streamlit.py

# -*- coding: utf-8 -*-
"""streamlit.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1pFSEWubb_Y6C3_db1He2iZdjB1EhEhG9
"""

! pip install streamlit
!pip install streamlit_option_menu
!pip install pandas
!pip install numpy
!pip install pickle
!pip install matplotlib.pyplot
!pip install seaborn
!pip install pillow
!pip install easyocr
!pip install plotly.express
!pip install wordcloud
!pip install collections
!pip install nltk
!pip install scikit-learn scipy matplotlib

# Commented out IPython magic to ensure Python compatibility.
# %%writefile app.py
# # impoting library
# import streamlit as st
# from streamlit_option_menu import option_menu
# import pandas as pd
# import numpy as np
# import pickle
# import matplotlib.pyplot as plt
# import seaborn as sns
# # In this image processing
# from PIL import Image,ImageFilter,ImageEnhance
# from PIL import ImageOps
# import plotly.express as px
# import easyocr
# # In this Text processing
# from wordcloud import WordCloud
# from nltk.corpus import stopwords
# from nltk.tokenize import word_tokenize, sent_tokenize
# from nltk.sentiment import SentimentIntensityAnalyzer
# from collections import Counter
# # In this library using Recommendation
# import nltk
# from nltk.stem.snowball import SnowballStemmer
# from sklearn.feature_extraction.text import TfidfVectorizer
# from sklearn.metrics.pairwise import cosine_similarity
# 
# # streamlit webpage design
# def set_page_config():
#     st.set_page_config(
#         page_title="",
#         page_icon="https://uxwing.com/wp-content/themes/uxwing/download/business-professional-services/column-chart-line-icon.png",
#         layout="wide",
#         initial_sidebar_state="expanded",
#         menu_items={'About': """# This OCR app is created by *DINESH*!"""}
#     )
# 
# set_page_config()
# # This is Application background image._.
# def setting_bg():
#     st.markdown(
#         """
#         <style>
#             .stApp {
#                 background: url("https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQn3ayvZFQIM9arN8ll_szWh_4dW6-FU_iYuVcVZnYEl_F9ntEOwF9hHAUT8OfMRmkY-uE&usqp=CAU");
#                 background-size: 100% 100vh;
#                 background-repeat: no-repeat;
#             }
#         </style>
#         """,
#         unsafe_allow_html=True
#     )
# 
# setting_bg()
# #  hide the streamlit main and footer
# hide_default_format = """
#        <style>
#        #MainMenu {visibility: hidden; }
#        footer {visibility: hidden;}
#        </style>
#        """
# st.markdown(hide_default_format, unsafe_allow_html=True)
# 
# with st.sidebar:
#     # To create a selecting option menu..
#     selected = option_menu(None, ["HOME", "CUSTOMER BEHAVIOR PREDICTION", "IMAGE-PROCESSING", "TEXT-PROCESSING", "PRODUCT RECOMMENDATION-SYSTEM", "PROFILE"],
#                                         icons=["house", "pie-chart", "image", "file", "body-text", "person-circle"],
#                                         default_index=0,
#                                         styles={"nav-link": {"font-size": "20px", "text-align": "left", "margin": "0px",
#                                                             "--hover-color": "#6495ED"},
#                                                 "icon": {"font-size": "20px"},
#                                                 "container": {"max-width": "300px"},
#                                                 "nav-link-selected": {"background-color": "#93cbf2"}})
# 
#     text_process = st.expander("Text Processing", expanded=False)
# 
# if selected == "HOME":
#     st.markdown("<h1 style='text-align: center; color: #f72323;'>Customer Insights and Recommendation System</h1>", unsafe_allow_html=True)
#     st.markdown("""
#     <div style="text-align: justify; font-size: 30px; font-weight: bold;">
#         <p style="font-size: 25px; text-align: justify;">
#             <span style="color: black;">Classification Prediction:</span> In the classification prediction model, we aim to analyze customer behavior using the following algorithms: Decision Tree, Logistic Regression, and Random Forest.<br>
#             <span style="color: black;">Image Processing:</span> In this module, we process images using techniques such as EasyOCR (Optical Character Recognition) to extract text from images, and the Python Imaging Library (PIL) to identify and extract objects from images. Additionally, PIL can be used to modify images by changing formats, rotating, and manipulating pixel sizes.<br>
#             <span style="color: black;">Text Processing:</span> In this module, we provide sentiment analysis for text based on user input, utilizing text processing techniques such as NLTK (Natural Language Toolkit).<br>
#             <span style="color: black;">Product Recommendation System:</span> Build a recommendation system for product selection using NLTK techniques.
