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Fake_News_Detection | Fake News Detection in Python | Mock library

 by   nishitpatel01 Python Version: Current License: MIT

 by   nishitpatel01 Python Version: Current License: MIT

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kandi X-RAY | Fake_News_Detection Summary

Fake_News_Detection is a Python library typically used in Testing, Mock applications. Fake_News_Detection has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However Fake_News_Detection build file is not available. You can download it from GitHub.
Fake News Detection in Python
Support
Support
Quality
Quality
Security
Security
License
License
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kandi-support Support

  • Fake_News_Detection has a low active ecosystem.
  • It has 205 star(s) with 166 fork(s). There are 19 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 9 open issues and 4 have been closed. On average issues are closed in 28 days. There are 2 open pull requests and 0 closed requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of Fake_News_Detection is current.
Fake_News_Detection Support
Best in #Mock
Average in #Mock
Fake_News_Detection Support
Best in #Mock
Average in #Mock

quality kandi Quality

  • Fake_News_Detection has 0 bugs and 0 code smells.
Fake_News_Detection Quality
Best in #Mock
Average in #Mock
Fake_News_Detection Quality
Best in #Mock
Average in #Mock

securitySecurity

  • Fake_News_Detection has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • Fake_News_Detection code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.
Fake_News_Detection Security
Best in #Mock
Average in #Mock
Fake_News_Detection Security
Best in #Mock
Average in #Mock

license License

  • Fake_News_Detection is licensed under the MIT License. This license is Permissive.
  • Permissive licenses have the least restrictions, and you can use them in most projects.
Fake_News_Detection License
Best in #Mock
Average in #Mock
Fake_News_Detection License
Best in #Mock
Average in #Mock

buildReuse

  • Fake_News_Detection releases are not available. You will need to build from source code and install.
  • Fake_News_Detection has no build file. You will be need to create the build yourself to build the component from source.
  • Installation instructions, examples and code snippets are available.
  • It has 9898 lines of code, 24 functions and 11 files.
  • It has high code complexity. Code complexity directly impacts maintainability of the code.
Fake_News_Detection Reuse
Best in #Mock
Average in #Mock
Fake_News_Detection Reuse
Best in #Mock
Average in #Mock
Top functions reviewed by kandi - BETA

kandi has reviewed Fake_News_Detection and discovered the below as its top functions. This is intended to give you an instant insight into Fake_News_Detection implemented functionality, and help decide if they suit your requirements.

  • Compute the most informative features
    • Detects fake news
    • Predict a message
    • Compute the mean of words
  • Plot a learning curve
    • Fit the model
  • Create bigrams
    • Create unigram from words
  • Stem a list of words
    • Stem a list of tokens
  • Builds the confusion matrix
    • Plots the recall curve
      • Return the prediction
        • Function to detect fake news
          • Creates a distribution plot

            Get all kandi verified functions for this library.

            Get all kandi verified functions for this library.

            Fake_News_Detection Key Features

            Fake News Detection in Python

            Community Discussions

            Trending Discussions on Fake_News_Detection
            • Flask webapp : 'Token' object has no attribute 'test' | render_template error
            Trending Discussions on Fake_News_Detection

            QUESTION

            Flask webapp : 'Token' object has no attribute 'test' | render_template error

            Asked 2021-May-23 at 19:14

            Code in Fake_News_Det.py :

            from flask import Flask, render_template, request
            from sklearn.feature_extraction.text import TfidfVectorizer
            from sklearn.linear_model import PassiveAggressiveClassifier
            import pickle
            import pandas as pd
            from sklearn.model_selection import train_test_split
            
            app = Flask(__name__)
            tfvect = TfidfVectorizer(stop_words='english', max_df=0.7)
            loaded_model = pickle.load(open('D:\Fake_News_Detection\model.pkl', 'rb'))
            dataframe = pd.read_csv('D:\Fake_News_Detection\data.csv')
            x = dataframe['text']
            y = dataframe['label']
            x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=0)
            
            def fake_news_det(news):
                tfid_x_train = tfvect.fit_transform(x_train)
                tfid_x_test = tfvect.transform(x_test)
                input_data = [news]
                vectorized_input_data = tfvect.transform(input_data)
                prediction = loaded_model.predict(vectorized_input_data)
                return prediction
            
            @app.route('/')
            def home():
                return render_template('index.html')
            
            @app.route('/predict', methods=['POST'])
            def predict():
                if request.method == 'POST':
                    message = request.form['message']
                    pred = fake_news_det(message)
                    print(pred)
                    return render_template('index.html', prediction=pred)
                else:
                    return render_template('index.html', prediction="Something went wrong")
            
            if __name__ == '__main__':
                app.run(debug=True)
            

