
+
+#plotly
+import plotly.express as px
+# This dataframe has 244 rows, but 4 unique entries for the `day` variable
+df = px.data.tips()
+figx = px.pie(df, values='tip', names='day', title='Tips per day')
+# Plot!
+st.plotly_chart(figx, use_container_width=True)
+
+#Altair
+import altair as alt
+import streamlit as st
+import numpy as np
+
+df = pd.DataFrame(
+     np.random.randn(300, 4),
+     columns=['a', 'b', 'c', 'd'])
+
+chrt = alt.Chart(df).mark_circle().encode(
+     x='a', y='b', size='c', color='c', tooltip=['a', 'b', 'c', 'd'])
+
+st.altair_chart(chrt, use_container_width=True)
+
+#Matplotlib
+import matplotlib.pyplot as plt
+import numpy as np
+
+arr = np.random.normal(1, 1, size=1000)
+fig, ax = plt.subplots()
+ax.hist(arr, bins=30)
+plt.grid()
+st.pyplot(fig)
+
+#Interactive widgets
+st.button("Click here")
+#st.download_button("Download audio", file)
+selected = st.checkbox("Accept terms")
+choice = st.radio("Select one", ["Apples", "Oranges"])
+
+
+option = st.selectbox(
+     'How would you like to receive your package?',
+     ('By air', 'By sea', 'By rail'))
+
+st.write('You selected:', option)
+import datetime
+day = st.date_input(
+     "When is your birthday?",
+     datetime.date(2022, 7, 6))
+st.write('Your birthday is:', day)
+
+color = st.color_picker('Choose A Color', '#00FFAA')
+st.write('The selected color is', color)
+
+
+@st.cache
+def fetch_data():
+    df = pd.read_csv("students.csv")
+    return df
+
+#data = fetch_data()
+
+#Visualization
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+#Matplotlib
+
+import matplotlib.pyplot as plt
+import numpy as np
+fig = plt.figure()
+ax = fig.add_axes([0,0,1,1])
+animals = ["Zebras", "Elephants", "Rhinos", "Leopards"]
+number = [65, 72, 77, 59]
+ax.bar(animals, number)
+fig = plt.show()
+st.pyplot(fig)
+#Seaborn
+import seaborn as sns
+fig = plt.figure()
+ax = sns.barplot(x = animals, y = number)
+fig = plt.show()
+st.pyplot(fig)
+
+#Altair
+#define data
+df = pd.DataFrame()
+
+df["Animals"] = animals
+df["Number"] = number
+#create chart
+chrt = alt.Chart(df, title="Ploting using Altair in Streamlit").mark_bar().encode(
+    x='Animals',
+    y='Number'
+)
+#render with Streamlit
+st.altair_chart(chrt, use_container_width=True)
+#Plotly
+#define data
+df = pd.DataFrame()
+df["Animals"] = animals
+df["Number"] = number
+#create plot
+fig1 = px.bar(df, x='Animals', y='Number', title="Ploting using Plotly in Streamlit")
+# Plot!
+st.plotly_chart(fig1, use_container_width=True)
+
+#data
+df = pd.DataFrame()
+df["Animals"] = animals
+df["Number"] = number
+#visualization
+st.vega_lite_chart(df, {
+     'mark': {'type': 'bar', 'tooltip': True},
+     'encoding': {
+         'x': {'field': 'Animals', 'type': 'nominal'},
+         'y': {'field': 'Number', 'type': 'quantitative'},
+     },
+ }, use_container_width=True)
+
+#Maps
+import pandas as pd
+states = pd.read_html('https://developers.google.com/public-data/docs/canonical/states_csv')[0]
+states.columns = ['state', 'lat', 'lon', 'name']
+states = states.drop(['state', 'name'], axis = 1)
+
+st.map(states)
+
+#Components
+from st_aggrid import AgGrid
+AgGrid(df)
+
+#Statefulnness
+import streamlit as st
+
+st.title('Streamlit Counter Example')
+count = 0
+
+add = st.button('Addition')
+if add:
+    count += 1
+
+st.write('Count = ', count)
+
+
+import streamlit as st
+
+st.title('Counter Session State')
+if 'count' not in st.session_state:
+    st.session_state.count = 0
+
+increment = st.button('Add')
+if increment:
+    st.session_state.count += 1
+
+st.write('Count = ', st.session_state.count)
+
+#Layout
+col1, col2 = st.columns(2)
+
+with col1:
+    st.altair_chart(chrt)
+with col2:
+    st.plotly_chart(fig1, use_container_width=True)
+with st.beta_container():
+    st.plotly_chart(figx, use_container_width=True)
+
+
+
+#Add side widget
+
+def your_widget(key):
+    st.subheader('Hi! Welcome')
+    return st.button(key + "Step")
+
+# Displayed in the main area