#         </p>
#     </div>
# """, unsafe_allow_html=True)
#     col1,col2 = st.columns([2,2])
#     with col1:
#         st.write('### :red[TECHNOLOGY USED]')
#         st.write('- PYTHON   (PANDAS, NUMPY)')
#         st.write('- SCIKIT-LEARN')
#         st.write('- DATA PREPROCESSING')
#         st.write('- EXPLORATORY DATA ANALYSIS')
#         st.write('- OCR')
#         st.write('- NLTK')
#         st.write('- WorldCould')
#         st.write('- STREAMLIT')
#     with col2:
#         st.write("### :red[MACHINE LEARNING MODEL]")
#         st.write('#### :red[CLASSIFICATION] - ***:red[RANDOMFOREST CLASSIFIER,LOGISTIC REGRESSION, DECISION TREE]***')
#         st.write('- The RandomForestClassifier is an ensemble learning method that combines multiple decision trees to create a robust and accurate classification model.')
#         st.write('- Logistic regression estimates the probability of an event occurring, such as voted or didnt vote, based on a given dataset of independent variables.')
#         st.write('- A decision tree is a non-parametric supervised learning algorithm, which is utilized for both classification and regression tasks.')
#   #--Classification Prediction --#
# if selected == "CUSTOMER BEHAVIOR PREDICTION":
#     st.markdown("<h1 style='text-align: center; color: #f72323;'>Classification Prediction</h1>", unsafe_allow_html=True)
#     selected = option_menu(None, ["Algorithms","Prediction"],
#                        icons=["clipboard-data","graph-down"],
#                        default_index=0,
#                        orientation="horizontal",
#                        styles={"nav-link": {"font-size": "35px", "text-align": "center", "margin": "0px",
#                                            "--hover-color": "#6495ED"},
#                                "icon": {"font-size": "35px"},
#                                "container": {"max-width": "6000px"},
#                                "nav-link-selected": {"background-color": "#93cbf2"}})
# 
#     text_process = st.expander("Text Processing", expanded=False)
#     if selected == "Algorithms":
#         df = pd.DataFrame({
#         "Algorithm Names":["Decision Tree","Logistic Regression ","Random Forest"],
#         "Accuracy":[94,78,96],
#         "Precision":[94,82,95],
#         "Recall":[93,60,95],
#         "F1_score":[93,69,95]
#     })
# 
#         st.table(df)
#     if selected == "Prediction":
#         with st.form(key='my_form'):
#             c1,c2 = st.columns(2)
#             with c1:
#                 st.subheader(":red[Min & Max given for reference, you can enter any value]")
#                 # st.write( f'<h5 style="color:red;">NOTE: Min & Max given for reference, you can enter any value</h5>', unsafe_allow_html=True )
#                 transactionRevenue = st.number_input("Enter transactionRevenue (Min:0.0 & Max:307221222.5)",value=None)
#                 num_interactions = st.number_input("Enter num_interactions (Min:20.0 & Max:25911.0)",value=None)
#                 count_hit = st.number_input("Enter count_hit (Min:2, Max:7085.0)",value=None)
#                 st.markdown("")
#                 historic_session_page = st.number_input("historic_session_page (Min:0.0, Max:5021.25)",value=None)
#                 for _ in range(2):
#                     st.markdown(" ")
#             with c2:
#                 time_on_site = st.number_input("time_on_site (Min:0.0, Max:26652.75)",value=None)
#                 avg_session_time = st.number_input("avg_session_time (Min:2.0, Max:1109.1536494755242)",value=None)
#                 avg_session_time_page = st.number_input("avg_session_time_page (Min:0.0, Max:339.53914141414145)",value=None)
#                 historic_session = st.number_input("historic_session (Min:2.0, Max:23271.5)",value=None)
#                 visits_per_day = st.number_input("visits_per_day (Min:0.9230769230769232, Max:304.8440708626405)",value=None)
#                 submit_button = st.form_submit_button(label="PREDICT STATUS")
#                 st.markdown("""
#                             <style>
#                             div.stButton > button:first-child {
#                                 background-color: #009999;
#                                 color: white;
#                                 width: 100%;
#                             }
#                             </style>
#                         """, unsafe_allow_html=True)
# 
#             for i in ["transactionRevenue","num_interactions","count_hit","historic_session_page","time_on_site","avg_session_time","avg_session_time_page","historic_session","visits_per_day"]:
#                 if submit_button :
#                     with open(r"/content/Model.pkl", 'rb') as file:
#                         loaded_model = pickle.load(file)
#                     with open(r"/content/Scaler.pkl", 'rb') as f:
#                         scaler_loaded = pickle.load(f)
#                     with open(r"/content/ct.pkl", 'rb') as f:
#                         t_loaded = pickle.load(f)
# 
#                 # Predict the has_converted for a new sample
#                     new_sample = np.array([[float(transactionRevenue),float(num_interactions),float(count_hit),float(historic_session_page),float(time_on_site),float(avg_session_time),float(avg_session_time_page),float(historic_session),float(visits_per_day)]])
