Delete app.py

app.py

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-# -*- coding: utf-8 -*-
-"""PRML_project.ipynb
-
-Automatically generated by Colab.
-
-Original file is located at
-    https://colab.research.google.com/drive/1_9mr_G1Wt8bteyyMEFJYBImPcIteTcSQ
-
-## Downloading & preparing the Dataset
-"""
-
-import pandas as pd
-import matplotlib.pyplot as plt
-import warnings
-from sklearn.model_selection import train_test_split
-from sklearn.metrics import accuracy_score,classification_report, ConfusionMatrixDisplay
-import re
-import string
-from sklearn.linear_model import LogisticRegression
-from sklearn.naive_bayes import MultinomialNB
-from sklearn.tree import DecisionTreeClassifier
-from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
-from sklearn.feature_extraction.text import TfidfVectorizer
-from xgboost import XGBClassifier
-from lightgbm import LGBMClassifier
-from sklearn.svm import SVC
-# Ignore FutureWarning messages
-warnings.simplefilter(action='ignore', category=FutureWarning)
-
-import os
-import sys
-from tempfile import NamedTemporaryFile
-from urllib.request import urlopen
-from urllib.parse import unquote, urlparse
-from urllib.error import HTTPError
-from zipfile import ZipFile
-import tarfile
-import shutil
-
-CHUNK_SIZE = 40960
-DATA_SOURCE_MAPPING = 'sentiment-analysis-dataset:https%3A%2F%2Fstorage.googleapis.com%2Fkaggle-data-sets%2F989445%2F1808590%2Fbundle%2Farchive.zip%3FX-Goog-Algorithm%3DGOOG4-RSA-SHA256%26X-Goog-Credential%3Dgcp-kaggle-com%2540kaggle-161607.iam.gserviceaccount.com%252F20240418%252Fauto%252Fstorage%252Fgoog4_request%26X-Goog-Date%3D20240418T100202Z%26X-Goog-Expires%3D259200%26X-Goog-SignedHeaders%3Dhost%26X-Goog-Signature%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'
-
-KAGGLE_INPUT_PATH='/kaggle/input'
-KAGGLE_WORKING_PATH='/kaggle/working'
-KAGGLE_SYMLINK='kaggle'
-
-!umount /kaggle/input/ 2> /dev/null
-shutil.rmtree('/kaggle/input', ignore_errors=True)
-os.makedirs(KAGGLE_INPUT_PATH, 0o777, exist_ok=True)
-os.makedirs(KAGGLE_WORKING_PATH, 0o777, exist_ok=True)
-
-try:
-  os.symlink(KAGGLE_INPUT_PATH, os.path.join("..", 'input'), target_is_directory=True)
-except FileExistsError:
-  pass
-try:
-  os.symlink(KAGGLE_WORKING_PATH, os.path.join("..", 'working'), target_is_directory=True)
-except FileExistsError:
-  pass
-
-for data_source_mapping in DATA_SOURCE_MAPPING.split(','):
-    directory, download_url_encoded = data_source_mapping.split(':')
-    download_url = unquote(download_url_encoded)
-    filename = urlparse(download_url).path
-    destination_path = os.path.join(KAGGLE_INPUT_PATH, directory)
-    try:
-        with urlopen(download_url) as fileres, NamedTemporaryFile() as tfile:
-            total_length = fileres.headers['content-length']
-            print(f'Downloading {directory}, {total_length} bytes compressed')
-            dl = 0
-            data = fileres.read(CHUNK_SIZE)
-            while len(data) > 0:
-                dl += len(data)
-                tfile.write(data)
-                done = int(50 * dl / int(total_length))
-                sys.stdout.write(f"\r[{'=' * done}{' ' * (50-done)}] {dl} bytes downloaded")
-                sys.stdout.flush()
-                data = fileres.read(CHUNK_SIZE)
-            if filename.endswith('.zip'):
-              with ZipFile(tfile) as zfile:
-                zfile.extractall(destination_path)
-            else:
-              with tarfile.open(tfile.name) as tarfile:
-                tarfile.extractall(destination_path)
-            print(f'\nDownloaded and uncompressed: {directory}')
-    except HTTPError as e:
-        print(f'Failed to load (likely expired) {download_url} to path {destination_path}')
-        continue
-    except OSError as e:
-        print(f'Failed to load {download_url} to path {destination_path}')
-        continue
-
-print('Data source import complete.')
