Create app.py

app.py

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+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+import torch.optim as optim
+import pandas as pd
+import numpy as np
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+from sklearn.model_selection import train_test_split
+from sklearn.model_selection import KFold
+from sklearn.model_selection import GridSearchCV
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.naive_bayes import MultinomialNB
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import accuracy_score, classification_report
+from sklearn.metrics import roc_curve, auc, confusion_matrix
+from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
+from sklearn.pipeline import make_pipeline
+from sklearn.pipeline import Pipeline
+import joblib
+
+import re
+import string
+import nltk
+nltk.download('stopwords')
+nltk.download('punkt')
+
+import os
+import streamlit as st 
+
+import functions
+
+torch.manual_seed(1)
+
+# Preprocess function
+import re
+from contractions import contractions_dict
+from nltk.corpus import stopwords
+from nltk.tokenize import word_tokenize
+
+def preprocess_text(text):
+    # Remove URLs
+    url_pattern = re.compile(r'https?://\S+')
+    text = url_pattern.sub(' ', text)
+
+    # Remove HTML Tags
+    html_pattern = re.compile(r'<[^<>]+>')
+    text = html_pattern.sub(' ', text)
+
+    # Expand contractions
+    text = ' '.join([contractions_dict.get(word, word) for word in text.split()])
+
+    # Remove punctuation and digits
+    text = re.sub(r'[^\w\s]', ' ', text)
+
+    # Remove emojis
+    emoji_pattern = re.compile("["
+        u"\U0001F600-\U0001F64F"
+        u"\U0001F300-\U0001F5FF"
+        u"\U0001F680-\U0001F6FF"
+        u"\U0001F1E0-\U0001F1FF"
+        u"\U0001F1F2-\U0001F1F4"
+        u"\U0001F1E6-\U0001F1FF"
+        u"\U0001F600-\U0001F64F"
+        u"\U00002702-\U000027B0"
+        u"\U000024C2-\U0001F251"
+        u"\U0001f926-\U0001f937"
+        u"\U0001F1F2"
+        u"\U0001F1F4"
+        u"\U0001F620"
+        u"\u200d"
+        u"\u2640-\u2642"
+        "]+", flags=re.UNICODE)
+    text = emoji_pattern.sub(' ', text)
+
+    # Convert to lowercase
+    text = text.lower()
+
+    # Tokenize and remove stopwords
+    stop_words = set(stopwords.words('english'))
+    tokens = word_tokenize(text)
+    tokens = [token for token in tokens if token not in stop_words]
+
+    # Join tokens back into text
+    text = ' '.join(tokens)
+
+    return text
+
+# Main function
+model_NB_path = './model_NB.sav'
+model_NB = joblib.load(model_NB_path)
+
+model_LR_path = './model_LR.sav'
+model_LR = joblib.load(model_LR_path)
+
+
+text = st.text_area('Enter some text !!! (English text : D )')
+if text:
+    out = functions.sentiment_analysis_LR(text)
+    if out == 0:
+        out = 'negative'
+        st.json(out)
+    else:
+        out = 'positive'
+        st.json(out)