#                     try:
#                         new_sample = np.array((new_sample[:, [0,1,2, 3, 4, 5, 6,7,8]]))
#                         new_sample = scaler_loaded.transform(new_sample)
#                         new_pred = loaded_model.predict(new_sample)
#                         if new_pred== 1:
#                             st.write('## :green[The Status is Converted] ')
#                             break
#                         else:
#                             st.write('## :red[The Status is Not Converted] ')
#                             break
#                     except ValueError as e:
#                         st.write(f"Error: {e}")
#                         st.write("Please make sure all input values are valid numbers.")
#                         break
#     uploaded_file=st.sidebar.file_uploader(label="Upload your csv or excel file.max(200mb)",type=["csv","xlsx"])
# 
#     if uploaded_file is not None:
#         print(uploaded_file)
# 
#         try:
#             df=pd.read_csv(uploaded_file)
#         except Exception as e:
#             print(e)
#             df=pd.read_excel(uploaded_file)
#     try:
#         st.write(df)
#         numeric_columns= list(df.select_dtypes(["float","int"]).columns)
#         categorical_column=list(df.select_dtypes("object").columns)
#     except Exception as e:
#         print(e)
#         st.write("Please upload file to the application.")
#     #add a select widget tot the  sidebar
#     chart_select=st.sidebar.selectbox(label="select the chart type",
#                                     options=["Scatterplots","Barcharts","Boxplot","Histogram"])
# 
#     if chart_select=="Scatterplots":
#         st.sidebar.subheader("Scatterplot settings")
#         try:
#             x_values= st.sidebar.selectbox("X axis",options=numeric_columns)
#             y_values= st.sidebar.selectbox("Y axis",options=numeric_columns)
#             plot=px.scatter(data_frame=df,x=x_values,y=y_values)
#             #display the chart
#             st.plotly_chart(plot)
#         except Exception as e:
#             print(e)
# 
# 
#     if chart_select=="Barcharts":
#         st.sidebar.subheader("Barcharts settings")
#         try:
#             x_values= st.sidebar.selectbox("X axis",categorical_column)
#             y_values= st.sidebar.selectbox("Y axis",options=numeric_columns)
#             plot=px.bar(data_frame=df,x=x_values,y=y_values)
#             #display the chart
#             st.plotly_chart(plot)
#         except Exception as e:
#             print(e)
# 
#     if chart_select=="Boxplot":
#         st.sidebar.subheader("Boxplot settings")
#         try:
#             x_values= st.sidebar.selectbox("X axis",categorical_column)
#             y_values= st.sidebar.selectbox("Y axis",options=numeric_columns)
#             plot=px.box(data_frame=df,x=x_values,y=y_values)
#             #display the chart
#             st.plotly_chart(plot)
#         except Exception as e:
#             print(e)
# 
#     if chart_select=="Histogram":
#         st.sidebar.subheader("Barcharts settings")
#         try:
#             y_values= st.sidebar.selectbox("Y axis",options=numeric_columns)
#             plot=px.histogram(data_frame=df,x=y_values,nbins=20)
#             #display the chart
#             st.plotly_chart(plot)
#         except Exception as e:
#             print(e)
# 
#   #-- Image Processing --#
# if selected == "IMAGE-PROCESSING":
#     st.markdown("<h1 style='text-align: center; color: #f72323;'>Image Processing</h1>", unsafe_allow_html=True)