+clicked = your_widget("First")
+
+# Shown within an expander
+your_expander = st.expander("Expand", expanded=True)
+with your_expander:
+    clicked = your_widget("Second")
+
+# Shown in the st.sidebar!
+with st.sidebar:
+    clicked = your_widget("Last")
+#Session State
+# Initialization
+if 'key' not in st.session_state:
+    st.session_state['key'] = 'value'
+   
+    
+
+# Session State also supports attribute based syntax
+if 'key' not in st.session_state:
+    st.session_state.key = 'value'
+    
+st.session_state.key = 'value x'     # New Attribute API
+st.session_state['key'] = 'value x'  # New Dictionary like API
+
+st.write(st.session_state)
+
+#Uploading files
+
+import streamlit as st
+
+#upload single file
+file = st.file_uploader("Please select a file to upload")
+if file is not None:
+    #Can be used wherever a "file-like" object is accepted:
+    df= pd.read_csv(file)
+    st.dataframe(df)
+
+#Multiple files
+#adding a file uploader to accept multiple CSV file
+uploaded_files = st.file_uploader("Please select a CSV file", accept_multiple_files=True)
+for file in uploaded_files:
+    df = pd.read_csv(file)
+    st.write("File uploaded:", file.name)
+    st.dataframe(df)
+#Uploading and Processing
+#upload single file
+from PIL import Image
+from PIL import ImageEnhance
+def load_image(image):
+    img = Image.open(image)
+    return img
+
+file = st.file_uploader("Please select image to upload and process")
+if file is not None:
+    image = Image.open(file) 
+    fig = plt.figure()
+    st.subheader("Original Image")
+    plt.imshow(image)
+    st.pyplot(fig)
+    fig = plt.figure()
+    contrast = ImageEnhance.Contrast(image).enhance(12)
+    plt.imshow(contrast)
+    st.subheader("Preprocessed Image")
+    st.pyplot(fig)
+
+    
+#Image classification
+import keras
+from PIL import Image, ImageOps
+import numpy as np
+
+
+
+import streamlit as st
+import streamlit as st
+from transformers import pipeline
+
+'''Hugging Face'''
+
+import streamlit as st
+from transformers import pipeline
+
+if __name__ == "__main__":
+
+    # Define the title of the and its description
+    st.title("Answering questions using NLP through Streamlit interface")
+    st.write("Pose questions, get answers")
+
+    # Load file
+    
+    raw_text = st.text_area(label="Enter a text here")
+    if raw_text != None and raw_text != '':
+
+        # Display text
+        with st.expander("Show question"):
+            st.write(raw_text)
+
+        # Conduct question answering using the pipeline
+        question_answerer = pipeline('question-answering')
+
+        answer = ''
+        question = st.text_input('Ask a question')
+
+        if question != '' and raw_text != '':
+            answer = question_answerer({
+                'question': question,
+                'context': raw_text
+            })
+
+        st.write(answer)
+
+        
+        
+
+
+
+
+
+
+
+    

hugginface.py

@@ -0,0 +1,33 @@
+'''Hugging Face'''
+
+import streamlit as st
+from transformers import pipeline
+
+if __name__ == "__main__":
+
+    # Define the title of the and its description
+    st.title("Answering questions using NLP through Streamlit interface")
+    st.write("Pose questions, get answers")
+
+    # Load file
+    
+    raw_text = st.text_area(label="Enter a text here")
+    if raw_text != None and raw_text != '':
+
+        # Display text
+        with st.expander("Show question"):
+            st.write(raw_text)
+
+        # Conduct question answering using the pipeline
+        question_answerer = pipeline('question-answering')
+
+        answer = ''
+        question = st.text_input('Ask a question')
+
+        if question != '' and raw_text != '':
+            answer = question_answerer({
+                'question': question,
+                'context': raw_text
+            })
+
+        st.write(answer)

students.csv

@@ -0,0 +1,5 @@
+Student Name,Marks
+Amelia Kami,82
+Antoinne Mark,76
+Peter Zen,96
+North Kim,68