            After running the code, and viewing it on local port it shows an error :-

            AttributeError AttributeError: 'Token' object has no attribute 'test'

            It have list of errors in sequence of type jinja errors, but what I think is

            File "D:\Fake_News_Detection\Fake_News_Det.py", line 31, in home
            return render_template('index.html')
            

            This has something to do with, although I have index.html file in templates folder

            My file path : Pl go through these image files https://imgur.com/a/kxQCqz0

            EDIT: index.html code :

            <html lang="en">
            <head>
              <meta charset="UTF-8">
              <title>Fake News📰 Detection System</title>
              <link href='https://fonts.googleapis.com/css?family=Pacifico' rel='stylesheet' type='text/css'>
            <link href='https://fonts.googleapis.com/css?family=Arimo' rel='stylesheet' type='text/css'>
            <link href='https://fonts.googleapis.com/css?family=Hind:300' rel='stylesheet' type='text/css'>
            <link href='https://fonts.googleapis.com/css?family=Open+Sans+Condensed:300' rel='stylesheet' type='text/css'>
            <link rel="stylesheet" href="{{ url_for('static', filename='style.css') }}">
            
            </head>
            
            <body>
             <div class="login">
                <h1>Fake News📰  Detector</h1>
            
                <form action="{{ url_for('predict')}}" method="POST">
                    <textarea  name="message" rows="6" cols="50" required="required" style="font-size: 18pt"></textarea>
                    <br>
                    <button type="submit" class="btn btn-primary btn-block btn-large">Predict</button>
            
                        <div class="results">
            
                {% if prediction == ['FAKE']%}
                <h2 style="color:red;">Looking Spam⚠️News📰 </h2>
                {% elif prediction == ['REAL']%}
                            <h2 style="color:green;"><b>Looking Real News📰</b></h2>
                {% endif %}
            
                </div>
            
                </form>
            
            </div>
            

            style.css code :

            @import url(https://fonts.googleapis.com/css?family=Open+Sans);
            .btn { display: inline-block; *display: inline; *zoom: 1; padding: 4px 10px 4px; margin-bottom: 0; font-size: 13px; line-height: 18px; color: #333333; text-align: center;text-shadow: 0 1px 1px rgba(255, 255, 255, 0.75); vertical-align: middle; background-color: #f5f5f5; background-image: -moz-linear-gradient(top, #ffffff, #e6e6e6); background-image: -ms-linear-gradient(top, #ffffff, #e6e6e6); background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#ffffff), to(#e6e6e6)); background-image: -webkit-linear-gradient(top, #ffffff, #e6e6e6); background-image: -o-linear-gradient(top, #ffffff, #e6e6e6); background-image: linear-gradient(top, #ffffff, #e6e6e6); background-repeat: repeat-x; filter: progid:dximagetransform.microsoft.gradient(startColorstr=#ffffff, endColorstr=#e6e6e6, GradientType=0); border-color: #e6e6e6 #e6e6e6 #e6e6e6; border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25); border: 1px solid #e6e6e6; -webkit-border-radius: 4px; -moz-border-radius: 4px; border-radius: 4px; -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.2), 0 1px 2px rgba(0, 0, 0, 0.05); -moz-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.2), 0 1px 2px rgba(0, 0, 0, 0.05); box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.2), 0 1px 2px rgba(0, 0, 0, 0.05); cursor: pointer; *margin-left: .3em; }
            .btn:hover, .btn:active, .btn.active, .btn.disabled, .btn[disabled] { background-color: #e6e6e6; }
            .btn-large { padding: 9px 14px; font-size: 15px; line-height: normal; -webkit-border-radius: 5px; -moz-border-radius: 5px; border-radius: 5px; }
            .btn:hover { color: #333333; text-decoration: none; background-color: #e6e6e6; background-position: 0 -15px; -webkit-transition: background-position 0.1s linear; -moz-transition: background-position 0.1s linear; -ms-transition: background-position 0.1s linear; -o-transition: background-position 0.1s linear; transition: background-position 0.1s linear; }
            .btn-primary, .btn-primary:hover { text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25); color: #ffffff; }
            .btn-primary.active { color: rgba(255, 255, 255, 0.75); }
            .btn-primary { background-color: #4a77d4; background-image: -moz-linear-gradient(top, #6eb6de, #4a77d4); background-image: -ms-linear-gradient(top, #6eb6de, #4a77d4); background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#6eb6de), to(#4a77d4)); background-image: -webkit-linear-gradient(top, #6eb6de, #4a77d4); background-image: -o-linear-gradient(top, #6eb6de, #4a77d4); background-image: linear-gradient(top, #6eb6de, #4a77d4); background-repeat: repeat-x; filter: progid:dximagetransform.microsoft.gradient(startColorstr=#6eb6de, endColorstr=#4a77d4, GradientType=0);  border: 1px solid #3762bc; text-shadow: 1px 1px 1px rgba(0,0,0,0.4); box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.2), 0 1px 2px rgba(0, 0, 0, 0.5); }
            .btn-primary:hover, .btn-primary:active, .btn-primary.active, .btn-primary.disabled, .btn-primary[disabled] { filter: none; background-color: #4a77d4; }
            .btn-block { width: 100%; display:block; }
            