#     uploaded_Image_file = st.file_uploader(label="Upload your jpg or jpeg file. Max size: 200mb", type=["jpg", "jpeg"])
#     if uploaded_Image_file is not None:
#         # Open the original image view
#         original_image = Image.open(uploaded_Image_file)
#         st.image(original_image, caption="Original Image")
#         resized_image = original_image.resize((1000, 500))
# 
#         # Perform OCR on the resized image
#         np_img = np.array(resized_image)
#         reader = easyocr.Reader(['en'])
#         ocr_result = reader.readtext(np_img)
#         # Display OCR results
#         ocr_text = [result[1] for result in ocr_result] if ocr_result else []
#         if ocr_text:
#             st.success("OCR Text: " + "\n".join(ocr_text))
#         else:
#             st.error("No text found : 🥺")
# 
#         try:
#             # Buttons for image processing arranged horizontally
#             col1, col2, col3, col4, col5, col6, col7 = st.columns(7)
#             with col1:
#                 if st.button("Convert to Grayscale"):
#                     gray_image = resized_image.convert("L")
#                     st.image(gray_image, caption="Grayscale Image")
#             with col2:
#                 if st.button("Apply Gaussian Blur"):
#                     blurred_image = resized_image.filter(ImageFilter.GaussianBlur(radius=10))
#                     st.image(blurred_image, caption="Blurred Image")
#             with col3:
#                 if st.button("Enhance Contrast"):
#                     blurred_image = resized_image.filter(ImageFilter.GaussianBlur(radius=10))
#                     contrast_enhanced_image = ImageEnhance.Contrast(blurred_image)
#                     st.image(contrast_enhanced_image.enhance(20), caption="Contrast Enhanced Image")
#             with col4:
#                 if st.button("Rotated Image"):
#                     st.image(resized_image.rotate(90))
#                     st.image(resized_image.rotate(180))
#                     st.image(resized_image.rotate(270))
#                     st.image(resized_image.rotate(360))
#             with col5:
#                 if st.button("Mirror Image"):
#                     st.image(ImageOps.mirror(resized_image), caption="Mirror Image")
#             with col6:
#                 if st.button("Brightened Image"):
#                     bright = ImageEnhance.Brightness(resized_image)
#                     bright_1 = bright.enhance(3)
#                     st.image(bright_1, caption="Brightened Image")
#             with col7:
#                 if st.button("Negative & Edge Detected Image"):
#                     neg_image = ImageOps.invert(resized_image)
#                     st.image(neg_image)
#                     Edge_det_image = resized_image.filter(ImageFilter.FIND_EDGES)
#                     st.image(Edge_det_image)
#                 if st.button("Sharpened & Framed Image"):
#                     sharped_image = ImageEnhance.Sharpness(resized_image)
#                     st.image(sharped_image.enhance(10))
#                     Framed_img = ImageOps.expand(resized_image, 10, "black")
#                     st.image(Framed_img)
#         except Exception as e:
#             st.error("Error: " + str(e))
# 
#   # --This is Text Processing --#
# if selected == "TEXT-PROCESSING":
#     st.markdown("<h1 style='text-align: center; color: #f72323;'>Text Processing</h1>", unsafe_allow_html=True)
#     # nltk.download('stopwords')
#     # nltk.download('punkt')
#     # nltk.download('vader_lexicon')