-
-import numpy as np # linear algebra
-import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
-
-# Input data files are available in the read-only "../input/" directory
-# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory
-
-import os
-for dirname, _, filenames in os.walk('/kaggle/input'):
-    for filename in filenames:
-        print(os.path.join(dirname, filename))
-
-d = pd.read_csv('/kaggle/input/sentiment-analysis-dataset/train.csv',encoding='latin1');
-f = pd.read_csv('/kaggle/input/sentiment-analysis-dataset/test.csv',encoding='latin1');
-df = pd.concat([d,f])
-
-print(df.shape)
-display(df.info())
-display(df)
-
-"""## Preprocessing the dataset"""
-
-df.dropna(inplace=True)
-
-df['sentiment'].value_counts(normalize=True).plot(kind='bar');
-
-df['sentiment'] = df['sentiment'].astype('category').cat.codes
-df['sentiment'].value_counts(normalize=True).plot(kind='bar');
-
-df['Time of Tweet'] = df['Time of Tweet'].astype('category').cat.codes
-# Convert Country column to categorical variable
-df['Country'] = df['Country'].astype('category').cat.codes
-# convert Age of User to integer
-df['Age of User']=df['Age of User'].replace({'0-20':18,'21-30':25,'31-45':38,'46-60':53,'60-70':65,'70-100':80})
-
-df.info()
-
-df.drop(columns=['textID','Time of Tweet', 'Age of User', 'Country', 'Population -2020', 'Land Area (Km²)', 'Density (P/Km²)'])
-
-def wp(text):
-    text = text.lower()
-    text = re.sub('\[.*?\]', '', text)
-    text = re.sub("\\W"," ",text)
-    text = re.sub('https?://\S+|www\.\S+', '', text)
-    text = re.sub('<.*?>+', '', text)
-    text = re.sub('[%s]' % re.escape(string.punctuation), '', text)
-    text = re.sub('\n', '', text)
-    text = re.sub('\w*\d\w*', '', text)
-    return text
-
-df['selected_text'] = df["selected_text"].apply(wp)
-
-"""## Training and testing split  """
-
-X=df['selected_text']
-y= df['sentiment']
-
-X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2,random_state=42)
-print(X_train.shape)
-print(X_test.shape)
-print(y_train.shape)
-print(y_test.shape)
-
-vectorization = TfidfVectorizer()
-XV_train = vectorization.fit_transform(X_train)
-XV_test = vectorization.transform(X_test)
-
-"""## Random forest and boosting methods
-
-### Random forest
-"""
-
-rf_classifier = RandomForestClassifier(n_estimators=100, random_state=42)
-rf_classifier.fit(XV_train, y_train)
-
-rf_pred = rf_classifier.predict(XV_test)
-
-print("Random Forest Accuracy:", accuracy_score(y_test, rf_pred))
-
-print("\nRandom Forest Classification Report:")
-print(classification_report(y_test, rf_pred))
-
-ConfusionMatrixDisplay.from_predictions(y_test, rf_pred);
-
-"""### Adaboost boosting method"""
-
-ada_classifier = AdaBoostClassifier()
-ada_classifier.fit(XV_train, y_train)
-
-ada_pred = ada_classifier.predict(XV_test)
-
-print("AdaBoost Accuracy:", accuracy_score(y_test, ada_pred))
-
-print("\nAdaBoost Classification Report:")
-print(classification_report(y_test, ada_pred))
-
-ConfusionMatrixDisplay.from_predictions(y_test, ada_pred);
-
-"""### Gradient Boosting"""
-
-from sklearn.ensemble import GradientBoostingClassifier
-# Gradient Boosting Machine (GBM)
-gbm_classifier = GradientBoostingClassifier()
-gbm_classifier.fit(XV_train, y_train)
-y_pred_gbm = gbm_classifier.predict(XV_test)
-accuracy_gbm = accuracy_score(y_test, y_pred_gbm)
-print("\nGradient Boosting Machine (GBM) Model:")