            * { -webkit-box-sizing:border-box; -moz-box-sizing:border-box; -ms-box-sizing:border-box; -o-box-sizing:border-box; box-sizing:border-box; }
            
            html { width: 100%; height:100%; overflow:hidden; }
            
            body {
                width: 100%;
                height:100%;
                font-family: 'Open Sans', sans-serif;
                background: #092756;
                color: #fff;
                font-size: 18px;
                text-align:center;
                letter-spacing:1.2px;
                background: -moz-radial-gradient(0% 100%, ellipse cover, rgba(104,128,138,.4) 10%,rgba(138,114,76,0) 40%),-moz-linear-gradient(top,  rgba(57,173,219,.25) 0%, rgba(42,60,87,.4) 100%), -moz-linear-gradient(-45deg,  #670d10 0%, #092756 100%);
                background: -webkit-radial-gradient(0% 100%, ellipse cover, rgba(104,128,138,.4) 10%,rgba(138,114,76,0) 40%), -webkit-linear-gradient(top,  rgba(57,173,219,.25) 0%,rgba(42,60,87,.4) 100%), -webkit-linear-gradient(-45deg,  #670d10 0%,#092756 100%);
                background: -o-radial-gradient(0% 100%, ellipse cover, rgba(104,128,138,.4) 10%,rgba(138,114,76,0) 40%), -o-linear-gradient(top,  rgba(57,173,219,.25) 0%,rgba(42,60,87,.4) 100%), -o-linear-gradient(-45deg,  #670d10 0%,#092756 100%);
                background: -ms-radial-gradient(0% 100%, ellipse cover, rgba(104,128,138,.4) 10%,rgba(138,114,76,0) 40%), -ms-linear-gradient(top,  rgba(57,173,219,.25) 0%,rgba(42,60,87,.4) 100%), -ms-linear-gradient(-45deg,  #670d10 0%,#092756 100%);
                background: -webkit-radial-gradient(0% 100%, ellipse cover, rgba(104,128,138,.4) 10%,rgba(138,114,76,0) 40%), linear-gradient(to bottom,  rgba(57,173,219,.25) 0%,rgba(42,60,87,.4) 100%), linear-gradient(135deg,  #670d10 0%,#092756 100%);
                filter: progid:DXImageTransform.Microsoft.gradient( startColorstr='#3E1D6D', endColorstr='#092756',GradientType=1 );
            
            }
            .login {
                position: absolute;
                top: 40%;
                left: 50%;
                margin: -150px 0 0 -150px;
                width:400px;
                height:400px;
            }
            
            .login h1 { color: #fff; text-shadow: 0 0 10px rgba(0,0,0,0.3); letter-spacing:1px; text-align:center; }
            
            textarea {
                width: 100%;
                margin-bottom: 10px;
                background: rgba(0,0,0,0.3);
                border: none;
                outline: none;
                padding: 10px;
                font-size: 25px;
                color: #fff;
                text-shadow: 1px 1px 1px rgba(0,0,0,0.3);
                border: 1px solid rgba(0,0,0,0.3);
                border-radius: 4px;
                box-shadow: inset 0 -5px 45px rgba(100,100,100,0.2), 0 1px 1px rgba(255,255,255,0.2);
                -webkit-transition: box-shadow .5s ease;
                -moz-transition: box-shadow .5s ease;
                -o-transition: box-shadow .5s ease;
                -ms-transition: box-shadow .5s ease;
                transition: box-shadow .5s ease;
            }
            input:focus { box-shadow: inset 0 -5px 45px rgba(100,100,100,0.4), 0 1px 1px rgba(255,255,255,0.2); }
            

            ANSWER

            Answered 2021-May-23 at 19:14

            there try to change the python version. sometimes downgrading and upgrading helps.

            It will best work on 2.7.8+

            Source https://stackoverflow.com/questions/67653286

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

            Vulnerabilities

            No vulnerabilities reported

            Install Fake_News_Detection

            These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

            Support

            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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