#     input_text = st.text_input("Enter the Text")
#     # NLP Pre-processing
#     if input_text:
#         # Tokenization
#         words = word_tokenize(input_text)
#         sentences = sent_tokenize(input_text)
# 
#         # Stopword removal
#         stop_words = set(stopwords.words('english'))
#         filtered_words = [word.lower() for word in words if word.isalnum() and word.lower() not in stop_words]
# 
#         # Display pre-processing results
#         st.header("NLP Pre-processing")
#         st.subheader("Tokenization")
#         st.write(words)
# 
#         st.subheader("Stopword Removal")
#         st.write(filtered_words)
# 
#         st.subheader("Sentence Tokenization")
#         st.write(sentences)
# 
#         # Keyword extraction
#         st.header("Keyword Extraction")
#         word_freq = Counter(filtered_words)
#         keywords = word_freq.most_common(5)  # Display top 5 keywords
#         st.write("Keywords:", [word[0] for word in keywords])
# 
#         # Sentiment Analysis
#         st.header("Sentiment Analysis")
#         sia = SentimentIntensityAnalyzer()
#         sentiment_score = sia.polarity_scores(input_text)
#         st.write("Sentiment Score:", sentiment_score)
# 
#         # Display overall sentiment
#         if sentiment_score['compound'] >= 0.05:
#             sentiment_label = "Positive"
#         elif sentiment_score['compound'] <= -0.05:
#             sentiment_label = "Negative"
#         else:
#             sentiment_label = "Neutral"
#         st.write("Sentiment:", sentiment_label)
# 
#         col1, col2 = st.columns([2, 2], gap="medium")
#         with col1:
#             WC = WordCloud(width=4000, height=3250).generate(input_text)
#             plt.figure(1, figsize=(10, 10))
#             plt.imshow(WC)
#             plt.axis('off')  # Turn off axis labels
#             st.set_option('deprecation.showPyplotGlobalUse', False)
#             st.pyplot()
#         with col2:
#             color_palette = {"neg": "red", "neu": "blue", "pos": "green", "compound": "purple"}
# 
#             # Create a DataFrame for visualization
#             data = {'sentiment_score': sentiment_score.values()}
#             df = pd.DataFrame(data, index=sentiment_score.keys())
#             df = df.reset_index().rename(columns={'index': 'sentiment'})  # Add sentiment labels as a column
# 
#             # Plot the bar chart with correct color palette
#             fig, ax = plt.subplots()
#             sns.barplot(x='sentiment', y='sentiment_score', data=df, palette=[color_palette[label] for label in df['sentiment']], ax=ax)
# 
#             ax.set_title('Sentiment Analysis BarPlot', fontsize=20)
#             st.pyplot(fig)
# 
#   # --Recommendation System-- #
# if selected == "PRODUCT RECOMMENDATION-SYSTEM":
#     st.markdown("<h1 style='text-align: center; color: #f72323;'>Product Recommendation System</h1>", unsafe_allow_html=True)
#     data = pd.read_csv("/content/recom_sys.csv")
#     # Remove unnecessary columns
#     data = data.drop('id', axis=1)
# 
#     # Define tokenizer and stemmer
#     stemmer = SnowballStemmer('english')
#     def tokenize_and_stem(text):
#         tokens = nltk.word_tokenize(text.lower())
#         stems = [stemmer.stem(t) for t in tokens]
#         return stems
# 
#     # Create stemmed tokens column
#     data['stemmed_tokens'] = data.apply(lambda row: tokenize_and_stem(row['Title'] + ' ' + row['Description']), axis=1)
# 
#     # Define TF-IDF vectorizer and cosine similarity function
#     