Update app.py

app.py

@@ -1,5 +1,36 @@
-import gradio as gr
 import pickle 
+import gradio as gr
+import numpy as np
+import pandas as pd
+
+from sklearn.model_selection import train_test_split
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.preprocessing import LabelEncoder
+from sklearn.pipeline import Pipeline
+from sklearn.naive_bayes import MultinomialNB
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import accuracy_score, classification_report
+
+import nltk
+from nltk.tokenize import word_tokenize
+from nltk.corpus import stopwords
+from nltk.stem import WordNetLemmatizer
+nltk.download('punkt')
+nltk.download('stopwords')
+nltk.download('wordnet')
+def preprocess_nltk(text):
+    lemmatizer = WordNetLemmatizer()
+    tokens = word_tokenize(text.lower())  # Tokenization
+    stop_words = set(stopwords.words("english"))
+    filtered_tokens = [lemmatizer.lemmatize(token) for token in tokens if token.isalnum() and token not in stop_words]
+    return " ".join(filtered_tokens)
+
+
+
+
+
+
+
 with open("sentiment_analysis_model.pkl", "rb") as file:
     pipe2 = pickle.load(file)
 def prediction(text):