-print("Accuracy:", accuracy_gbm)
-report_gbm = classification_report(y_test, y_pred_gbm)
-print("Gradient Boosting Machine (GBM) Classification Report:")
-print(report_gbm)
-# If you want to display confusion matrix for GBM, you can use:
-ConfusionMatrixDisplay.from_predictions(y_test, y_pred_gbm)
-
-"""### LightGBM"""
-
-import lightgbm as lgb
-from sklearn.metrics import accuracy_score, classification_report, ConfusionMatrixDisplay
-
-# LightGBM
-lgb_classifier = lgb.LGBMClassifier()
-lgb_classifier.fit(XV_train, y_train)
-y_pred_lgb = lgb_classifier.predict(XV_test)
-accuracy_lgb = accuracy_score(y_test, y_pred_lgb)
-print("\nLightGBM Model:")
-print("Accuracy:", accuracy_lgb)
-report_lgb = classification_report(y_test, y_pred_lgb)
-print("LightGBM Classification Report:")
-print(report_lgb)
-# If you want to display confusion matrix for LightGBM, you can use:
-ConfusionMatrixDisplay.from_predictions(y_test, y_pred_lgb)
-
-"""## SVM(Support Vector Machine)
-
-### Kernel ---> 'Linear'
-"""
-
-svm_classifier = SVC(kernel='linear')
-svm_classifier.fit(XV_train, y_train)
-
-svm_pred = svm_classifier.predict(XV_test)
-
-svm_accuracy = accuracy_score(y_test, svm_pred)
-print(f"SVM with linear kernel Accuracy:", svm_accuracy)
-
-print("\nSVM ( Kernel='linear' ) Classification Report:")
-print(classification_report(y_test, svm_pred))
-
-ConfusionMatrixDisplay.from_predictions(y_test,svm_pred);
-
-"""### Kernel--->'Poly'"""
-
-svm_classifier = SVC(kernel='poly')
-svm_classifier.fit(XV_train, y_train)
-
-svm_pred = svm_classifier.predict(XV_test)
-
-svm_accuracy = accuracy_score(y_test, svm_pred)
-print(f"SVM with poly kernel Accuracy:", svm_accuracy)
-
-print("\nSVM ( Kernel='Poly' ) Classification Report:")
-print(classification_report(y_test, svm_pred))
-
-ConfusionMatrixDisplay.from_predictions(y_test,svm_pred);
-
-"""### Kernel--->'RBF'"""
-
-svm_classifier = SVC(kernel='rbf')
-svm_classifier.fit(XV_train, y_train)
-
-svm_pred = svm_classifier.predict(XV_test)
-
-svm_accuracy = accuracy_score(y_test, svm_pred)
-print(f"SVM with rbf kernel Accuracy:", svm_accuracy)
-
-print("\nSVM ( Kernel='RBF' ) Classification Report:")
-print(classification_report(y_test, svm_pred))
-
-ConfusionMatrixDisplay.from_predictions(y_test,svm_pred);
-
-"""# Decision Tree"""
-
-from sklearn.tree import DecisionTreeClassifier, plot_tree
-decision_tree=DecisionTreeClassifier(max_depth=20)
-
-decision_tree.fit(XV_train,y_train)
-
-dt_pred=decision_tree.predict(XV_test)
-
-dt_accuracy=accuracy_score(y_test,dt_pred)
-print(f"Decision Tree Accuracy with depth=20:", dt_accuracy)
-
-print("\nDecision Tree Classification Report:")
-print(classification_report(y_test, dt_pred))
-
-ConfusionMatrixDisplay.from_predictions(y_test,dt_pred);
-
-"""# Logistic Regression"""
-
-logistic_model = LogisticRegression(max_iter=100)
-
-logistic_model.fit(XV_train, y_train)
-
-y_pred_logistic = logistic_model.predict(XV_test)
-
-accuracy_logistic = accuracy_score(y_test, y_pred_logistic)
-print("Logistic Regression Model:")
-print(f"Accuracy: {accuracy_logistic}")
-
-report_logistic = classification_report(y_test, y_pred_logistic)
-print("Logistic Regression Classification Report:")
-print(report_logistic)
-
-ConfusionMatrixDisplay.from_predictions(y_test,y_pred_logistic);
-
-"""# Naive Bayes"""
-
-nb_classifier = MultinomialNB()
