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Author: krishnaik06

14-Streamlit/app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+
+## Title of the aplication
+st.title("Hello Streamlit")
+
+## Diplay a Simple Text
+st.write("This is a imple text")
+
+##create a simple Dataframe
+
+df = pd.DataFrame({
+    'first column': [1, 2, 3, 4],
+    'second column': [10, 20, 30, 40]
+})
+
+
+## Display the Dataframe
+st.write("Here is the dataframe")
+st.write(df)
+
+
+##create a line chart
+
+chart_data=pd.DataFrame(
+    np.random.randn(20,3),columns=['a','b','c']
+)
+st.line_chart(chart_data)

14-Streamlit/classification.py

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+import streamlit as st
+import pandas as pd
+from sklearn.datasets import load_iris
+from sklearn.ensemble import RandomForestClassifier
+
+@st.cache_data
+def load_data():
+    iris = load_iris()
+    df = pd.DataFrame(iris.data, columns=iris.feature_names)
+    df['species'] = iris.target
+    return df, iris.target_names
+
+df,target_names=load_data()
+
+model=RandomForestClassifier()
+model.fit(df.iloc[:,:-1],df['species'])
+
+st.sidebar.title("Input Features")
+sepal_length = st.sidebar.slider("Sepal length", float(df['sepal length (cm)'].min()), float(df['sepal length (cm)'].max()))
+sepal_width = st.sidebar.slider("Sepal width", float(df['sepal width (cm)'].min()), float(df['sepal width (cm)'].max()))
+petal_length = st.sidebar.slider("Petal length", float(df['petal length (cm)'].min()), float(df['petal length (cm)'].max()))
+petal_width = st.sidebar.slider("Petal width", float(df['petal width (cm)'].min()), float(df['petal width (cm)'].max()))
+
+input_data = [[sepal_length, sepal_width, petal_length, petal_width]]
+
+## PRediction
+prediction = model.predict(input_data)
+predicted_species = target_names[prediction[0]]
+
+st.write("Prediction")
+st.write(f"The predicted species is: {predicted_species}")
+

14-Streamlit/sampledata.csv

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+,Name,Age,City
+0,John,28,New York
+1,Jane,24,Los Angeles
+2,Jake,35,Chicago
+3,Jill,40,Houston

14-Streamlit/streamlit.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Introduction to Streamlit\n",
+    "Streamlit is an open-source app framework for Machine Learning and Data Science projects. It allows you to create beautiful web applications for your machine learning and data science projects with simple Python scripts."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

14-Streamlit/widgets.py

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+import streamlit as st
+import pandas as pd
+
+st.title("Streamlit Text Input")
+
+name=st.text_input("Enter your name:")
+
+
+age=st.slider("Select your age:",0,100,25)
+
+st.write(f"Your age is {age}.")
+
+options = ["Python", "Java", "C++", "JavaScript"]
+choice = st.selectbox("Choose your favorite language:", options)
+st.write(f"You selected {choice}.")
+
+if name:
+    st.write(f"Hello, {name}")
+
+
+data = {
+    "Name": ["John", "Jane", "Jake", "Jill"],
+    "Age": [28, 24, 35, 40],
+    "City": ["New York", "Los Angeles", "Chicago", "Houston"]
+}
+
+df = pd.DataFrame(data)
+df.to_csv("sampledata.csv")
+st.write(df)
+
+
+uploaded_file=st.file_uploader("Choose a CSV file",type="csv")
+
+if uploaded_file is not None:
+    df=pd.read_csv(uploaded_file)
+    st.write(df)
+