tfidf_vectorizer = TfidfVectorizer(tokenizer=tokenize_and_stem)
#     def cosine_sim(text1, text2):
#         # tfidf_matrix = tfidf_vectorizer.fit_transform([text1, text2])
#         text1_concatenated = ' '.join(text1)
#         text2_concatenated = ' '.join(text2)
#         tfidf_matrix = tfidf_vectorizer.fit_transform([text1_concatenated, text2_concatenated])
#         return cosine_similarity(tfidf_matrix)[0][1]
# 
#     # Define search function
#     def search_products(query):
#         query_stemmed = tokenize_and_stem(query)
#         data['similarity'] = data['stemmed_tokens'].apply(lambda x: cosine_sim(query_stemmed, x))
#         results = data.sort_values(by=['similarity'], ascending=False).head(10)[['Title', 'Description', 'Category']]
#         return results
# 
#     # web app
#     st.title(':red[Search Engine and Product Recommendation System ON Am Data]')
#     query = st.text_input("Enter Product Name")
#     sumbit = st.button('Search')
#     if sumbit:
#         res = search_products(query)
#         st.write(res)
# 
# 
#   # --MY progile-- #
# if selected == "PROFILE":
#     st.subheader(":red[DATA SCIENCE FINAL PROJECT]",divider='rainbow')
#     st.subheader(":red[The objective of this project is to:]")
#     st.markdown("""
#                 <div style="text-align: justify; font-size: 30px;">
#                     <p style="font-size: 25px; text-align: justify;">
#                         The objective of this project is to perform a comprehensive analysis and implement various tasks, including Exploratory Data Analysis (EDA) on an e-commerce dataset, image processing, Natural Language Processing (NLP), and the development of a recommendation system. It is important to note that executing these steps will necessitate a sound understanding of the relevant tools and libraries.
#                     </p></div>""", unsafe_allow_html=True)
#     col1,col2 = st.columns([3,3],gap="medium")
#     with col1:
#         for _ in range(5):
#             st.write(" ")
#         # Create additional vertical space
#         for _ in range(3):
#             st.write(" ")
#         st.markdown("### :orange[Name:  ] :blue[Kaleeswari S]")
#         st.markdown("### :orange[GitHub] ⬇️")
#         github_url = "https://github.com/Kaleeswari-S/Final_Project"
#         button_color = "#781734"
#         # Create a button with a hyperlink
#         button_html = f'<a href="{github_url}" target="_blank"><button style="font-size: 16px; background-color: {button_color}; color: #fff; padding: 8px 16px; border: none; border-radius: 4px;">GitHub</button></a>'
#         st.markdown(button_html, unsafe_allow_html=True)
# 
#     with col2:
#         # Create vertical space using empty containers
#         for _ in range(5):
#             st.write(" ")
#         # Create additional vertical space
#         for _ in range(3):
#             st.write(" ")
#         st.markdown("### :orange[Email: kaleeswariramkumar25@gmail.com] ")
#         st.markdown("### :orange[LinkedIn] ⬇️")
#         linkedin_url = "https://www.linkedin.com/in/kaleeswari-s-081a392a6/"
#         button_color = "#781734"
#         button_html = f'<a href="{linkedin_url}" target="_blank"><button style="font-size: 16px; background-color: {button_color}; color: #fff; padding: 8px 16px; border: none; border-radius: 4px;">My LinkedIn profile</button></a>'
#         st.markdown(button_html, unsafe_allow_html=True)

!npm install localtunnel

!streamlit run app.py &>/content/logs.txt & npx localtunnel --port 8501 & curl ipv4.icanhazip.com