-
-nb_classifier.fit(XV_train, y_train)
-
-y_pred = nb_classifier.predict(XV_test)
-
-accuracy = accuracy_score(y_test, y_pred)
-print("Naive Bayes Model:")
-print("Accuracy:", accuracy)
-
-report_naive_bayes = classification_report(y_test, y_pred)
-print("Naive Bayes Classification Report:")
-print(report_naive_bayes)
-
-ConfusionMatrixDisplay.from_predictions(y_test,dt_pred);
-
-"""# K Nearest Neightbors (KNN)"""
-
-from sklearn.neighbors import KNeighborsClassifier
-from sklearn.metrics import accuracy_score, classification_report, ConfusionMatrixDisplay
-
-# K-Nearest Neighbors (KNN)
-knn_classifier = KNeighborsClassifier()
-knn_classifier.fit(XV_train, y_train)
-y_pred_knn = knn_classifier.predict(XV_test)
-accuracy_knn = accuracy_score(y_test, y_pred_knn)
-print("K-Nearest Neighbors (KNN) Model:")
-print("Accuracy:", accuracy_knn)
-report_knn = classification_report(y_test, y_pred_knn)
-print("K-Nearest Neighbors (KNN) Classification Report:")
-print(report_knn)
-# If you want to display confusion matrix for KNN, you can use:
-ConfusionMatrixDisplay.from_predictions(y_test, y_pred_knn)
-
-"""# Test"""
-
-def output_lable(n):
-    if n == 0:
-        return "The Text Sentement is Negative"
-    elif n == 1:
-        return "The Text Sentement is Neutral"
-    elif n == 2:
-        return "The Text Sentement is Positive"
-
-def manual_testing(news):
-    testing_news = {"text":[news]}
-    new_def_test = pd.DataFrame(testing_news)
-    new_def_test["text"] = new_def_test["text"].apply(wp)
-    new_x_test = new_def_test["text"]
-    new_xv_test = vectorization.transform(new_x_test)
-    pred_lr = logistic_model.predict(new_xv_test)
-    pred_svm = svm_classifier.predict(new_xv_test)
-
-    return print((output_lable(pred_lr[0])))
-
-text = input("Enter Text to Classify ")
-manual_testing(text)
-
-pip install gradio
-
-import gradio as gr
-import matplotlib.pyplot as plt
-import seaborn as sns
-
-# Function to classify sentiment
-def classify_sentiment(text):
-    # Preprocess the text
-    processed_text = wp(text)
-    # Vectorize the text
-    vectorized_text = vectorization.transform([processed_text])
-    # Predict sentiment using logistic regression model
-    prediction = logistic_model.predict(vectorized_text)[0]
-    # Output sentiment label
-    sentiment_label = output_label(prediction)
-    # Get probabilities for each sentiment class
-    probabilities = logistic_model.predict_proba(vectorized_text)[0]
-
-    # Plot probabilities
-    plt.figure(figsize=(8, 6))
-    sns.barplot(x=["Negative", "Neutral", "Positive"], y=probabilities)
-    plt.xlabel("Sentiment")
-    plt.ylabel("Probability")
-    plt.title("Sentiment Probability Distribution")
-    plt.ylim([0, 1])
-    plt.tight_layout()
-    plt.savefig("sentiment_probabilities.png")
-
-    return sentiment_label, "sentiment_probabilities.png"
-
-# Input and output components for the interface
-inputs = gr.Textbox(lines=10, label="Enter the text you want to analyze:")
-outputs = [
-    gr.Textbox(label="Sentiment Prediction"),
-    gr.Image(label="Sentiment Probability Distribution")
-]
-
-# Create the Gradio interface
-interface = gr.Interface(fn=classify_sentiment, inputs=inputs, outputs=outputs, title="Sentiment Classification", description="Enter a piece of text and analyze its sentiment.")
-interface.launch()
-